01
Flow-first detection
Track attacker-controlled input across functions, object fields, collections, templates, and sinks.
Semantic SAST scanning
SAST scanning built for AI-generated code.
Cognium traces generated code paths with semantic SAST, then gives reviewers production-style proof: vulnerabilities found, spec checks verified, and noisy alerts reduced.
We read the code. We know what it does.
We read the code. We know what it does.
Find vulnerabilities across dependencies, code patterns, and injection risks. Cognium scans everything in parallel and returns proof reviewers can trust.
02
Framework-aware scanning
Model HTTP handlers, request objects, database APIs, encoders, and sanitizers used by real services.
03
Actionable output
Produce findings that explain source, path, sink, severity, and the safest remediation path.
Implementation
Where Cognium fits in the SDLC
Use Cognium locally, in staging CI, or as a merge gate for repositories where agents write production code.
Developer scan
Run the open-source scanner locally or in a branch workflow.
Pull request verification
Attach SARIF, risk summaries, and trust scores to every AI-generated PR.
Production gate
Block exploitable flows while letting clean changes move through review faster.
Comparison
How Cognium changes the workflow.
These pages are built for teams evaluating AI coding security, agent trust, and enterprise governance beyond basic scanner checklists.
| Current approach | Typical gap | Cognium approach |
|---|---|---|
| Legacy SAST | Large rule sets with noisy pattern matching. | Semantic flow tracking tuned for generated code. |
| Code review | Human reviewer hunts through generated diffs. | Reviewer starts with verified data-flow evidence. |
| CI checks | Pass/fail without business context. | Findings become part of a trust score and policy decision. |
Every PR scanned. Every vulnerability caught.
Every PR scanned. Every vulnerability caught. Every spec requirement verified. Your reviewers start with a clean slate.