AI code security risk: The need for a smarter layer between detection and remediation

Date:
Fri, 22 May 2026 11:00:29 +0000

Description:
AI is accelerating code and risk. Security needs smarter triage beyond detection to remediation.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter AI has dramatically increased the speed and volume of software development. In a recent Google survey, 90% of developers reported using AI tools to assist them in their work, with 71% using it to write code.

One company told the New York Times that after adopting Cursor, an AI-native code-writing product, they went from producing 25,000 lines of code a month
to 250,000, creating an enormous backlog of lines that needed to be reviewed by their team. Harshit Agarwal Social Links Navigation

CEO and co-founder of Appknox. While these tools have accelerated software delivery, theyve introduced more risk. One study finds that 45% of AI-generated code contains security vulnerabilities, and AI-generated pull requests contain 1.7x more issues on average than those written by humans. Latest Videos From You may like Beyond the hype: The critical role of
security in responsible AI development AI tools have made vulnerability exploitation faster and easier From fragmentation to flow: Rethinking modern software development

Detection isnt the challenge. Modern security tooling can identify the problems, generating more findings and vulnerabilities than ever before. The problem for most security and engineering teams is what happens next.

With the sheer volume of AI-generated code flowing in, security teams cant keep pace. Theyre struggling to discern which issues pose a genuine risk. Because static severity levels treat every flagged issue equally, triaging gets complicated.

Siloed and disconnected security tools slow remediation, forcing development teams to context-switch just to assess an issue. And the more vulnerability reports to sift through, the higher the likelihood that real risks will slip through the cracks.

Whats needed is a smarter layer between detection and developmentone that validates findings, identifies whats truly exploitable and delivers fixes developers can act on within their flow of work. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over. Detection is happening. But what comes next? With static analysis, dynamic testing and automated scanning , modern security tools are proficient at flagging vulnerabilities. The more complex problem is what follows: How do security teams determine which vulnerabilities pose a risk, and how do they get fixes to developers before those issues reach production?

Most teams default to severity scores to manage the backlog, but those scores were designed for a different era of software development . They rank vulnerabilities against a standardized rubric instead of the specific architecture, data flows or exposure profile of a given application.

A vulnerability rated "critical" in one context may be completely unreachable in another. When every alert demands urgent attention, nothing does.
Engineers stop acting on scores and start acting on instinct, which is where real risks get missed. What to read next Why software defects are now the biggest security threat Patch window is officially dead as AI finds bugs faster than humans can squash them How AI agents are wrecking havoc in legacy security setups and enterprises are catching up

AI-powered development merely compounds the challenge. More findings, more noise and far greater difficulty separating what matters from what doesn't. And as development accelerates, the window to catch and fix those issues before they reach production keeps shrinking. The smarter layer: Triaging, working in context and taking action Cutting through that noise requires tooling that provides a smarter layer between detection and development, helping teams validate issues, triage them and take action before a problem escalates.

This starts with a few key shifts:

If teams are going to accurately detect and fix vulnerabilities, they need to shift from static to runtime analysis. Heres why:

Static code analysis evaluates code as its written, which means its not analyzing code as it behaves at runtime. Runtime-grounded analysis, on the other hand, can improve detection accuracy and establish a clear link between whats vulnerable and the fix. In effect, prioritizing decisions becomes easier, and teams can begin remediation faster.

The speed of remediation depends on how quickly it can reach developers, delivered in plain language and applied in the environments theyre already working in.

Forcing developers to move out of the AI-native environments theyre working in, like Cursor or Claude Code, to check a separate security dashboard
creates unnecessary friction and slows them down. At scale, that friction becomes delay, and delay is where vulnerabilities survive.

Developers need security tooling that integrates directly into their workflow and behaves more like engineering tools than an entirely separate system scanning for detection, validating exploitability and delivering a fix in context. Where AI fits into a smart security layer Closing the growing gap between the number of vulnerabilities found by detection tools and how development teams respond to them requires several changes. If security tooling is going to keep pace with AI-assisted development, it needs to be part of the solution.

Integrated into the development workflow, AI can help teams validate
problems, triage risks by severity and exploitability and deliver guidance so developers can make fixes in real time. Heres how.

Integrate security tooling into development workflows. Developers need guidance on how to remediate issues in the moment, within the tools they use every day. By investing in security tools that integrate with AI code environments, teams can reduce workflow friction, eliminate context switching and speed up the steps to get to a fix.

Move away from static severity scoring. Static codes eventually get drowned out. A critical scoring wont catch anyones eye, especially if the vulnerability being flagged requires the developer to move to an entirely separate application to assess it (and then back to make the fix).

Instead, teams that make the shift to exploitability-based prioritization ensure their development teams can sift through the noise and address the vulnerabilities that pose real-world risk first.

Validate earlier. Its much more costly to work backward once code has reached production. By catching issues, validating them and fixing them earlier in
the development process, teams get back time and resources and reduce overall risk for their organization.

Organizations shouldnt have to choose between speed and security when
adopting AI-enabled development. They instead need security tools that help them cut through the noise, keep pace with this new speed of production and close the gap between flagged vulnerabilities and what comes next. We've featured the best laptop for programming. This article was produced as part
of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit



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