Most DAST tools were designed for a world where release cycles were measured in months and penetration testing happened once a year. That model made sense when development moved slowly enough for episodic security reviews to provide meaningful coverage.  

Then AI accelerated everything, with AI coding assistants compressing weeks of work into hours. 

The gap between how fast applications are being built and how quickly they can be validated is exactly where risk lives. Runtime validation has moved from a nice-to-have to a foundational part of any serious application security program.  

The question is no longer whether to implement DAST. It is whether your DAST can keep pace with how fast your teams are building. 

Checkmarx has been investing and adapting in runtime security since 2023, well before AI-driven development made it a market-wide priority. So, when AI fundamentally changed the pace of software development, we didn’t need to retrofit our approach – because we were already building for this moment. 

The result is the next generation of Checkmarx DAST: runtime security designed to move at AI speed. 

Why Traditional DAST Can’t Keep Pace 

Legacy DAST often depends on heavy infrastructure setup. Scanning internal applications can require firewall changes, VPN access, security exceptions, or container deployments. These dependencies introduce approval cycles and coordination overhead that simply don’t align with applications being built in days or hours.  That model may work for annual testing, but it breaks down completely when security needs to run continuously in your CI/CD pipeline. 

Configuration adds another layer of friction. Authentication scripting, scan tuning, and policy setup frequently require specialized expertise. When onboarding takes longer than development itself, coverage gaps become inevitable. 

Even when scanning runs successfully, context is often fragmented. If SAST and DAST operate in separate systems, teams must manually reconcile findings, deduplicate issues, and correlate risk. That overhead slows remediation and reduces the practical value of runtime testing. 

In short, traditional DAST wasn’t built for continuous, developer-driven workflows. It was built for episodic pen testing. And in the AI era, this security creates exposure. 

Runtime Validation Is Now Foundational 

Runtime testing has become a core component of modern application security programs. 

In fact, according to the Future of AppSec report, DAST adoption increased 24% year over year, with 47% of organizations now deploying DAST – up from 38% the previous year. The reason is clear: static analysis alone isn’t enough to secure dynamic, API-driven, AI-assisted applications. 

Many vulnerabilities, such as business logic flaws, authentication weaknesses, and configuration errors only emerge when applications are running. So, validating behavior in live environments is no longer optional; it’s essential. 

The conversation has shifted from whether to implement DAST to how to implement it effectively without slowing development. 

Why Runtime Validation Matters in the AI Era 

AI-generated code increases productivity, but it also introduces new risks. Large language models (LLMs) generate functional code, yet they lack full business context and architectural awareness. At higher velocity, human review becomes more constrained. 

SAST remains critical for identifying vulnerabilities in source code before deployment. But it does not verify how an application behaves once it is running, especially in environments with complex authentication, APIs, client-side logic, and layered infrastructure. 

DAST provides that validation. 

By simulating real-world attacker behavior against live applications, it identifies issues that only appear under real operating conditions. 

Static analysis shows you what the code is. Runtime validation and DAST show you how it behaves. Modern application security requires both. 

How Does Checkmarx DAST Solve This? 

Complete AppSec in One Platform

Checkmarx DAST is built natively within Checkmarx One, delivering unified SAST and DAST findings in a single platform. DAST vulnerabilities are incorporated into a unified risk scoring, enabling faster triage and eliminating duplicate effort. 

It is true platform integration with shared context from code to runtime.  

Live API scanning further strengthens coverage. REST, SOAP, and gRPC endpoints are tested dynamically, and APIs discovered by both SAST and DAST are consolidated into one unified inventory. 

Production-Ready in Minutes

Traditional DAST adoption has been slowed by infrastructure and configuration barriers.  

Checkmarx DAST removes them. 

Teams can begin scanning immediately without complex network reconfiguration or custom authentication scripting through: 

• Pre-configured tunneling for secure internal application scanning
• Advanced authentication support with guided setup and MFA validation
• Pre-built templates that simplify configuration 
• Direct CI/CD integration for continuous testing

What once required weeks to set up now can be done in minutes. 

Designed for Developer Workflows 

With legacy tools, teams file networking tickets, wait for authentication scripts, and manually reconcile findings before deployment. 

With Checkmarx DAST, scanning is configured quickly, authentication is validated through guided workflows, and SAST and DAST findings appear together with correlated risk scoring. Developers receive actionable feedback directly within their pipeline and deploy confidently without introducing bottlenecks. 

Security moves with development, not against it. 

Runtime Validation You Can Trust 

Checkmarx DAST validates live applications and uncovers vulnerabilities that only emerge at runtime. Because it operates within a unified platform, findings are correlated with SAST results to reduce false positives and improve prioritization. 

The result is accurate, actionable runtime security without added friction. 

Here’s What Makes Checkmarx DAST Different

Checkmarx DAST stands apart because it is: 

• Integrated seamlessly within Checkmarx One, not acquired technology stitched together  
• Infrastructure-light, eliminating complex agent and network requirements
• Comprehensive in scope, covering full web applications and APIs
• Enterprise-grade, while remaining accessible to development teams

It is built on the proven ZAP foundation with commercial-grade enhancements. The Checkmarx-ZAP collaboration enables open-source innovation alongside enterprise reliability and scalability.  

In fact, ZAP project leaders Simon Bennetts, Rick Mitchell, and Ricardo Pereira joined Checkmarx to help build the next generation of our enterprise-grade DAST offering, while continuing to invest in the open-source ZAP project and grow its global community. 

Getting Started 

Existing Checkmarx customers: Professional and Enterprise plans include DAST. Essentials customers can add DAST to their existing subscription.  

New customers: See the unified Checkmarx One platform in action and discover how DAST integrates seamlessly with SAST for complete code-to-runtime security. 

You can also tune into our DAST webinar to see it in action here. 

What’s Next 

The shift is already underway. According to the Future of AppSec report, DAST adoption grew 24% year over year – not because security teams suddenly discovered runtime testing, but because the old model of annual pen tests and periodic scans no longer provide meaningful coverage. Teams building with AI-generated codeneed security that moves on the same timeline. 

Checkmarx DAST is built for that reality: unified with SAST on a single platform, deployable in minutes, and designed to work within developer workflows rather than around them. 

If you are an existing Checkmarx customer, DAST is already included in Professional and Enterprise plans. Essentials customers can add it to their current subscription and new customers can see it in action at our upcoming webinar. 

This gap exposed that many organizations are still relying heavily on AppSec tools that predate AI-generated code. And they’re quickly discovering, sometimes painfully, that these tools are struggling to make sense of (let alone protect) code created by AI. 

This convergence is driving an unexpected shift in application security: DAST is experiencing a renaissance. 

For years, DAST (Dynamic Application Security Testing) was dismissed as a “nice to have,” useful primarily for checking compliance boxes or just viewed as a pen testing tool. But as AI accelerates code creation and introduces new behaviors and attack surfaces, organizations are rediscovering that DAST is actually a critical pillar of AppSec. Only DAST can provide the broad deployment and meaningful security coverage needed in this new reality; coverage that static tools simply can’t deliver in an AI-driven world. 

This was the central theme of our recent webinar, The Future of DAST: Why AI-Generated Code Demands a New Strategy, hosted by Checkmarx product leaders. Grounded in data from our annual Future of AppSec Report, the discussion explored some pressing questions: In a world where AI is reshaping how applications are built, what’s working, what’s broken, and why is DAST suddenly rising from the dead to become a crucial safeguard? 

Meet the Expert Panel 

To explore these questions, we brought together three Checkmarx leaders uniquely positioned at the intersection of DAST innovation and AI-driven development: 

Simon Bennetts, ZAP Software Engineering Expert, Checkmarx and ZAP project leader and founder. 

Frank Emery, Director of Product Management, Checkmarx 

Moderated by Avi Hein, Senior Product Marketing Manager at Checkmarx, the conversation offered a candid look at the future of application security, and why DAST has become essential in the age of AI. 

The Hidden Reality of AI-Generated Code 

The webinar opened with a simple poll: What percentage of your organization’s application code is AI-generated? The result was revealing, nearly half of respondents answered, “We don’t know.” 

This sentiment aligns with findings from our Future of AppSec Report, which showed that while organizations recognize the risks of AI-generated code, they deploy it anyway. At the same time, however, the report revealed something surprising: DAST adoption is rising sharply. 

“47% said they have DAST in place for 2025, up from 38% last year,” Avi noted. “That’s nearly a 24% increase year over year.” 

This growth signals a critical shift: organizations are increasingly recognizing that AI will be present in their code, but they’re also admitting that their traditional security approaches aren’t keeping up. The result? They’re returning to runtime testing engines like DAST to close the gap. 

The DAST Renaissance 

For the past decade, DAST lived in the margins of AppSec programs. It wasn’t ignored entirely, but it wasn’t central either. Many organizations ran it infrequently, before a major release or to satisfy a compliance requirement. 

Simon described this evolution: “DAST started strong…But then as applications changed, DAST found it harder to explore these applications. Even authentication got really hard.” 

As modern frameworks and authentication flows grew more complex, DAST struggled to keep up. Meanwhile, SAST surged in popularity because it was so simple to use. As Simon put it, “SAST was much easier to set up. You point it at your repo, and it can just go from there.”  Suddenly, organizations were treating it as a choice: DAST or SAST. 

But the truth is that no single testing method provides complete coverage. 

Simon emphasized: “I’ve never bought into the DAST or SAST thing. It’s much more important to combine these [two engines]. There is no one view of security.” 

In the AI era, DAST’s unique strength in being to see what actually happens when an application runs matters more than ever. DAST reveals what’s “genuinely vulnerable, delivering fewer false positives and a better signal-to-noise ratio than static analysis alone.” 

The AI Twist: Code That Looks Secure 

One of the most compelling insights that emerged from the discussion was about AI-generated code. Many developers assume that if AI writes the code, it must be secure.  

Frank explained why that assumption is dangerous: “People have this impression that AI-generated code is secure because the AI knows better. But what we’re finding is AI writes code that looks very secure but still has a lot of gaps.” 

And DAST plays a critical role in catching these hidden flaws. 

Frank put it bluntly: “DAST is acting as the police officer, confirming that all of the code that’s being written – especially by AI – is actually being written correctly.” With decades of development and maturity behind it, DAST can catch vulnerabilities that other tools miss. 

This is why organizations relying heavily on GitHub Copilot, ChatGPT, and other generative tools are increasingly turning to DAST for protection. 

DAST Adoption Lagged, But It’s Accelerating Now 

Although DAST has always been powerful, its adoption has historically been slow.  

Simon summarized the challenge: “DAST… is not as simple as SAST. You need a running system. You need to be able to authenticate. You need to be able to explore the application…Knowing how to tune [DAST] best for your applications is hard.” 

Frank agreed and added: “You start to see onboarding and adoption issues when you create a bottleneck around how DAST is used. Historically, you have experts…in charge of getting DAST up and running and that fundamentally restricted how much it could be adopted.” 

This complexity meant many organizations limited DAST usage to a handful of specialists, and you had to pick and choose what to test and how to test it. But modern DAST tools are focused on solving some of the usability challenges that more people within an organization can set up DAST. 

As Avi joked: “If I can set it up, anybody can.” 

It’s this new focus on accessibility that is driving much of DAST’s resurgence today. 

Will AI Replace DAST? Not Even Close. 

A major question during the session was whether agentic security systems might eventually replace DAST. 

Simon’s answer was unequivocal: “I don’t see agentic systems as being a threat to DAST and they won’t replace DAST, but I do see that DAST will feed into agentic systems, and we’ll also get LLMs configuring these systems.”  He explained that there will be a shift in the marketplace, but DAST remains unmatched and it won’t be going anywhere any time soon. 

Frank echoed this view: “LLMs are not going to get rid of DAST at all. It’s just a more expensive way to solve a problem, but they will get rid of a lot of the manual stuff.” He sees LLMs playing a role in helping to configure and scale DAST. It will look different than how people are envisioning it.  

The consensus was that AI will be leveraged to enhance DAST by automating configuration, improving coverage, and reducing human effort – while DAST continues to anchor runtime security. 

Testing AI-Powered Apps 

As organizations deploy more AI-powered applications, a critical question emerged during the session: How do we test the security of AI-powered AppSec engines? 

Frank admitted that AI introduces the need for entirely new testing requirements that go beyond traditional DAST capabilities: 

“The end goal [of trying to secure your application and trying to find vulnerabilities] hasn’t changed. But, as new technologies come out, likely the engines you involve and how you orchestrate them together will look a little bit different. And that’s where some of the value of more modern DAST tools is going to come in.” 

But eventually we will need AI solutions that can secure themselves. Frank discussed the broader vision of self-securing applications, which he broke into four essential steps: identifying vulnerabilities, triaging them, fixing them, and verifying the fix. 

“People think this idea of a self-securing application is very Star Trek,” Frank said. “I’m a huge believer. I think we’re actually a lot closer than people realize.” 

DAST already plays a central role in three of these four steps – it’s the foundation that will ultimately make self-securing applications possible. 

Why Siloed Security Tools Are Failing 

The session concluded with a discussion about fragmented AppSec stacks – having separate tools for SAST, SCA, and DAST, with each producing isolated reports with no correlation between findings. 

When asked if this fragmentation is truly as problematic as it sounds, Simon didn’t hesitate: “No, it is as bad as you’re making it sound. It’s generally horrible.” 

Issues fall through the cracks, teams lose visibility, and developers drown in noise. Frank connected this directly to the AI acceleration challenge: “If you’re generating code ten times faster and your security team isn’t getting ten times faster, then you’re going to have to make difficult decisions, and that’s where risk emerges.” 

The solution lies in unified AppSec platforms like Checkmarx One, which consolidate findings across all testing engines, correlate signals to reduce noise, and deliver security feedback directly into developer workflows – at the speed AI demands. 

What Will 2026 Look Like? 

Everyone agrees that AI-generated applications will become more standardized, making DAST more effective over time: 
According to Frank, “with LLM-generated apps, there’s more standardization… The bulk will coalesce around standard ways of doing things, and that will make our jobs easier.” 

He also predicts growing reliance on DAST as the primary method for validating AI-generated code: “People are going to rely on DAST progressively more as the way to secure AI-generated code. It’s too easy a solution to the problems we’re seeing for it not to become standardized.” 

The Takeaway: DAST Is No Longer Optional 

Across the entire discussion, the message was unmistakable: DAST has shifted from a compliance checkbox to a mission-critical security control for the AI era. 

As AI accelerates development and introduces new runtime behaviors, only DAST can reveal what is truly exploitable in the live application. Organizations that treat DAST as optional will struggle to keep up with the pace and unpredictability of AI-driven development. 

Those that embrace it and integrate it into unified AppSec workflows will be best positioned to secure the next generation of software. 

Find Out More About DAST 

Want to see the future of DAST?  Contact us for a demo and a discussion about the future – and present – of DAST and why DAST is so critical for the AI era. 

At Checkmarx, that vision has guided our work for years. And Agentic AI is our latest breakthrough. 

The Path to Autonomous Remediation 

The journey to agentic AI didn’t happen overnight.   It’s built on the following AppSec solutions that paved the way for this breakthrough: 

Real-Time IDE Scanning: In 2024, we introduced real-time IDE scanning. The idea: catch vulnerabilities while developers were actively writing code. This solution aimed to shift security left to where it matters most. 

AI Security Champion with Auto-Remediation: Next, we launched the AI Security Champion, which brought auto-remediation directly to SAST findings. This allowed developers to fix issues faster, with less manual work. 

All leading to our latest solution, Agentic AI. Checkmarx One Developer Assist moves away from automatic remediation to autonomous remediation. Powered by Agentic AI, it doesn’t just suggest fixes – it plans, executes, and validates them, ultimately delivering production-ready code. 

The result? Security expertise combined with cutting-edge AI that can autonomously fix vulnerabilities, refine code, and empower developers to build new features faster.  

What Makes Agentic AI Different? 

Here’s what autonomous SAST remediation looks like in action: 

Real-Time Feedback as You Code 
Our real-time IDE scanning already catches vulnerabilities instantly, but Developer Assist builds on that foundation with immediate, contextual feedback right where you work. Whether you’re writing code yourself or using an AI coding assistant, vulnerabilities get highlighted with their risk levels clearly marked. Developers can decide how to respond on the spot. 

• While this blog focuses on SAST, it’s worth giving a shout-out to a critical SCA capability as well: Safe Refactor. As mentioned in our recent blog, “When a vulnerable or malicious package is detected, the developer can launch agentic-AI Safe Refactor capabilities that automatically generate code changes directly within the IDE, complete with step-by-step explanations that developers can review and approve before implementation.” It points out that “Safe Refactor first attempts to replace a dangerous package with a safe version of the same package. In cases where no safer version exists, Safe Refactor leverages the developer’s generative AI tools (e.g., Cursor or Co-Pilot) to suggest alternative packages with similar functionality, ensuring developers aren’t blocked, without a path forward.” 
   

Context That Actually Helps 

Developers don’t need another wall of cryptic error messages; they need clarity and context. Agentic AI changes the game by turning static scan results into actionable insights that developers can immediately understand and use. Instead of endless triage, they get answers that drive confident, secure coding decisions right where they work- in the IDE.

• What makes this code vulnerable? Clear explanations.
  Agentic AI goes beyond flagging issues and explains why a piece of code is risky. Developers can instantly see which lines introduce a vulnerability, what pattern caused it, and how it could be exploited. By surfacing the root cause instead of just the symptom, Agentic AI helps teams learn from every fix instead of repeating the same mistakes.

• Why does this matter? Real security implications and risks.
  Understanding vulnerabilities in context means understanding their real-world impact. Agentic AI provides plain-language explanations of potential risks, from data exposure to injection attacks, helping developers see the security implications behind every coding choice. It bridges the gap between development speed and AppSec priorities, ensuring teams can act fast and stay secure.

• How should I fix it? Best practices and remediation recommendations.
  Every issue comes with best-practice remediation guidance tailored to the framework, language, and context. Whether it’s suggesting safer APIs, updating dependencies, or improving validation logic, Agentic AI provides the how so developers can fix vulnerabilities the right way, not just the fastest way.

• Can AI do this for me? Flexibility to fix manually or let agentic AI handle it.
  Sometimes developers want to fix it themselves. Sometimes, they just want it done. Agentic AI gives teams both options with manual control or autonomous remediation. It can automatically apply verified fixes, validate the change across syntax, build, functionality, and security, and deliver production-ready code. The result is less chasing, fewer vulnerabilities, and more time to innovate.
   

One-Click Autonomous Remediation 

Our AI Security Champion introduced auto-remediation and Agentic AI advances it to full autonomy. One click launches an intelligent, multi-phase process: 
 
Phase 1: Strategic Planning 
The agent connects to Checkmarx’ MCP (Model Context Protocol) server and retrieves vulnerability-specific remediation instructions. These aren’t generic suggestions; they’re precise guidelines tailored to the issue you’re dealing with. 
 
Phase 2: Intelligent Implementation 
The agent implements the fix while following strict remediation protocols. Only modifying what’s needed to fix the vulnerability, preserving functionality, and unnecessary changes. No scope creep. 
 
Phase 3: Comprehensive Verification  
Here’s the critical part. Developer Assist validates its own work through multiple checks: syntax verification, build validation, functional testing, and security confirmation – all to confirm that the vulnerability is eliminated. 

If any check fails, the agent recognizes it and adjusts, resulting in production-ready fixes, not just code suggestions. 

See it in Action

Watch how a high-severity deserialization vulnerability gets identified, explained, autonomously remediated, and verified – all within the developer’s workflow. No context switching, no friction. 

Picture a developer pushing a feature branch minutes before a release freeze. Normally that high-risk deserialization issue would trigger a ticket, a review, and a multi-day delay. But with Agentic AI running inside the IDE, that vulnerability is detected, fixed and validated before the commit even lands. The build stays green, the release stays on schedule, and nobody had to work through the weekend! 

Developer Control Stays Where It Belongs

Autonomous doesn’t mean you lose control. After remediation wraps up, developers get everything they need to make informed decisions. 

A complete summary in the IDE shows which files were modified and what was changed – what was removed and added, with a clear visual differentiation. Final status confirms everything passed and was successfully remediated. 

AI handles the heavy lifting, but developers remain the final authority on what gets pushed to production. 

Why This Is a Breakthrough

Agentic AI takes everything we’ve built – real-time scanning and auto-remediation – and takes it one step further.  

Real-time IDE scanning instantly catches vulnerabilities, and auto-remediation helps fix them. But now we have added Agentic AI which can handle everything – the planning, implementation, and validation without any manual intervention. 

Agentic AI goes beyond suggesting fixes, it automatically implements them, validates each change for syntax, build integrity, functionality, and security, and delivers production-ready code. It’s a meaningful step forward. In addition, the AI agent retrieves precise remediation instructions from Checkmarx’ MCP server, tailored to each vulnerability type. There are not generic patterns and contextual guidance is based on our security knowledge base. This specificity matters when you’re dealing with complex vulnerabilities. 

With this foundation, your team can address security findings at the speed of discovery. Each fix is automatically verified for quality and correctness, ensuring that faster doesn’t mean riskier – you get both speed and reliability. 

And throughout the whole process, developers retain complete control. They can review, adjust, or approve changes with full visibility into what the AI did and why. The human isn’t removed from the loop, we just made the loop more efficient. 

Autonomous Remediation Isn’t the Future – It’s Now

We’ve been working toward this reality for years. From real-time IDE scanning to AI-powered auto-remediation, and now to fully autonomous Agentic AI. Each step building on the last. 

This vision is taking shape with Developer Assist – autonomous, AI-driven remediation with validated, production-ready security fixes that integrate seamlessly into your workflow. It’s not about replacing developers; it’s about giving them better tools to work faster and more securely (and at the same time!).  

See it in Your Environment

Book a demo to explore how Agentic AI builds on Checkmarx’ already proven capabilities.

We’re proud to announce that Checkmarx has been recognized as a Leader in The Forrester Wave: Static Application Security Testing Solutions, Q3 2025.  

We believe this achievement is particularly significant: Checkmarx received the highest scores possible across 8 critical criteria and received the highest score in the Current Offering category of the vendors evaluated. Forrester assessed 10 SAST providers “that matter most” across current offering, strategy, and customer feedback. 

“We believe being recognized as a Leader in the Forrester Wave for SAST reflects both our relentless customer focus and our forward-looking innovation,” said Jonathan Rende, Chief Product Officer at Checkmarx. “We believe that Forrester’s recognition of our AI investments and our roadmap underscore the value we’re delivering today while also preparing enterprises to secure the future of AI-driven development.” 

Why this report matters for security professionals 

The Forrester Wave: Static Application Security Testing Solutions, Q3 2025 provides critical insights for AppSec leaders, CISOs, and development leads making strategic decisions about their application security testing infrastructure.  

This independent evaluation comes at a pivotal moment when organizations are grappling with the security implications of AI-assisted development and accelerated release cycles. 

For security professionals, Forrester’s rigorous methodology provides an objective assessment of vendor capabilities. Choosing the right application security solution can be the difference between ensuring the ideal balance between dev speed and security, and between setting an organization’s security posture back years – impacting developer productivity, and compliance readiness. This evaluation provides the independent analysis needed to make confident decisions. 

Recognized for AI-Enhanced Application Security 

As AI transforms software development, organizations need SAST solutions that can secure both human-written and AI-generated code today – not in future releases. 

According to the Forrester report, “Checkmarx stands out for its investment in AI.”  

The evaluation further notes that, “Checkmarx’s vision is to secure modern applications. To help customers develop AI with guardrails and executive visibility, Checkmarx is developing a suite of AI agents for code creation, policies, and insights. In addition, its SAST roadmap includes support for AI programming languages and frameworks, integration with AI code generators, and LLM security.” 

Experience AI-powered SAST. Get a demo now. 

Why Choose an Analyst-Recognized AppSec Solution 

We believe Forrester’s recognition of Checkmarx in The Forrester Wave: Static Application Security Testing Solutions, Q3 2025 reflects our strategic vision to secure modern applications and our leadership in AI-driven application security. Checkmarx believes being recognized as a leader in the SAST market positions them as the ideal partner for enterprises leveraging existing and emerging technologies in their application development.  

Checkmarx’s Top Ranked Criteria 

Checkmarx achieved the highest possible score of 5 across eight critical evaluation criteria, which Checkmarx believes demonstrates leadership in key areas that matter most to AppSec professionals: 

• Risk Prioritization – Scored 5 for superior capabilities in vulnerability prioritization, grouping fixes together, and correlating results from multiple security testing tools to help developers focus on the most critical issues. 

• Language and Framework Support – Achieved the top score of 5 for our exceptional breadth of programming language coverage, supporting more than 30 programming languages and 100 frameworks with transparent product support. 

• Modern Application Development Support – Received a top score of 5 for superior static analysis capabilities across AI applications, low-code languages, CI/CD pipeline security, and API security aligned with OWASP standards. 

• Policy Management – Scored 5 for advanced policy customization capabilities, centrally definable policies, and sophisticated enforcement actions across the entire software development lifecycle. 

• Application Portfolio Risk Management – Achieved with the highest  score of 5 for superior application risk visibility, code-to-cloud correlation, and automated discovery of AI components. 

• AI-Powered Tools in SDLC – Received the highest score of 5 for our investment in AI-enhanced security capabilities and integration with AI development workflows. 

• Roadmap – Scored 5 for our strategic vision and planned innovations, including AI agents for code creation, policies, and insights. 

• Supporting Services and Offerings – Achieved the highest score of 5 for comprehensive customer support, professional services, and training capabilities.

We believe these scores translate to real business outcomes: faster development cycles, reduced security debt, and lower total cost of ownership for enterprise security programs. 

What Sets Checkmarx Apart 

• Comprehensive Excellence –  Checkmarx received the highest scores possible across eight diverse criteria – demonstrating, in our opinion,  comprehensive excellence rather than point solutions. 

• AI Investment recognized – We believe our 5/5 score in “AI-powered tools in SDLC” reflects not just future promises, but current capabilities that help developers secure AI-generated code and prepare for the evolving landscape of AI-assisted development. 

• Enterprise-Ready Platform Integration – For us, our top 5/5 scores in Policy Management and Application Portfolio Risk Management demonstrate that Checkmarx One isn’t just a scanning tool – it’s a comprehensive platform designed for enterprise-scale governance and risk management. 

• Future-Proof Technology Coverage – Checkmarx believes their highest scores possible in Language and Framework Support, Modern Application Development Support, and Roadmap show that they adapt to new technologies while maintaining deep expertise across traditional and emerging development practices. 

Discover What Sets Leaders Apart 

For Checkmarx, its recognition as a Leader in The Forrester Wave: Static Application Security Testing Solutions, Q3 2025 and the highest ranked vendor in the Current Offering category validates our commitment to securing the future of application development. With the highest scores possible across eight critical criteria, we’re defining the evolving threat landscape. 

As organizations navigate AI-assisted development and accelerated release cycles, they need a trusted partner that combines deep SAST expertise with comprehensive platform capabilities. We believe this recognition confirms that security leaders can rely on Checkmarx to deliver cutting-edge innovation and enterprise-proven reliability. 

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The Forrester Wave: Static Application Security Testing Solutions, Q3 2025, Forrester Research, Inc., September 9, 2025   

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here . 

And yet, the next time the company CISO encounters it is the same night at 3:42 AM—with a call from the team that’s sitting red-eyed in boxer shorts, hunched over their laptops, puzzled by an unexpected spike in internal API traffic that is rapidly escalating into a severe security breach. 

The AppSec team did everything by the book. And still, this seemingly harmless, non-urgent vulnerability is keeping everyone up, kicking themselves for letting it slip under the radar.  

Scenarios like this are becoming increasingly common in enterprises delivering software across hybrid and multi-cloud environments. For CISOs, these aren’t just isolated incidents — they’re indicators that the current approach to managing application risk is flawed. But why does this happen? What are we missing here? And how can CISOs cope with this expanding threat landscape? 

Why Security Gaps Form in Modern Cloud Environments – A Question of Context 

Modern applications span code, clouds, and containers. They’re composed of different third-party libraries, developed across global teams, and deployed multiple times per day. 

In this multidimensional world, traditional AppSec methodology can protect the separate parts and processes but simply doesn’t scale to capture the interactions between components in the cloud environment. It’s as effective as a beekeeper working with a patchwork suit.  

CISOs and AppSec leaders can check all the boxes on the relevant scans: SAST, DAST, SCA, IaC. They’re building their AppSec program by the book.  

But the book itself needs a rewrite. It doesn’t apply to a multi-faceted cloud environment: All the tools operate in silos. Each one produces its own findings. Each team reviews them independently. And when something slips through, no one sees the full picture, because they’re missing the context. 

If we go back to the hypothetical we started with – the one that ruined a certain CISO’s good night’s sleep, here’s what happened: The code flaw was identified by the scan, and on its own, wasn’t much to look at and was classified as low-severity. But it was in an exposed API processing regulated customer data, running with elevated permissions, deployed via infrastructure with misconfigured access controls—and so in that context, it became a serious risk with implications for compliance, reputation, and financial exposure. None of the individual tools identified it as urgent because none had the full context. 

How The Cloud Changed Everything About Application Risk 

Most organizations have successfully transformed their development practices to embrace cloud-native approaches, but their security setup still operates in silos: 

• Application security teams focus on SAST, DAST, and SCA, but lack visibility into how code vulnerabilities manifest in cloud environments. 

• Cloud security teams monitor infrastructure configurations and runtime activity, but don’t understand how application code interacts with cloud services. 

• DevOps teams are caught in the middle, receiving conflicting guidance from both groups. 

The rapid evolution of cloud-native architecture—with its containers, Kubernetes, serverless functions, and proliferating APIs—has created an expanded attack surface that traditional security models weren’t designed to protect.  

Most enterprises’ security approach failed to catch up to their development architecture. A 2023 survey of over 1,000 IT security professionals revealed that 90% of organizations operate with siloed risk and compliance data—and these organizations experienced breaches at nearly twice the rate of the 10% with integrated security approaches. 

This siloed approach fails because in modern cloud environments, application risk isn’t determined solely by code quality. A seemingly minor vulnerability can become critical depending on: 

• The cloud services the application interacts with 

• The permissions and IAM configuration of the application’s service accounts 

• The network exposure of vulnerable components 

• Whether the application processes sensitive data 

• The cloud provider’s shared responsibility boundaries 

This new reality means traditional application security tools that focus purely on code analysis are missing half the picture. Similarly, cloud security tools that don’t understand application vulnerabilities miss critical context about what’s actually exploitable. For CISOs, this means that even a well-funded AppSec program can leave high-risk exposures unaddressed if the organization lacks visibility across this full matrix. 

This is exactly where a new security model needs to be introduced: the “Fix–Find–Fuse” model, or 3xF for short. 

What’s the 3xF Model, and How to Implement It 

The Fix-Find-Fuse model is a practical framework for CISOs that helps security leaders evolve their program — not by replacing everything, but by weaving together what’s already in place. It surfaces the insights that matter most from the clutter, connects findings across domains, and equips teams with the live context they need to prioritize their focus and act upon it. 

For enterprises investing in scalable, cloud-native application security platforms, this framework serves as a north star for what a modern AppSec operating model should look like—one that harmonizes today’s architectures, development velocity, and organizational structure to hone security processes, close coverage gaps and reduce DevSec frictions. 

Let’s break it down and focus on each of its three parts.  

1. Fix: Align Existing Tools to Modern Development 

Most organizations already have the building blocks of a strong security program. But those tools must evolve to match today’s velocity and complexity. That means: 

• SAST should be incremental, developer-native, and pipeline-integrated. It should scan code early and often—without slowing builds. After running a complete scan, you can run incremental scans on only changed code. Additionally, in the IDE itself, get real-time insights on security best practices before committing code. 

• SCA should prioritize vulnerabilities in the context of usage, reachability, and exposure—not just list every CVE in the dependency tree or simply serve as an SBOM generating machine. 

• DAST should shift from crawling web forms to testing APIs and cloud services based on OpenAPI specs. DAST can help identify shadow and zombie APIs which are then further tested using an API Testing tool. 

• IaC scanning should happen before infrastructure is provisioned, identifying misconfigurations in Terraform, Helm, and Kubernetes. 

As you scan a single application, you should be able to launch scans of multiple engines at the same time and correlate results across tools. By understanding context, you can better understand risk. The result enables more precise results, with fewer false positives and false negatives, and a better sense of where risk actually lies. 

These adjustments make your tools more useful. But they also expose where they fall short: in context, prioritization, and coordination. 

2. Find: Identify Your Gaps  

Blind spots multiply with architectural complexity. As enterprises adopt microservices, containerization, and multi-cloud strategies, traditional boundary-based security models fail to capture the interconnected nature of modern applications.  

Scale amplifies the cost of incomplete visibility – what appears as an acceptable gap in development environments becomes a critical risk when replicated across thousands of production instances and cloud regions. 

That is why this second step in the 3xF model is perhaps the most crucial: to establish complete and continuous visibility—not just into vulnerabilities, but more importantly, into blind spots. True visibility requires mapping what exists before determining what’s vulnerable:  

• Establish continuous attack surface discovery as your foundation: Before code scanning can be effective, you must regularly inventory all assets – including undocumented services, ephemeral workloads, and shadow IT that exist outside formal CI/CD pipelines. You can’t secure what you don’t know exists. 

• Build API governance around data flows, not just endpoints: Look beyond API documentation to understand how data actually traverses your environment. Track sensitive information from ingestion to processing to identify exposure points that formal specifications miss. As mentioned above, use SAST and DAST to identify shadow and zombie APIs.  

• Catch IaC misconfigurations before they reach production: Shift IaC security scanning left to catch misconfigurations during the design phase. Preventing problematic configurations from reaching production is exponentially more efficient than remediating them after deployment.  

• Identify which third-party components are reachable from external traffic: Map dependency chains to understand where third-party code interacts with critical systems. Prioritize remediation based on actual application connectivity rather than isolated vulnerability scores. 

Without this foundation, AppSec investments are misaligned with actual risk exposure—and the organization is flying blind. 

3. Fuse: Create a Holistic Security Vision 

The final step in the 3xF model is about meaningful integration rather than mere collection. While “Fix” updates your tools and “Find” identifies your gaps, “Fuse” transforms disconnected security data into a coherent security strategy that cuts through complexity to surface what truly matters. “Fuse” is what elevates AppSec from operations to strategy. 

This fusion operates through several key mechanisms: 

• Favoring integration over replacement: Rather than ripping out existing security tools that work, invest in platforms that connect and enhance what you’ve already built. Seek solutions that create a unified view across your security toolchain while leveraging open standards for data exchange. The goal isn’t a new dashboard—it’s revealing relationships between findings that remain hidden when viewed in isolation. Effective integration preserves your existing investments while multiplying their value through context. 

• Prioritizing based on context, not CVSS: Modern risk isn’t defined by severity alone. The relevant questions here are: (1) How exploitable is this vulnerability in the context of our environment?; (2) Does it touch sensitive data? (3) Is it exposed to the internet?
  

The goal is smarter triage. Risk scoring must reflect exposure, cloud permissions, API protection, and runtime behavior—not just code flaws in isolation. 

• Making security native to development: Security shouldn’t feel external to engineering. The best programs deliver feedback directly into the developer’s flow—whether that’s in an IDE, pull request, or pipeline. It should seamlessly integrate into their environment, be as frictionless as possible and work behind the scenes.  
  They shift scanning left, automate enforcement, and make remediation guidance clear and fast within the development environment – not external security tools. Security becomes a workflow accelerator, not a blocker. 

• Breaking down silos between code and cloud: Siloed teams miss correlated risks. Unified security programs foster shared visibility across AppSec and CloudSec. They align risk models across teams, establish consistent metrics, and build cross-functional security squads that own end-to-end outcomes—from code to configuration to cloud. 

• Finding the right metrics: Legacy metrics like “vulnerabilities found” don’t reflect posture. Modern programs track: (1) Mean time to remediate exploitable cloud-exposed risks; (2) Reduction in excessive cloud permissions; (3) Alignment of findings to critical business applications.

These metrics speak to business risk—not just security activity. These are board-ready KPIs—not vanity metrics. 

Embedding Application Security Posture Management (ASPM): ASPM is not a scanner—it’s the layer that connects scanners to each other, and to business logic. It correlates code-level vulnerabilities with runtime context, identity configurations, cloud architecture, and sensitive data exposure. When implemented correctly, ASPM enables: 

• A unified view of application assets across dev and cloud 

• Contextual risk scoring that goes beyond CVSS 

• Development and runtime context 

• Routing of findings to the right teams based on ownership and impact 

This is where security becomes more than just detection. It becomes direction. 

 
What Does 3xF Look Like in Practice? 

Here’s how a hypothetical might play out in a real-world enterprise that employs the 3xF model: 

1. Detection Phase: Multiple security tools identify what appear to be isolated issues: 

• An incremental SAST scan flags a parameter injection issue in a Go microservice 

• SCA identifies a vulnerable library reachable from an API endpoint 

• IaC scanning finds default allow-all ingress rules in the environment 

• DAST confirms the API lacks rate limiting and responds to malformed payloads 

2. Correlation and Prioritization: The AppSec manager receives a critical alert as these findings are correlated, recognizing that together they create an exploitable path to regulated customer data. The development lead is automatically notified via a Jira ticket enriched with contextual risk details and remediation steps. 

3. Response and Remediation: The security team implements temporary API gateway rules for immediate protection while developers address the root causes: 

• Fixing the parameter injection using contextual guidance delivered in the IDE 

• Upgrading the vulnerable library to a secure version 

• Updating infrastructure code to enforce least-privilege access 

• Implementing API rate limiting to prevent abuse 

4. Verification and Improvement: Post-remediation scans confirm the complete resolution of all issues. The incident is documented internally, feeding metrics like mean time to remediation and contributing to ongoing risk reduction. 

In isolation, each tool might have marked its finding as low priority. Together, these interconnected vulnerabilities represented a high-risk situation that demanded immediate attention — highlighting the power of the 3XF approach.  

The Road Ahead: Finding Clarity in the Cloud 

The attack surface is expanding fast. APIs, multi-cloud workloads, ephemeral infrastructure, and AI-generated code are redefining how applications are built and breached in the cloud era.  

In this new reality, security leadership isn’t about owning more tools. It’s about owning the model. 

The 3xF framework offers a path forward. It equips CISOs to drive unity across teams, clarity across data, and prioritization across findings. But it’s only effective when implemented on a platform designed to bring application and cloud security together—unifying code, configuration, and context. 

You don’t need to rip and replace your stack. You need to rearrange it and the process around it in a way that will provide your team with visibility into what’s deployed, insight into what’s vulnerable, and confidence that they fix what matters most – first. 

Learn more about Checkmarx One’s comprehensive code-to-cloud security platform and how it can help you adopt the 3xF framework.  

Just over a decade ago, CISOs tried to ban Dropbox and Google Drive to stop unsanctioned file sharing. That didn’t work. Cloud apps simply went underground—until security leaders realized that blocking wasn’t the answer. Governance was. 

Today, AI coding tools like GitHub Copilot and Amazon Q are the new Shadow IT. Developers are using them—sometimes with approval, but mostly without. And almost always with insufficient oversight or policy guardrails. 

They’re moving fast. Ignoring it won’t stop the adoption and trusting dedicated AI coding tools and existing security protocols to be ‘secure enough’ is a leap of faith CISOs can’t afford. 

This article skips the AI hype and gets practical, providing CISOs and security leaders with a brass-tacks guide to secure AI-generated code at the pace it’s being written—with real-time IDE scanning, instant feedback in Github repos, enforceable governance, and tools like Checkmarx One. 

But first, a quick review of the AI-generated code landscape.  

The Reality of AI Coding Adoption – the Train Has Long Left the Station 

Checkmarx’ upcoming 2025 global survey, conducted with Censuswide, found that AI coding tools have already become a core part of modern development workflows.  

Across CISOs, AppSec managers, and developers, nearly 70% of respondents estimated that more than 40% of their organization’s code was AI-generated in 2024, and 44.4% of respondents estimating 41–60% of their code is AI-generated.  

This stat is corroborated by the Stack Overflow 2024 Developer Survey, showing that 76% of developers now use AI tools in their work. 

AI-Generated Code – The New Risky Norm

AI coding assistants like GitHub Copilot, Gemini Code Assist, Cursor, and Amazon Q Developer don’t replace built-in security. They are not a replacement for AST testing. While they can make development faster, even the vendors recommend using “automated tests and tooling.”  

Relying on AI coding assistants to be secure-by-default falls short. Among other risks, AI coding assistants potentially introduce new risks such as hallucinated code or prompt injection, and manual reviews alone struggle with scalability. Their transparency is also limited, as they provide only vague details on their model training or AI vulnerabilities.  

A 2024 empirical study on Security Weaknesses of Copilot-Generated Code in GitHub Projects analyzed 733 Copilot-generated snippets from GitHub projects. It found that 29.5% of Python and 24.2% of JavaScript snippets contained security weaknesses, including XSS and improper input validation. 

AI-generated code is not inherently more secure than human-generated code. Just as human-generated code imposes security risks, so does AI. But what’s different is the scale and speed of AI-generated code, as well as the psychological factors which lead to the lack of oversight.   

Why AI-Generated Code Gets Less Review and Creates Security Risks

Because developers might not fully understand or carefully review code created by AI, this code could end up having more security problems and errors compared to code written and checked by people. The way AI creates code can be unclear, and it might learn from flawed examples. If developers trust AI too much and don’t double-check its work, issues can easily be missed.  

Research shows AI-generated code often receives less careful checking than human-written code, creating serious security risks. Developers feel less responsible for AI-generated code and spend less time reviewing it properly.

Research also shows that coders using AI tools wrote more insecure code than those who didn’t. This false confidence is made worse because many developers have an unfounded sense of trust in AI-generated code and are less familiar with the logic behind it. Without proper review processes and specialized tools for checking AI-generated code, these problems will continue as developers trust AI too much without verifying its work. 

That’s why securing AI-generated code requires a new kind of strategy: one tailored to the unique challenges it poses. 

A CISO’s Strategy for Securing AI-Generated Code 

To address the rising complexity and scale of AI-written code, CISOs must implement a layered strategy that combines real-time technical controls with organizational governance.  

Governance Controls 

Governance controls help CISOs enforce responsible AI adoption at scale by defining boundaries, policies, and shared responsibilities that span development, security, and compliance teams. Some of these governance controls are good practices, even when dealing with human-generated code. But they become even more important when AI is added to the mix. 

Here’s what CISOs should be doing:  

AI Code Usage Policies 

Establish granular policies to govern AI tool usage: 

• Specifying which AI tools are permitted, and for what capacity. 

• Defining acceptable use cases (e.g., prototyping vs. production code). 

• Ensuring that AI generated code is clearly identifiable. 

• Limiting use of AI-generated code in sensitive or critical components, such as authentication modules or financial systems.  

• Mandating peer reviews to ensure quality and security 
  

Security Review Processes 

Formalize the review process. This means: 

• Establishing thresholds for when reviews are required (e.g., all AI-generated code touching sensitive systems or business logic) 

• Assigning responsibility to trained AppSec reviewers or peer developers, and integrating those reviews into PR and CI/CD workflows  

• Defining reviews in multiple places in the SDLC: Pre-review, following commit, within CI/CD tools using a tool like Vorpal,  etc. 

• Aligning reviews to ensure code meets secure coding standards like the OWASP Top 10  

• Training reviewers on how to review AI-generated code, and what to look for – going beyond functionality checks and into the inspected code handles inputs, sanitizes data, and manages privilege boundaries 
  

Developer Education 

Invest in training that goes beyond general secure coding principles and focuses on the unique risks posed by GenAI tools. 

Developers should understand how AI models generate code, the security weaknesses they’re prone to introducing, and how to critically evaluate AI-generated snippets before integrating them.  

This includes recognizing the limits of AI suggestions and validating logic paths. To reinforce this mindset, organizations can incorporate ongoing, role-specific education into developer workflows through platforms like Checkmarx Codebashing. 

Cross-Functional Accountability

 Build formal accountability frameworks that unite AppSec, DevOps, and compliance.  

This includes setting shared KPIs (like reducing AI-originated vulnerabilities or improving time-to-remediation), maintaining audit trails for how AI-generated code is reviewed and approved, and running regular cross-team assessments to track policy adherence. 

Culturally, it means shifting from siloed enforcement to shared ownership, where developers, too, are aware of compliance expectations, and security teams offer collaborative, context-aware guidance. 

Technical Controls 

Technical and governance controls complement one another. With technical controls, the focus is on automated, scalable solutions that integrate into development pipelines. 

 Implementation should leverage existing security tools, prioritize critical systems, and ensure measurable risk reduction without diving into granular configurations. Below are the main technical controls: 

Automated Security Testing

 AST testing, including SAST, DAST, API Security, and SCA are foundational tools for detecting known vulnerabilities in source code and applications, insecure dependencies, and misconfigurations across the SDLC. While they’re not enough on their own to secure AI-generated code, they remain essential as a baseline layer of protection in any application security strategy. 

Real-Time IDE Scanning (AI Secure Coding Assistants) 

AI Secure Coding Assistants guide developers when working with AI-generated code, by identifying insecure patterns and recommending secure alternatives in real time. 

They offer contextual suggestions as code is written, helping developers spot flaws early, before code reaches staging or production environments.  

Real-time scanning inside the IDE helps developers flag potential risks and coding patterns that lack best practices. This is useful for human-generated code, but it is critical for AI-generated code. 

These tools provide instant feedback on short snippets of code before it’s even committed, surfacing risks like unsafe input handling or insecure defaults.  

For developers using GitHub Copilot, ASCA can even generate remediation suggestions, turning AI from just a coding assistant into a security partner.

Unlike SAST, which analyzes entire applications post-commit, IDE scanning focuses on localized code blocks—not replacing deep analysis, but rather tightening feedback loops so developers learn secure coding practices in real-time. 

See ASCA in action: 

Pre-merge Developer Feedback  

Vorpal, a lightweight GitHub Action, provides a critical security checkpoint at the pull request stage. Acting as the last line of defense before code enters your main branch, Vorpal flags violations of secure coding best practices with results visible directly in GitHub’s interface.  

Available as a free GitHub Action for developers worldwide, Vorpal is particularly effective with AI-generated code, which may appear syntactically correct but carry hidden risks due to insecure patterns.

Unlike traditional security gates that slow development, Vorpal integrates seamlessly into existing workflows, allowing teams to maintain velocity while enhancing security.

Additional AI Tools 

AI tools will sometimes suggest insecure open-source packages. SCA identifies a secure package. If no alternative secure package exists, Checkmarx can suggest a package with similar functionality. 

Security integration into AI tools is helpful. For example, Checkmarx One integrates into ChatGPT and GitHub Copilot to automatically scan source code and identify malicious packages, within the AI interface itself. 

Isolated Execution Environments 

Sandboxing or containerization to test AI-generated code in controlled environments, limit the blast radius of potential flaws or malicious logic.  

API Security 

APIs are particularly sensitive to risks introduced by AI-generated code because they’re high-exposure entry points into critical systems. AI tools might accidently generate code referencing non-existent APIs, misusing authentication flows, or API implementations. 

API security tools mitigate these risks by offering automated discovery, traffic inspection, anomaly detection, and AI-driven exploit prevention.  

They help enforce strong authentication (e.g., OAuth, JWT), validate inputs, and block business logic abuse, making them a vital control point for detecting and preventing AI-induced API vulnerabilities.  

How Checkmarx One Secures AI-Generated Code 

Checkmarx One protects AI-generated code through a layered defense strategy that spans the entire development lifecycle.

The platform combines foundational AppSec tools—SAST, DAST, SCA, Secrets Detection, Malicious Package Protection, Container Security, IaC Security, and API security—with AI-specific controls designed for the unique challenges of AI-generated code. 

What sets Checkmarx One apart is its comprehensive approach to security: ASCA catches issues as developers write code within the IDE, comprehensive SAST/DAST scans offer deeper analysis before deployment, and the platform integrates seamlessly with open source tools.

For GitHub users, Checkmarx’s free, open-source Vorpal tool provides an additional security checkpoint during pull requests, complementing the Checkmarx One platform. This multi-layered approach ensures AI-generated code receives appropriate scrutiny at each stage of development. 

Most importantly, Checkmarx One brings it all together within a single platform, providing CISOs, AppSec leaders and developers with complete visibility and consistent enforcement from code creation to deployment. 

Security integration directly into AI coding tools represents the newest frontier in application security. Checkmarx One now offers integrations with popular AI coding assistants, automatically scanning generated code and identifying security issues without requiring developers to switch contexts.

These integrations can also help identify potentially malicious packages that AI assistants might suggest, offering secure alternatives with similar functionality when available. This approach meets developers where they are—inside their preferred AI tools—rather than requiring them to adopt yet another security solution. 

Conclusion 

AI-generated code is no longer an emerging challenge – it’s the new normal. As with any technological breakthrough, it introduces significant benefits and new risks—both demand attention. With the right combination of tools and governance, CISOs can ensure their teams embrace the productivity gains of AI coding assistants without compromising security.  

The organizations that will thrive in this new landscape won’t be those that resist AI-generated code, but those that secure it effectively. Checkmarx One offers a unified approach to this challenge, helping security teams keep pace with AI-accelerated development while maintaining robust protection.

Request a demo to see how Checkmarx One helps secure AI-generated code while maintaining your development velocity.

Delivery methods have undergone an equally significant shift. Traditional waterfall processes with predictable, infrequent releases have given way to continuous delivery pipelines where code changes deploy multiple times daily. With this acceleration, security can no longer function as an end-of-cycle gate without becoming a major bottleneck. 

This evolution has driven the shift to DevSecOps, where security integrates throughout the development lifecycle rather than being a separate phase.  

Security responsibilities have shifted from specialized teams to a shared model where developers actively participate in securing applications. Since Checkmarx pioneered SAST in 2006, the company has evolved its solutions to address these changing dynamics. 

This article explores the practical implications of modernizing Application Security: why legacy SAST is no longer enough, what a modern cloud-based platform can offer, and how teams can make the move with minimal disruption and maximum impact. 

Today’s Development Demands Flexible Security 

On-premises SAST solutions have built a strong security foundation for many organizations. However, as development practices evolve, several challenges have emerged. 

Key limitations of on-premises SAST 

• Infrastructure Overhead: Running security infrastructure requires dedicated hardware and software licenses that consume IT resources 

• Scalability Bottlenecks: Fixed scanning capacity creates bottlenecks during busy development periods, potentially slowing delivery 

• Integration Complexity: Connecting to modern CI/CD pipelines often needs custom integration work that requires specialized expertise 

• Developer Friction: Complex security tools drive developers to find workarounds, creating potential blind spots 

• Limited Coverage: Modern applications contain many components beyond custom code – APIs, containers, cloud services – that need specialized security testing that SAST doesn’t provide 

The evolution of software development means security teams must scan more code, more frequently, across more technologies than ever before. This scaling challenge is particularly evident during peak development periods when multiple teams need concurrent scanning. 

How Cloud-Native AppSec Builds on Proven Foundations 

Modern platforms like Checkmarx One address these challenges with cloud-native capabilities designed for speed, scale, and simplicity, without sacrificing security. They extend the benefits of traditional SAST while removing the bottlenecks. 

Key advantages of moving to a modern AppSec platform 

• Infrastructure Freedom: Eliminates hardware procurement cycles and infrastructure management, reducing IT overhead 

• Elastic Capacity: Scales automatically to match development workloads, preventing bottlenecks even during peak periods 

• Built-in Connectivity: Offers out-of-the-box integrations rather than custom connections, simplifying toolchain integration 

• Continuous Updates: Updates security engines automatically without disruption, ensuring up-to-date protection against emerging threats 

• Global Access: Supports distributed teams with consistent access from anywhere, matching modern work patterns 

Cloud-native platforms reduce IT burden, eliminate scanning delays, and keep security in step with development velocity. 

Better Developer Experience, Better Security 

Developers determine the success of security tools. If the experience is smooth, security gets used. If not, it gets bypassed. 

Checkmarx One improves the developer experience by embedding security directly into daily workflows and providing developers with key capabilities: 

Watch this quick overview to see how Checkmarx One brings security into the IDE: 

• IDE Integration: Embeds security directly in Visual Studio, VS Code, Eclipse, and JetBrains IDEs where developers spend their day. Even ASPM capabilities are available within the IDE, helping development teams prioritize critical risks and manage AppSec posture. This type of integration makes secure coding part of everyday work. 

• Automatic Scanning and Decorated Pull Request – Automatically summarize security changes in the SDLC. 

• Shift-Left Feedback: Identifies problems live while writing code, rather than later, when context is lost and fixes become more complex 

• Easy IDE setup: Lightweight plugins that install in seconds 

• Scan Local Branches: Scan local branches in the IDE, before deployment 

• Native DevOps Connection: Connects seamlessly with GitHub, GitLab, Azure DevOps, Bitbucket and other source repositories and pipelines. No complex set up required 

• Flow Preservation: Keeps developers in their workflow instead of switching contexts, maintaining productivity 

• Auto-remediation: Provides specific, practical fix guidance through AI assistance, taking it a step further from just “you have a problem” to “here’s how to fix it” 

Instead of the traditional model where developers wait for security feedback after committing code, Checkmarx One provides immediate guidance during development.  

For example, when a developer writes code containing a potential SQL injection vulnerability, Checkmarx One can highlight the issue in real-time within their IDE, explain the security implications, and suggest a specific fix – all before the code is even committed.  

This real-time feedback loop helps developers resolve issues before code is even committed—dramatically reducing the time and cost of fixing vulnerabilities. 

Checkmarx One also supports enterprise-scale security management, including policy enforcement and build-breaking for violations. 

Security Coverage Beyond SAST 

Securing modern applications requires more than scanning custom code. Checkmarx One goes beyond SAST to cover the full SDLC—from open-source dependencies and API endpoints to containers and cloud infrastructure. 

Additionally, unlike many platforms that are pieced together through acquisitions, providing a disjointed user experience, Checkmarx One is built as a holistic end-to-end solution from the ground up, fully incorporating the following capabilities and components: 

• Software Composition Analysis (SCA): Finds vulnerabilities in open-source components, identifies licensing issues, and detects malicious packages 

• Malicious Package Protection: Helps you identify — and eliminate the dangers of — malicious open-source packages throughout the SDLC, leveraging the industry’s largest database of malicious packages 

• Secrets Detection: Prevents the exposure of secrets by detecting and validating hardcoded passwords, access tokens, keys, and other sensitive credentials — while proactively blocking any Git commit containing secrets, ensuring that they never reach shared repositories 

• Repository Health: Helps improve your security posture with full visibility into the security, dependency management, and maintenance health of the code repositories used in your applications 

• Dynamic Application Security Testing (DAST): Discovers runtime vulnerabilities by testing running applications, finding issues that only emerge during execution 

• API Security: Detects weaknesses in API implementations, identifies misconfigured endpoints, and validates input validation 

• Infrastructure-as-Code (IaC): Catches misconfigurations before cloud deployment, preventing insecure cloud resources 

• Container Security: Checks Docker images and Kubernetes configurations for vulnerabilities and security issues 

Each engine addresses security concerns specific to its domain, providing comprehensive coverage that a single testing approach can’t achieve alone.  

Multiple scan types can run simultaneously, with correlated results across engines, giving security teams complete visibility into the issue. This increases accuracy as the vulnerability context helps prioritize application risk. 

This approach has several key advantages: 

• Complete visibility across custom code, third-party components, runtime behavior, and infrastructure 

• Reduced tool sprawl by consolidating multiple security functions in one platform 

• Consistent policy enforcement across all application components 

• Simplified compliance through comprehensive coverage and reporting 

For security teams, this means more efficient operations and better risk coverage. For developers, it means a single set of security guidelines rather than conflicting requirements from multiple tools. 

Application Security Posture Management: Security at Scale 

As AppSec matures, scanning alone isn’t enough. Security teams need to understand posture, risk, and trends across all their apps. That’s where ASPM comes in. 

Checkmarx One’s Application Security Posture Management (ASPM) capabilities help teams scale their security operations by: 

• Risk-based prioritization: Evaluating vulnerabilities based on actual risk factors like exposure, data sensitivity, and exploitability 

• Portfolio-wide visibility: Providing unified visibility across all applications, allowing teams to identify systemic issues 

• Policy standardization: Implementing consistent security standards organization-wide through automated policy enforcement 

• Security trend analysis: Tracking security improvements over time with clear metrics and visualizations 

• Vulnerability correlation: Connecting related findings across testing types to reveal broader security patterns 

This approach shifts security from reactive vulnerability management to proactive risk reduction. Instead of chasing individual findings across different systems, security teams can focus on the highest-impact issues and systemic improvements. 

For example, ASPM capabilities might reveal that certain teams consistently struggle with the same security patterns, highlighting opportunities for targeted training. Or they might show that a specific framework is responsible for a disproportionate number of vulnerabilities, prompting architectural review. With Checkmarx One, ASPM is even brought into the IDE. Discover why this shift is critical in our recent blog, ASPM is for Everyone. 

AI Transforms Security Effectiveness 

AI features enhance both developer workflows and security team capabilities. 

Checkmarx One integrates artificial intelligence to streamline application security processes, offering tools that assist both developers and security professionals:​ 

• AI Secure Coding Assistant (ASCA): ASCA provides real-time feedback by scanning code as developers write it. It identifies security best practice violations and, when integrated with tools like GitHub Copilot, suggests remediation snippets to address these issues promptly 

• AI Security Champion: Generative AI-driven remediation suggestions for vulnerabilities detected by SAST and Infrastructure as Code (IaC) scans. It aids developers in understanding and resolving security issues efficiently within their development environment 

• AI Query Builder: Assists security teams in crafting custom queries for SAST and IaC scans. By leveraging generative AI, it simplifies the process of writing and refining queries, enabling tailored security assessments for specific applications​ 

• Integration with Generative AI Tools: Checkmarx One integrates with platforms like GitHub Copilot and ChatGPT, helping teams identify vulnerabilities earlier – before they’re deployed 

These AI-powered features are designed to enhance the effectiveness of application security efforts, enabling teams to identify and remediate vulnerabilities more swiftly and accurately.​ 

Supporting Your DevSecOps Journey 

Transitioning to DevSecOps requires more than tools – it needs a platform that connects security with development processes. Integrations need to be easy to do and out-of-the-box. As DevOps and continuous delivery have become the norm, your security tools need to seamlessly integrate with your pipeline without complex implementation. Checkmarx One supports this shift through: 

• Unified Visibility: Dashboards showing security across all applications in a single view 

• Intelligent Risk Ranking: Algorithms that identify critical issues based on multiple risk factors 

• Automated Governance: Policy enforcement without manual intervention or security bottlenecks 

• Connected Insights: Analytics that link findings across different testing types and applications 

Organizations using Checkmarx One report faster deployment cycles without compromising security. By embedding security checks throughout the development process rather than concentrating them at the end, teams catch issues earlier when they’re easier and cheaper to fix. 

This approach also improves collaboration between security and development teams. With shared visibility and clear metrics, both groups can work together on improving security rather than engaging in the traditional back-and-forth about whether issues are real or important.  

Making Migration Practical 

Security concerns are often a barrier to cloud adoption. Checkmarx One addresses these head-on with enterprise-grade protections: 

Security of the platform itself 

 Checkmarx One addresses this with comprehensive measures: 

• End-to-end encryption for code and findings both in transit and at rest 

• Granular role-based access controls that can match or exceed on-premises permissions 

• SOC 2 Type II and ISO 27001 certifications verifying security practices 

• Regular penetration testing and security assessments 

Maintaining protection during transition  

The migration process includes: 

• Parallel running of both platforms during the transition period 

• Step-by-step application migration with verification at each stage 

• Policy verification to ensure consistent security standards 

• Results comparison to validate detection capabilities 

• Clear milestones and success criteria for each phase 

This structured approach ensures continuous protection throughout the migration process, allowing organizations to move at their own pace without creating security gaps.  

When users migrate from CxSAST to Checkmarx One, they can migrate: 

• Existing users and expand identify management 

• Previous triage work 

• Previously customized presets 

• Previously customized queries 

What does this mean in practice? 

Let’s take the example of a large US financial institution that recently upgraded from their on-premises SAST to Checkmarx One: By moving they streamlined their workflow and enhanced efficiency.  

The result? A 2000% increase in scan volume and 100% vulnerability backlog reduction. 

By evolving your application security strategy, you can move faster, reduce risk, and build software with confidence. Explore our migration resources for best practices, insights, and resources. 

Ready to take the next step to modernize your AppSec program?

Whether you’re currently using an on-prem Checkmarx solution or a competitor’s legacy tool, now’s the time to see what a modern, cloud-native platform can do for your organization.  

Request a demo of Checkmarx One to explore what migration looks like in practice for you. 

And while they nodded and thanked you politely, and maybe even asked you a couple of questions — as they always do—their faces left you with a nagging doubt: how much of it did they really get?

You’re in good – and overwhelmingly common – company.

A snapshot of the industry reveals that the average board member has limited-to-no understanding of cybersecurity: 59% of directors admit they struggle to understand cyber risk drivers, according to a 2022 PwC report.

Moreover, despite growing awareness of cyber risk and cybersecurity being the most challenging area of oversight for corporate leaders, according to the Diligent Institute and Corporate Board Member Survey, boards are not doing enough to bridge the educational and communicational gap:

• Only 47% of board members interact regularly with their CISOs, according to the Harvard Business Review
• Checkmarx’ own 2024 survey of 200 CISOs revealed that 20% of their boards don’t ask about applications security.
• Only 51% of boards reviewed their process for identifying and disclosing a cybersecurity incident, according to a 2025 survey.
• Another study reveals that 60% of board members felt they had insufficient training on cyber resilience in the past year

That is why many CISOs enter the boardroom armed with metrics on attack vectors, vulnerability rates, and compliance checklists, only to be met with confusion or polite indifference.

The issue isn’t that they don’t consider cybersecurity important. In fact, 74% of companies in the Russell 3000 index have codified cybersecurity oversight at the board or committee level. .

But the reality is that most boards are made up of executives with backgrounds in finance, law, and operations — usually not security. For example, consider the board of a publicly traded coffee chain valued at $9 billion (Dutch Bros). Of the ten board members, seven come from retail, two from finance, and just one from cybersecurity, and this is not an outlier.

When a typical board must deal with the topic of cybersecurity in practice, their “complexity aversion bias” kicks in, and they’d rather brush past it to check the necessary boxes, to move on to the topics that are closer to their comfort zone. This self-reinforcing cycle only widens the communication gap and perpetuates the problem.

CISOs live in a world of security frameworks, attack vectors, and risk mitigation. Board members, however, speak the language of EBITDA margins, capital allocation strategies, and competitive market positioning. When this disconnect isn’t addressed, cybersecurity budgets get slashed, critical security initiatives stall, and CISOs are left out of key business decisions.

If security leaders want to get board-level buy-in, they must learn how to translate their messaging to the language that the board would understand. They must master ‘Boardish.’

What’s The Cost of Not Speaking ‘Boardish’

When cybersecurity isn’t communicated effectively, there can be dire consequences:

• Getting security budgets approved is a struggle, and security spending is at constant risk of being deprioritized in favor of revenue-generating initiatives.
• CISOs are sidelined from the strategic decision-making process, often finding out about big moves when they’re already underway and having to adjust on the fly.
• Insurance premiums spike unexpectedly as cyber insurance becomes both more expensive and more restrictive, creating significant budget disruptions.
• Crisis response is chaotic during incidents, directly impacting breach costs and recovery time when boards haven’t authorized proper incident response resources.
• Compliance violations escalate in severity as boards often don’t grasp the difference between technical findings and material violations with financial consequences.
• Competitive disadvantages develop as security becomes a market differentiator, affecting revenue when sales cycles lengthen due to customer security requirements.
• Disconnected risk management frameworks emerge where security metrics don’t align with the enterprise risk appetite the board has established.
• Third-party risk management becomes ineffective as boards approve vendor relationships without understanding the technical security implications.
• Most importantly, no matter what happens along the way, the buck stops with the CISO. They remain accountable for breaches and security failures, even when boards fail to listen, understand, or allocate resources necessary for adequate security measures. This accountability paradox creates a precarious position where security leaders bear responsibility for outcomes they weren’t empowered to prevent—potentially putting their careers, reputation, and even legal standing at risk.

These consequences aren’t just theoretical risks—they represent real business impacts that are ready to materialize at any moment (if they haven’t already) when the translation gap between security and business leadership persists. That’s why mastering ‘Boardish’ isn’t optional—it’s the difference between being viewed as a cost center or a strategic business partner.

So, how does one approach speaking ‘Boardish’?  Recognize that the Board is not one audience.

Board members have different priorities and perspectives. The CFO may worry about financial impact, while the CEO focuses on business continuity and the General Counsel prioritizes regulatory compliance. Think of it as different dialects of ‘Boardish’ – each member speaks the same language, but with distinct vocabulary, concerns, and priorities that reflect their expertise and responsibilities.

Who’s in the Room?

According to Spencer Stuart’s 2024 U.S. Board Index, among newly appointed S&P 500 directors:

• 29% have financial expertise
• 30% are active or retired CEOs
• 19% come from the technology/telecommunications sector

Common Biases

We all have biases, and board members are no exception. Whether conscious or unconscious, these biases shape how they perceive cybersecurity risks and decisions. Biases vary from one individual to another, based on their background, position, current concerns, and more. Understanding these tendencies can help CISOs navigate boardroom discussions more effectively.

Here are examples of the most common biases that can influence cybersecurity conversations at the board level:

• Complexity Aversion Bias: As we mentioned above, board members may avoid engaging with complex cybersecurity issues due to a lack of understanding, leading to oversimplified solutions that fail to address the root causes of security challenges. This bias can result in inadequate security measures and increased vulnerability to sophisticated cyber threats.
• Loss Aversion Bias: The tendency to prefer avoiding losses over acquiring equivalent gains can lead boards to adopt overly conservative cybersecurity strategies, potentially hindering necessary investments in innovative security solutions. This bias emphasizes the fear of potential losses, which can prevent taking calculated risks essential for robust cybersecurity postures.
• Groupthink: The desire for harmony and conformity within the board can suppress dissenting opinions, leading to unchallenged assumptions about cybersecurity risks and a lack of critical evaluation of security strategies. This phenomenon can result in overlooked vulnerabilities and inadequate preparedness for cyber incidents.
• Ambiguity Aversion (Ellsberg Paradox): Boards may favor decisions with known probabilities over those with uncertain outcomes, even if the latter could lead to better security results. This aversion to ambiguity can limit the exploration of innovative cybersecurity approaches that carry uncertain but potentially significant benefits.
• Bikeshedding (Law of Triviality): Boards might spend disproportionate time on trivial cybersecurity issues that they understand better, neglecting more critical, complex matters that require their attention. This focus on minor details can divert resources from addressing significant security threats and processes.

 Typical Personas and How They View Security

As we’ve seen, boards consist of members with diverse backgrounds. Each of these professionals not only brings specific expertise but also distinct perspectives on cybersecurity, that CISOs must learn to recognize and address.

Successfully communicating with your board requires more than generic business language—it demands tailored messaging that resonates with each member’s professional lens and priorities. By identifying these common board personas and understanding what drives their decision-making, you can maximize the chance to impact their decisions and understand the value of your work.

Here are some key common board personalities you’ll encounter, and what drives their decision-making:

The CFO (Financial Expert):  

• Focuses on: Cost and financial implications.
• Wants to know: How this investment prevents financial losses? Will it improve operational efficiency? Why should we prioritize this over other business initiatives?
• Frame security as: A financial safeguard and risk-reduction investment with ROI that exceeds the opportunity cost of alternative investments.

The Former Entrepreneur (Growth-focused):

•  Security risks can derail growth trajectories and damage hard-won market positioning. This persona needs to see security as a deal enabler.
• Frame security as: A value protector that prevents disruptions to growth momentum and preserves the company’s market position and valuation.

The Private Equity Representative:

• Prioritizes investment returns and company valuation.
• Wants to know: How does this security investment protect or increase the value of their investment? Will it improve exit multiples or prevent value destruction?
• Frame security as: A value preservation mechanism that protects the investment from catastrophic risks and maintains the planned growth and exit trajectory.

The Cybersecurity Expert:

• Wants validation that the right technical measures are in place but doesn’t need a deep dive.
• More interested in governance, oversight, and risk management frameworks.
• Frame security as: A strategic program aligned with industry best practices. Demonstrate your point with commonalities and parallels to what they or other respected CISOs did elsewhere.

The Compliance & Audit Specialist:

• Concernedabout regulatory alignment, liability reduction, and avoiding fines.
• Will emphasize compliance-driven security needs, particularly in light of SEC cybersecurity disclosure rules that require timely and accurate incident reporting.
• Frame security as: A compliance necessity that mitigates regulatory risk.

The CEO (Former CRO, Sales-Focused):

• Needs to know how security enhances customer trust, brand reputation, and business continuity.
• Frame security as: A business enabler that strengthens brand and market position.

The COO (Operations-Focused):

• Focuses on resilience and uptime.
• Might ask whether security measures will slow down operations or create inefficiencies.
• Frame security as: A safeguard that ensures operational continuity without disruption.

Actionable Tip: Map the composition of your board and research the board members’ backgrounds, priorities and potential biases. Tailor your security pitch to align with their concerns, ensuring engagement and strategic buy-in. The more relevant and digestible your message, the more likely it is to resonate.

Tailor the Message to the Moment

Context matters hugely in board communications. Just as different board members require different messaging, different scenarios demand different framing. Security reports should never be one-size-fits-all. Let’s look at how to handle a few common boardroom scenarios:

How to Communicate in Key Scenarios

Asking for a Budget Increase?

• Emphasize ROI, cost savings, and competitive advantage.
• Don’t say: “We need $1M for new security testing tools.”
• Do say: “A $1M investment will reduce the risk of API-related data leaks, which cost enterprises an average of $4.35M per breach.”

Providing a Security Overview?

• Focus on business impact, industry trends, and regulatory risk.
• Don’t say: “Our new framework follows OWASP ASVS and ISO 27001 controls.”
• Do say: “By integrating security into the development pipeline, we ensure compliance with relevant regulatory demands and reduce exploitable vulnerabilities by 50%, keeping our applications secure and compliant.”

Promoting a Strategic Initiative?

• Focus on business impact, industry trends, and regulatory risk.
• Don’t say: “Our new framework follows OWASP ASVS and ISO 27001 controls.”
• Do say: “By integrating security into the development pipeline, we ensure compliance with relevant regulatory demands and reduce exploitable vulnerabilities by 50%, keeping our applications secure and compliant.”

Discussing Emerging Threats?

• Emphasize peer comparison, financial impact, and actionable intelligence.
• Don’t say: “The threat landscape is evolving with new attack vectors targeting our industry.”
• Do say: “Three of our competitors faced supply chain attacks last quarter with average recovery costs of $2.8M. Here’s our exposure to similar threats and what we’re doing to protect ourselves.”

Responding to an Incident?

Be direct. Explain what happened, the immediate impact, and how the company is mitigating risk.

• Don’t say:“We detected anomalous activity in our production environment.”
• Do say:“An insecure third-party dependency in our e-commerce application allowed unauthorized access. We patched it within six hours, preventing data theft. The implications are…”

Actionable Tip: Develop a playbook for different boardroom scenarios. Practice framing security insights in business terms. For reference on structuring effective response playbooks, you can review the Federal Government Cybersecurity Incident and Vulnerability Response Playbooks

Use Financial and Business Terms Instead of Security Jargon

In a recent Checkmarx survey of over 200 CISOs, only 25% report beyond vulnerability metrics to address application and business risks. This critical communication gap leaves board members unable to connect security investments with business outcomes.

To bridge the communication gap with your board, focus on quantifying security in business terms wherever possible. Here are some examples of terminology and business metrics you can consider using:

• Industry Benchmarks: “Similar breaches in our industry cost an average of $4.2M”
• Comparative Analysis: “This control addresses the vulnerability that led to our competitor’s breach last quarter”
• Operational Impact: “This security feature reduces friction in our customer verification process by 30%”
• Compliance Requirements: “Implementing this control satisfies requirements for the financial sector RFPs we’re pursuing”
• Relative Risk Reduction: “This initiative addresses our highest-priority risk area, which threatens 15% of our revenue”
• Customer Expectations: “Our top five enterprise customers now require this certification in their contracts”
• Efficiency Metrics: “This automation reduces manual security reviews, freeing 20% of our security team’s capacity”
• Project De-risking: “This approach reduces security-related delays in our product roadmap by an estimated 30%”

Actionable Tip: Build cross-functional partnerships to strengthen your business case. Work with finance to estimate potential breach costs, sales to identify security-driven opportunities, legal to quantify compliance risks, and product teams to measure security’s impact on development velocity. These partnerships not only improve your board communications but also integrate security more deeply into business operations.

How APMA Can Help CISOs Prepare

A strong security strategy begins with understanding where your organization stands today and identifying areas for improvement. That’s where the Application Security Program Methodology & Assessment (APMA) comes in.

What is APMA?

APMA is a structured framework developed by Checkmarx to help CISOs assess, benchmark, and enhance their application security maturity. It provides actionable steps to align security initiatives with business goals and industry best practices.

3 Ways APMA Supports Board-Level Communication

1. Clarity in Risk Reporting – APMA helps CISOs present clear, structured security insights to the board, ensuring risks and priorities are framed in business terms.
2. Strategic Roadmap for Improvement – With APMA, CISOs can outline a clear maturity journey that shows tangible progress over time. This is a useful framework to present to the board.
3. Data-Driven Decision Making – The assessment generates measurable insights, enabling CISOs to support conversations and reporting with strong, data-backed arguments.

Actionable Tip: Take the complimentary APMA digital assessment to evaluate your current security maturity and identify areas for improvement. Start your assessment now.

Final Thoughts: Cybersecurity Needs a Bilingual CISO

The most effective security leaders aren’t just technical experts—they’re business translators who can move fluidly between security concepts and board priorities.

The boardroom is where funding decisions are made, strategic initiatives are prioritized, and risk tolerance is set. If CISOs aren’t part of those conversations, both their organizations and their careers remain vulnerable.

Speaking ‘Boardish’ fluently isn’t just about getting budget approval for the next security tool. It’s about elevating security to a strategic business function with a seat at the decision-making table.

In the boardroom, the clarity of your communication is just as important as the quality of your security program. Even the most sophisticated security strategy will hit a brick wall if you can’t get the board to understand, value, and get behind it.

Your challenge is clear: Learn to speak the board’s language, so your message doesn’t get lost in translation.

You’re juggling budget constraints, regulatory demands, and an ever-growing attack surface. Your application security stack is a patchwork of tools that don’t integrate, while developers push code faster than security can keep up, and that’s without talking about the network and data security tools that you are responsible for.

Traditional approaches to application security—where CISOs have complete control over the budget and get into the nitty-gritty details of which security tools are being used—is making way to a new approach, where developers have a decisive say in AppSec tool selection. And it does have a sound logic behind it. After all, developers are the ones who must integrate these tools in their workflow, balancing between agile development and continuous delivery, and fixing vulnerabilities.

However, since the security buck stops with CISOs, it’s up to them to establish a new security model where CISOs actively enable AppSec teams and development teams to work together, to fix vulnerabilities effectively without slowing business velocity.

The solution lies in a unified, proactive security strategy that stays ahead of threats without impeding development velocity. But achieving this balance requires a fundamental shift in how CISOs approach application security

The AppSec Landscape is Changing and CISOs Must Evolve

In today’s security landscape traditional command-and-control approaches are becoming less effective. The democratization of technology in organizations is shifting security budgets and tooling decisions from security leaders to the teams who engage with the tools the most. Given the rapid pace of modern application development, development teams increasingly influence tool selection. As with any major shift, it does not happen without challenges. To navigate this new reality, CISOs must evolve from tool purchasers to strategic leaders who enable secure development at scale. This evolution centers on three critical pillars:

• Eliminating Guesswork in Risk Prioritization – Helping dev teams know what needs to be fixed first by identifying and focusing on the most critical vulnerabilities, to mitigate risks effectively
• Let Your Devs Work – Enabling developers to integrate security into the development process to improve both productivity and security outcomes.
• Make It Work Together – Reducing complexity, improving visibility, and lowering operational costs by streamlining and consolidating all security tools into one platform.

Let’s dive deeper into each of these pillars.

Pillar #1: End the Guesswork – Know What to Fix First

Security and development teams often face an overwhelming volume of vulnerabilities. Without proper prioritization, time and resources are wasted on low-risk issues while critical threats remain unaddressed.

Without proper context and prioritization, security teams waste precious time investigating low-risk issues while critical vulnerabilities potentially go unaddressed. Development teams, in turn, waste time addressing non-issues, which slow down their workflow. In many cases, the sheer volume of alerts and security fatigue can backfire, creating a higher risk—developers may ignore vulnerabilities to stay on track and meet deadlines, inadvertently increasing exposure.

How CISOs Can Stay Ahead

To cut through this noise, CISOs need to provide their organizations with:

• Faster, more actionable insights into their application security landscape: This means moving beyond simple vulnerability scanning to understand the real-world impact of security findings.
• Contextual prioritization: True risk prioritization that considers factors like exploitability, exposure to the internet, and business impact. Not all vulnerabilities are created equal, and security teams need tools that help them focus on what matters most.
• Scan-depth flexibility: The ability to go deep or wide, depending on the circumstances – a fast, high-level scan that highlights a few pressing issues, or a deep-dive that goes in depth and provides a more thorough and detailed picture of the security status.
• Reporting automation: Automated compliance reporting and clear audit trails that make it easy to demonstrate security posture to stakeholders and auditors.

An integrated security platform enables security teams to consolidate risk visibility, maintain audit readiness, and ensure compliance without overwhelming developers with excessive security alerts.

How Checkmarx Helps

Rather than presenting AppSec practitioners with a flood of disconnected alerts, Checkmarx application security platform provides the context and clarity needed to make informed security decisions:

• Risk Correlation – Integrates security data from multiple tools to identify and prioritize exploitable vulnerabilities.
• Comprehensive Visibility – Provides a holistic view of an organization’s application security posture, ensuring informed decision-making.
• Correlation – Integrates security findings across multiple testing tools and correlates them to identify true areas of risk.
• Exploitable Path – Shows exactly how attackers could exploit weaknesses in the code. This capability traces the complete attack path from source to sink, helping developers understand not just what’s vulnerable, but why it matters and how to fix it.
• Compliance Readiness – Automated reporting and compliance dashboards, streamlining audits and security assessments.
• Flexible Scanning – Organizations can choose between rapid scans for quick feedback during development and comprehensive scans for deeper security analysis.
• Presets – Pre-configured security rules. Organizations can choose what to look for and tailor their security scanning to match their specific needs and risk tolerance

Pillar #2: Let Your Developers Work – Make Security Seamless

When security tools operate in isolation from development workflows, they create friction that slows down delivery and decreases security adoption. Vulnerabilities go unfixed, and security alerts are seen as a nuisance, rather than as an integral part of the workflow.

How CISOs Can Stay Ahead

The key to changing this perception among developers lies in making security as seamless and intuitive as possible for them.

This means:

• Integration with existing tools and workflows: Security checks should run within the IDE and CI/CD pipeline, providing immediate feedback without requiring developers to change their workflow, context switch or learn new tools.
• Real-time guidance and feedback: Developers need clear, live, and actionable information about security issues as they code.
• Automated remediation support: When issues are found, developers should receive clear guidance on how to fix them, ideally with automated remediation options where possible.

How Checkmarx Helps

Checkmarx provides developers a seamless experience, allowing them to address vulnerabilities without distracting them from dev work:

• Seamless Integration: Security is embedded directly into the tools developers already use. Bug trackers, IDEs, CI/CD tools, SCM integrations are your developers’ natural environment.
• DevOps Policy Management: Break builds if security policies are violated. Integrate directly into the CI/CD process and have security policies automatically enforced.
• AI-Powered Coding Assistant: Provides instant security feedback during coding, helping developers remediate issues in real-time.
• Guided and Auto-remediation: Remediate vulnerabilities at a click of a button. No need for developers to be security experts. Easier to fix vulnerabilities means more vulnerabilities are fixed.
• Developer Enablement: Guided remediation and training ensure that security adoption is frictionless and efficient.

Pillar #3: Make It Work Together – Create a Unified AppSec Strategy

Tool sprawl is more than an inconvenience; it’s a security risk. When organizations rely on multiple disconnected security tools, they create blind spots, increase management overhead, and drive up costs. Tool sprawl doesn’t allow synergies. Additionally, tool sprawl is a management challenge, overwhelming CISOs with too many vendors and budget concerns to manage.

How CISOs Can Stay Ahead

A unified approach to application security is essential for modern organizations.

This unification should deliver:

• Improved security coverage: Correlation and prioritization across all application types, security testing methods, and dev stages allows for more cohesive security coverage.
• Centralized visibility: Allows for more control and a better overview of the total security posture through unified dashboards and reporting.
• Better collaboration: AppSec and dev teams can collaborate more efficiently and reduce frictions through shared processes.
• Reduced total cost of ownership: Tool consolidation and automated workflows reduce the overall cost of ownership across all teams and functions.

How Checkmarx Helps

Checkmarx provides a comprehensive security platform that enables multiple teams to collaborate efficiently throughout the SDLC, across multiple pipelines.

• Multiple tools in one Platform: Checkmarx One combines SAST, DAST, API Security, Container Security, IaC Security, and more all on one platform, providing a single pane of glass.
• Built-in ASPM Dashboards: Unify security findings to improve risk prioritization.

Leveraging APMA for Strategic Application Security

CISOs need to create an application security strategy.

To create the strategy, you need to know where you currently stand, what gaps remain, and how to fix them.

To assist organizations in measuring and enhancing their security posture, Checkmarx developed the Application Security Program Maturity Assessment (APMA) framework. APMA provides a structured methodology for evaluating AppSec strategies, identifying gaps, and implementing improvements. It focuses on five key dimensions:

1. Strategy and Governance: Aligning high-level security goals, objectives, and policies, typically under CISO’s purview.
2. Security Testing (Tactical): Examining AppSec program processes, often managed by the head of AppSec.
3. Security Testing (Operational): Assessing required tools and their utilization, usually the responsibility of the head of application development in collaboration with AppSec management.
4. Security Testing (Architecture and Scale): Evaluating the infrastructure needed for security testing, primarily handled by the IT/infrastructure manager.
5. Planning: Breaking down security initiatives into work packages, timelines, and resources, typically managed by project, program, or delivery managers.

APMA has been leveraged in over 300 security assessments across 200+ organizations, with an additional 600 self-assessments conducted using APMA Digital.

A real-world example of APMA’s impact is Cdiscount, one of the largest e-commerce companies in Europe. Cdiscount faced growing vulnerabilities and fragmented security processes. By leveraging APMA, they gained a clearer view of their security maturity, streamlined risk management, and aligned their teams under a unified AppSec strategy. The result was a significant reduction in security friction and improved risk visibility.

Conclusion: When Everything Clicks into Place

A modern approach to application security enables CISOs to achieve true alignment between security and development teams. By prioritizing the most critical vulnerabilities, integrating security into developer workflows, and consolidating security tools, CISOs can finally get ahead of application risk without slowing down innovation.

Ready to Get Ahead of Application Risk?

With Checkmarx, CISOs gain complete visibility into security risks, enable developers to fix vulnerabilities in real-time, and maintain control over security across cloud and legacy applications. Unifying your AppSec on Checkmarx One provides a 177% ROI, according to analysis conducted as part of the Forrester Total Economic Impact report.

Checkmarx enables security leaders to achieve this transformation, ensuring organizations are always ready to run—without compromising on security or development speed. The result is a security program that enables innovation while maintaining robust protection against evolving threats.

Request a demo today and see what it’s like to be Always Ready to Run.

It seems today that adapting to – and embracing – AI in AppSec is no longer optional. It’s true that AI is no longer a futuristic fantasy. It’s a vibrant reality shaking up every aspect of development and cybersecurity, but there is a tempest coming with both promise and peril: AI-generated code. 

Those fancy new GenAI tools your developers have fallen head-over-heels for are rapidly becoming indispensable for productivity, but let’s get real: They’re also becoming an increasingly juicy target for attackers.

Let’s get back to the question at hand, though. If you ask me, then the answer is AI won’t, and can’t, replace expert level engineers and analysts anytime soon, but that doesn’t mean it isn’t already leveling them up. Let’s dive into what that means and how we can secure the entire AI-driven development lifecycle.

AI Code: Friend or Foe?

Here are the facts: Developers are embracing AI-generated code because it boosts productivity. These Large Language Models (LLMs) excel at writing impressive snippets of code, completing boilerplate tasks, refactoring and improving existing codebases, debugging and identifying errors, and making documentation slightly less soul-draining.

But there’s a catch: LLMs weren’t exactly schooled in secure coding best practices. That slick-looking code snippet? It might just be the cybersecurity equivalent of a beautiful yet structurally unsound bridge. It looks fantastic, but it could collapse spectacularly under attack. 

AI also reflects biases and mistakes inherent in training data (public repositories), potentially propagating outdated, inefficient, or insecure practices. It also has a potential to hallucinate – make things up, including even inventing non-existent libraries.  Unclear intellectual property and licensing risks stemming from code generation using proprietary or GPL-licensed open-source repositories also poses legal and licensing risks.

This may be AI 101, but developers can’t implicitly trust AI-generated code as sound and secure. That trust, without verification, is an open invitation to trouble. They need review, and human eyes are still non-negotiable.

Navigating AI Security Risks in AppSec

Generative AI introduces new attack vectors targeting not only your apps but also your AI tools themselves. Attackers can exploit vulnerabilities inherent in the AI ecosystem, poisoning training data or tricking AI code generators into spewing insecure or malicious code. The more dependent your workflows become on AI, the more urgent your need to secure those AI processes.

Yet, halting AI adoption altogether isn’t practical. Governance challenges arise, and executives see AI as essential for productivity. Fighting adoption is pointless. Instead, let’s set guardrails to encourage responsible use, train your developers to scrutinize AI-generated outputs, and, most importantly, integrate security scanning directly into your developer workflows.

Embracing AI in Application Security

AI poses risks, but it also plays a pivotal role in modern AppSec. The very technology creating vulnerabilities also equips us with potent new AI security tools to counteract threats.

Consider the power of AI secure coding assistants. By integrating intelligent scanning directly within the IDE, these tools identify vulnerabilities as code is written, before risky code even hits the repository. Real-time feedback on AI-generated and manually written code gives developers immediate insights, empowering them to fix vulnerabilities instantly. This approach shifts security left dramatically, catching mistakes long before they escalate into costly disasters in production.

Moreover, there’s AI query building, an unsung hero that leverages generative AI to construct and enhance custom security queries to enhance fidelity. Whether it’s Static Application Security Testing (SAST) or Infrastructure-as-Code (IaC), AI-powered query tools dramatically accelerate AppSec workflows. Developers and security analysts alike can write targeted, precise queries without spending hours buried in documentation, boosting efficiency and coverage in equal measure.

However, if you are going to use AI in AppSec, it is best used as an integral part of the software development lifecycle (SDLC) rather than just a tacked-on tool because of the need for unified risk visibility across the entire SDLC. Simply put, the stakes are too high to leave any stone unturned when evaluating AI. With a holistic approach to AI across multiple AppSec domains, there are fewer places for blindspots to hide.

Intelligent Remediation and AI Security Champions

Perhaps the most exciting area where AI shines in cybersecurity is remediation. Manual remediation is time consuming, inefficient, and slows down the whole development cycle. Developers dread it, AppSec teams hate nagging about it, and vulnerabilities linger far longer than they should. Unfortunately – and fortunately – it’s a necessary part of the process.

Enter AI-assisted remediation. Imagine not only pinpointing a security flaw but instantly receiving actionable, tailored suggestions for fixing it. AI remediation tools do exactly that: They analyze vulnerability findings from your SAST or IaC security scans and generate ready-to-use code snippets tailored to your specific issues. It’s like having an AppSec expert embedded within each developer’s IDE. There’s no need to worry about the AI making changes behind your back, either. AI remediation should never auto-commit changes. Rather, it provides suggested fixes that developers review and apply.

In practice, teams drastically cut their vulnerability backlog, improving security posture at unprecedented speed. Developers, armed with immediate solutions, actually grow their security awareness by seeing detailed explanations of the issues alongside code fixes. It’s security training disguised as productivity, exactly how AppSec pros like it.

Governance and Culture: Your Best AI Security Tools

While AI technology transforms cybersecurity tools and practices, it’s your culture and governance approach that ultimately determine success. AI is indeed a powerful partner, but it won’t replace cybersecurity teams. Instead, AI is just another tool that, when used correctly, enhances the team’s productivity. It’s always scanning, learning, and offering insights, but it still requires seasoned oversight.

The human role in AppSec isn’t diminished by AI; rather, it’s elevated. The job now involves orchestrating AI tools effectively, training teams, and evolving security programs to keep pace. Establish clear guidelines for AI use, embed AI security capabilities into your AppSec strategy, and ensure ongoing assessment and adjustment.

Incorporating AI in your AppSec program also requires maturity in governance. Carefully outline how your organization adopts and secures AI, from code generation to deployment. Define rules around which AI tools are acceptable, how outputs are vetted, and which security scanning mechanisms must accompany AI-generated code. Governance, when thoughtfully implemented, turns AI from potential liability into tangible strategic advantage.

Final Thoughts: Your AI-Enhanced Security Future

AI won’t replace your cybersecurity team, but it will reshape your workflows profoundly. Your task isn’t to fear AI but to harness it strategically. Deploy intelligent scanning, automate remediation with AI assistance, and establish clear governance around AI-generated code. Embrace AI confidently, understanding both its risks and rewards.

From code generation through testing and deployment, integrating AI responsibly creates safer, more efficient, and even more enjoyable AppSec experiences. Embrace it wisely, guide it carefully, and you’ll find your application security team not replaced, but transformed into a supercharged, AI-enhanced security powerhouse.If you’re sold on the value of AI in AppSec and how it can enhance your team, try checking out Checkmarx AI Security.