01
Intent verification
Compare generated implementation against the ticket, spec, or expected behavior.
AI trust and verification
Verify AI-generated code before humans review it.
Every AI-generated PR, verified. Cognium checks intent, vulnerabilities, policy, and audit evidence so reviewers start from production-grade proof.
Every PR scanned. Every vulnerability caught. Every spec requirement verified.
Every PR scanned. Every vulnerability caught. Every spec requirement verified.
AI trust is not a model score. It is a release decision backed by evidence about what changed, why it changed, whether it is safe, and whether it matches the spec and policy for that repository.
02
Security verification
Use SAST and LLM-assisted review to confirm whether a reported path is exploitable.
03
Decision evidence
Attach trust scores, findings, policy results, and reviewer context to the PR.
Implementation
Verification outputs your teams can use
Cognium gives engineering, security, and compliance teams a shared record instead of scattered PR comments.
Trust score
Summarize risk from code, dependencies, tools, skills, and policy.
Spec drift report
Show missing, extra, or ambiguous behavior introduced by the agent.
Audit trail
Export the evidence behind approve, review, or block decisions.
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 |
|---|---|---|
| AI assistant output | Code appears complete but intent and risk are unclear. | Every PR gets a verification record. |
| Manual review | Reviewer must infer safety from raw diff. | Reviewer starts from security and intent evidence. |
| Compliance review | Evidence is reconstructed after the fact. | Evidence is captured during the merge workflow. |
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