Skip to main content

Agentic AI for Defense: How Checkmarx Turns Security into a Coding Partner

“AI-powered” has become the default label for every security tool on the market. But there’s a meaningful difference between a tool that uses AI to generate alerts after the fact and one that actively participates in development, preventing vulnerabilities as code is written.

That difference is what separates reactive AI from agentic AI. And it matters more now than ever.

What “Agentic” Actually Means in AppSec

In the context of application security, agentic AI isn’t a buzzword. It describes a specific set of capabilities: the tool proactively surfaces security issues in real time, understands the context in which code is being written, and recommends fixes before insecure patterns reach the pipeline. The developer still makes the call. But instead of finding out about a vulnerability hours or days after committing it, they get guidance at the moment they can act on it most efficiently.

Three qualities define the approach. Agentic AI is proactive, performing inline validation as developers write rather than waiting for a post-commit scan. It’s context-aware, understanding the intent behind a code pattern rather than just matching syntax rules. And it’s assistive, offering guided remediation and recommended fixes that developers can review, accept, or modify, keeping decision-making authority where it belongs.

Most tools on the market today check one of those boxes, maybe two. Checking all three is what makes the approach genuinely agentic.

What This Looks Like in Practice

An agentic approach only works if it reaches every layer of the development lifecycle: the individual developer writing code, the organization setting policy, and the leadership team measuring outcomes. Gaps between those layers are where risk accumulates.

Checkmarx built its Checkmarx One Assist platform around that principle, with each layer addressing a distinct challenge.

Developers need remediation guidance without leaving their editor. Developer Assist validates code in real time inside VS Code, JetBrains, Cursor, and Windsurf, including AI-generated completions. When it identifies a vulnerability, it provides guided remediation in-flow rather than routing developers to a separate dashboard. For changes with broader impact, Safe Refactor cascades fixes across affected files and dependencies, ensuring a local fix doesn’t introduce new breakage elsewhere.

Organizations need governance that keeps pace with how code is actually written. Policy Assist lets teams codify security guardrails scoped by repository, language, or role, and those rules are enforced consistently whether a developer is writing code manually or accepting suggestions from an AI assistant. Policies become active participants in the coding process rather than gates that trigger only during a CI/CD run.

Security leaders need to measure what’s working. Insights Assist tracks MTTR, SLA adherence, and risk trends across the portfolio. Instead of vague assurances about security posture, CISOs and their teams can see how quickly vulnerabilities are resolved, where bottlenecks persist, and whether improvements are real or cosmetic.

What makes this agentic rather than just comprehensive is that these layers operate together. The IDE validation, the policy enforcement, and the executive visibility reinforce each other continuously, not as separate products stitched together after the fact. Most AppSec vendors cover one of these layers well. Some cover two. Checkmarx is the only agentic platform that works across all three: IDE, CI/CD, and portfolio, in a single integrated experience.

Eight Questions to Test Whether a Vendor’s AI Is Truly Agentic

Not every tool that claims agentic capabilities delivers them. Here’s a practical framework for separating substance from marketing.

1. Does it act before commit, or only scan after the fact? Agentic AI validates intent and logic in the IDE as code is written. Reactive tools run post-commit scans and hand developers noisy reports long after they’ve moved on.

2. Can it explain its reasoning? Agentic AI provides context-aware, human-readable explanations for why a line is risky. Reactive models flag issues without justification, which erodes developer trust over time.

3. Does it fix, or only find? Agentic platforms generate safe refactors, package blast-radius insights, and guided remediation. Reactive tools stop at highlighting the problem and leave the fix to someone else.

4. Can it enforce policies in real time? Agentic AI applies organization-wide security rules inline, scoped by repo, language, or role. Reactive tools push enforcement downstream into CI/CD, where catching a violation means rolling back work that’s already done.

5. Does it adapt to generative AI-specific threats? Agentic AI detects threats like Lies-in-the-Loop (LITL), prompt injection, shadow AI usage, and poisoned packages. Reactive tools weren’t built for these vectors and miss context-driven exploits.

6. How does it handle shadow AI? Agentic platforms surface unapproved AI usage across teams, scanning completions from tools like Copilot, Claude, or Replit AI. Reactive vendors ignore shadow AI entirely, letting policy drift accumulate unchecked.

7. What’s the measurable impact on MTTR and throughput? Agentic AI reduces mean time to remediate and accelerates release cycles by eliminating rework. Reactive tools often add friction to the process, reintroducing the “slow security” problem they were supposed to solve.

8. Is it embedded everywhere developers work? Agentic AI integrates across IDEs, repositories, CI/CD pipelines, package managers, and SIEM/SOAR platforms. Reactive AI is typically bolted onto a single layer (often just the repo), creating gaps everywhere else.

Put Your Current Vendor to the Test

Run these eight questions against whatever AppSec tools you’re evaluating or already using. The answers will quickly tell you whether you’re looking at a genuinely agentic platform or a reactive tool repackaged under a new label. The distinction will only matter more as AI-generated code becomes the norm rather than the exception.

Download the Agentic AppSec Buyer’s Guide: Download Now →

See what agentic AI looks like in practice. Watch the Checkmarx Assist demo →



from DevOps.com https://ift.tt/fzouQl1
Labels:

Comments

Post a Comment

Popular posts from this blog

Claude Code’s Ultraplan Bridges the Gap Between Planning and Execution

Planning a complex code change is hard enough. Reviewing it in a terminal window shouldn’t make it harder. Anthropic is addressing that friction with a new capability called Ultraplan, currently in research preview as part of Claude Code. The feature moves the planning phase of a coding task from your local terminal to the cloud — and gives developers a richer environment to review, revise, and approve a plan before a single line of code changes. It’s a small workflow shift with real practical value, especially for teams working on large-scale migrations, service refactoring, or anything that requires careful coordination before execution begins. How it Works Ultraplan connects Claude Code’s command-line interface (CLI) to a cloud-based session running in plan mode. When a developer triggers it — either by running /ultraplan followed by a prompt, typing the word “ultraplan” anywhere in a standard prompt, or choosing to refine an existing local plan in the cloud — Claude picks u...
Post a Comment
Read more

Security as Code is Becoming the New Baseline: Continuous Compliance in DevOps 

There was a time when compliance meant a quarterly ritual. Someone from security would walk over with a spreadsheet, ask a few questions, tick a few boxes and disappear until the next audit cycle. The infrastructure team would scramble to prove that yes, encryption was enabled, and no, that S3 bucket was not public anymore. Everyone felt relieved, went back to shipping features and quietly hoped nothing would drift before the next review.   That model is dead; it just hasn’t been buried yet.   The problem is not that teams lack security awareness. Most engineering organizations today understand that vulnerabilities need catching early and that production environments need hardening. The problem is that compliance has historically lived outside the delivery pipeline — treated as a checkpoint rather than a continuous practice. In a world where teams deploy dozens of...
Post a Comment
Read more

Java 26 Arrives With AI Integration and a New Ecosystem Portfolio — What It Means for DevOps Teams

Oracle released Java 26 on March 17, 2026, and while every six-month release comes with its own set of improvements, this one carries a broader message: Java isn’t just keeping pace with the AI era — it’s actively positioning itself as the infrastructure layer where AI workloads will run. For DevOps teams managing large Java estates, that’s worth paying attention to. The Scale of What You’re Already Running Before getting into what’s new, it helps to remember what’s already in place. According to a 2025 VDC study, Java is the number one language for overall enterprise use and for cloud-native deployments. There are 73 billion active JVMs running today, with 51 billion of those in the cloud. That scale matters when you’re thinking about where AI fits in. Most of the systems where agentic AI will eventually operate — transactional platforms, backend services, data pipelines — are already running on Java. The question for DevOps teams isn’t whether to adopt Java for AI. It’s how to ...
Post a Comment
Read more