Resource
Agentic application security governs security evidence from autonomous coding agents, AI assistants, and agentic workflows. It treats AI-generated claims as probabilistic inputs and requires independent, policy-bound verification before those claims can support a consequential decision.
| Stage | Agentic claim | Verification question |
|---|---|---|
| IDE / coding agent | AI generated or modified this code | What is the provenance and review record? |
| MCP server | AI retrieved context or executed a tool | What was accessed, and was it authorized? |
| Repository | Commit was reviewed | Is there reviewer attestation and diff evidence? |
| CI | Tests and gates passed | Which artifacts were evaluated against which policy version? |
| Cloud / runtime | Fix is deployed and behaving as expected | Does runtime evidence corroborate the fix? |
Agentic application security is the practice of governing security evidence produced by autonomous coding agents, AI assistants, and agentic workflows. It ensures AI-generated claims are qualified, policy-bound, and not treated as authoritative without independent corroboration.
Coding agent security is the subset of agentic AppSec that focuses on AI agents that write, modify, or refactor code. It asks whether the agent output was reviewed, what evidence supports its safety claims, and whether those claims can be reproduced.
AI judgment is probabilistic. A high-confidence assessment can be wrong. Security authority requires independent, non-probabilistic evidence so the decision can be defended if it is later questioned.
GoSentrix captures AI-provenance signals, caps AI-generated claims at the detected evidence level, and requires non-probabilistic corroboration before any claim can support a consequential decision.
Govern Model Context Protocol servers and the agents that access them.
Capture the origin and review evidence for AI-generated code.
Ensure autonomous fixes are verified before release.
The public operating doctrine for release-evidence verification.
GoSentrix captures AI-provenance signals and evaluates them against versioned policy so agentic decisions can be replayed and defended.
