AI Code Insights Brings X-Ray Vision to Code Repositories, Turning Developer Chaos Into Security Clarity

In a time when DevSecOps moves at warp speed, security teams are often left deciphering cryptic alerts and chasing vulnerabilities across vast, opaque codebases. ArmorCode thinks it has a solution—and it’s betting big on AI to bring it.

Today, the application security posture management (ASPM) provider launched AI Code Insights, a new capability designed to demystify code repositories using ArmorCode’s agentic AI, Anya. The launch builds on ArmorCode’s mission to unify security, development, and compliance around what it calls "contextual risk intelligence"—and it aims to tackle the elephant in the room: code repositories as black boxes filled with unprioritized findings and incomplete asset inventories.

“While development velocity has skyrocketed, security teams are often flying blind, buried in alerts without understanding the actual risk lurking within their code repositories,” said Mark Lambert, Chief Product Officer at ArmorCode.

AI Code Insights attempts to flip that script. Instead of throwing more alerts at already overwhelmed security teams, the platform adds intelligence and traceability to what’s already being collected. It links vulnerabilities to developers, surfaces hidden assets like APIs or containers not caught in traditional scans, and identifies compliance-impacting code changes before they trigger an audit nightmare.

Cracking Open the Black Box

One of the platform’s most practical capabilities is cloud-to-code correlation, mapping runtime alerts from platforms like Microsoft Defender and CrowdStrike Falcon all the way back to the specific infrastructure-as-code file or developer who owns the problem. That direct mapping dramatically reduces the manual effort needed to trace security issues across toolchains—and more importantly, gets the fix into the right hands faster.

The platform also emphasizes proactive risk management for AI frameworks—automatically flagging where AI code is embedded in applications, correlating those components with scanner findings, and recommending prioritized fixes. It’s a timely feature given the rise of "shadow AI" in enterprise software pipelines, where developers may include generative or machine learning components without proper oversight.

Redefining ASPM with Context

AI Code Insights isn’t just a bolt-on feature. It represents a deeper shift in how ASPM platforms are expected to function—moving from detection to decision-making.

ArmorCode enriches findings with context that’s often missing: what programming languages are in play, where sensitive data lives, how cryptography is implemented, and who touched what. That information is used to feed workflows for remediation prioritization, compliance automation, and asset visibility, helping teams surface and secure critical components before they make it into production.

“We’re providing the crucial context – the ‘what, who and how’ – behind the code and vulnerability,” Lambert explained. “This allows organizations to finally cut through the noise, prioritize effectively, and proactively secure their most critical assets before they become liabilities.”

Built to Work With What You Already Have

Unlike many security tools that demand new integrations or force their own dashboards, AI Code Insights takes a plug-and-play approach. It pulls from existing developer platforms like GitHub and integrates with more than 285 tools across the security ecosystem. The pitch? Don't replace your stack—just make it smarter.

With over 175,000 practitioners already using ArmorCode, the company is banking on its AI-led, context-first approach to win over security leaders frustrated by alert fatigue and limited visibility. If it succeeds, AI Code Insights could shift the ASPM conversation from detection to strategic risk management—one developer commit at a time.