AI Code Security Review

AI code security review is the structured security assessment of code produced with AI coding tools, autonomous agents, or automated development workflows. It covers familiar software risks such as injection flaws, broken authentication, insecure dependencies, and unsafe configuration, but adds risks that are specific to AI-assisted delivery. Reviewers look for hallucinated APIs, missing error paths, weak tests, excessive permissions, prompt-injection exposure, secret leakage, uncontrolled network access, and assumptions the model introduced without evidence. A strong review combines static analysis, dependency scanning, runtime checks, human architecture review, and often a second agent that independently revalidates proposed fixes. The important shift is repeatability: teams need clear merge gates, reproducible test commands, traceable findings, and documented decisions rather than a one-off gut check. AI code security review therefore becomes the operating layer between fast AI-generated implementation and production-grade software. It should happen continuously during development, not only before release, because AI can scale both useful code and hidden security debt at the same time.