Enji GuardSAST for AI-generated code that needs repo context
Guard is not just another SAST scanner, it is repo-aware static application security testing for AI-generated code. Generic SAST drowns teams in low-confidence alerts and still misses AI-shaped flaws. Guard reads source, configs, dependency files, and security-sensitive flows with runbooks built around AI-assisted failure modes.
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01
AI-written code fails in its own patterns
AI reproduces the insecure examples it learned from: a secret returned from a health route, an auth check that never runs, input trusted straight into a query. A repo-aware security audit catches what a generic ruleset talks past. AI-generated code security is less about exotic new bug classes than about old ones reappearing where no human would put them.
02
What the static audit finds
Guard reads the source for the security flaws AI introduces most:
- Hardcoded secrets, tokens, and keys
- Broken authentication and missing access control
- Untrusted input flowing into queries, commands, or paths
- Insecure defaults and over-permissive configuration
- Sensitive data exposed through reachable routes
03
Signal over noise
Static application security testing tools catch known patterns. Guard adds repo context and prioritises high-confidence findings in plain language, so security work goes to issues your team can understand and act on.
04
Static plus dynamic, not either-or
Static analysis flags risk in the code; bounded runtime checks add safe web-app evidence where that evidence matters. Guard turns confirmed findings into reviewable issues or pull requests.
Quick questions
SAST and DAST comparison: where does SCA fit?
SAST reads your source, DAST tests the running app, and SCA checks your dependencies. AI-built apps often need all three; Guard can combine static review, dependency analysis, and scoped runtime checks when a linked app is configured.
Will it drown me in false positives like generic SAST?
That is what Guard is designed to avoid. Instead of dumping a ruleset, it uses repo context to prioritize high-confidence findings in plain language, so security work goes to issues your team can actually act on.
Is static analysis enough on its own for AI-generated code?
No. Static review cannot see an endpoint that is unauthenticated in production or a rate limit that never fires. When you link an approved app target, Guard can add bounded runtime checks around those gaps too.
Does it fit into a DevOps workflow?
Yes. It runs on a schedule, ties findings to your GitHub repo as issues or pull requests, and connects through a revocable GitHub App rather than a separate dashboard.
Run static analysis built for AI code.
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