How to tune semantic rules for framework code
A developer guide to modeling request sources, sanitizers, encoders, and sinks for internal frameworks.
Research articles
Benchmarks and field notes for AI code security.
Trust infrastructure for code built by AI agents, with practical articles on semantic SAST, AI verification, Circle-IR, and production guardrails.
Latest
AI verification and benchmark articles
Use these posts to attract developers comparing SAST tools, validating AI-generated pull requests, and building guardrails for high-volume agentic coding.
LLM guideComing next Coming next
Cognium-AI LLM enrichment model guide
Compare local Ollama models, GitHub Models, and OpenAI-compatible endpoints for context-aware security scan enrichment.
AI trustAI-generated code needs AI-first verification
Why human review alone is not enough for AI-generated pull requests, and how guardrails make verification scalable and less subjective.
Circle-IRWhy Circle-IR matters for AI code verification
How an interpretive intermediate language gives scanners and AI verifiers a shared semantic layer for source, sink, policy, and intent checks.
BenchmarkSemantic SAST + LLM verification on CWE-Bench-Java
How Cognium frames SAST-only detection, LLM-assisted verification, and CodeQL baseline comparison for AI-generated code review.
Agent trust evidence in pull requests
How AI trust scores, SAST output, and skills registry evidence can become a review decision.
Developers: visit cognium.dev for open-source tools.
Use the open-source scanner, then connect one staging repository when you are ready to verify AI-generated pull requests in CI.