Topic starter
02/08/2026 12:24 pm
TL;DR: Agentic AI adds identity, authorisation, and runtime control gaps that OWASP, CSA, NIST, and MITRE do not fully close, according to Straikerai. Agents need governance that is tested against abuse paths, not just mapped to framework coverage.
Editorial analysis by NHI Mgmt Group, based on content published by Straikerai: “Comparing AI Security Frameworks: OWASP, CSA, NIST, and MITRE”.
Key questions
Q: Why do existing AI governance frameworks struggle with agentic systems?
A: They mostly describe models, risk categories, or high-level oversight, while agentic systems also need identity, permissions, tool access, and execution controls.
Q: What do security teams get wrong about AI access risk?
A: Many teams focus on the model while ignoring the identity path that reaches it.
Q: How should security teams govern semiautonomous AI agents before they go live?
A: Start with task-scoped permissions, explicit credential lifecycles, and human oversight points before deployment volume makes retrofits impractical.
Practitioner guidance
- Define agent identity and ownership Assign each autonomous agent a named business owner, security owner, and explicit lifecycle record so delegated authority is never anonymous.
- Constrain tool use to declared tasks Bind tool permissions to task scope and revoke access once the task boundary ends, especially for multi-step orchestration.
- Test agent abuse paths with adversary scenarios Red-team prompt injection, unsafe tool invocation, and chained action abuse to see where runtime controls fail under realistic pressure.
Bottom line: Agentic AI security is no longer just a model safety problem because delegated action turns identity, authorisation, and runtime control into the real security boundary.
Glossary terms: Agentic AI, AI Governance, Adversary Emulation, Agent Identity
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This topic was modified 2 days ago by NHI Mgmt Group
02/08/2026 1:23 pm
Agentic AI security now depends on identity governance, not just model governance. The article is right to separate orchestration risk from traditional application security because an agent is not merely generating text, it is exercising delegated authority. That shifts the critical control point from prompt filtering to permissioning, approval boundaries, and lifecycle management. Practitioners should treat agent identity as a first-class governance object.
A few things that frame the scale:
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: What should organisations do when governance frameworks do not cover agent abuse paths?
A: Add attack-path validation to governance reviews and use adversarial scenarios to test whether the controls actually hold. Governance tells you who is accountable; validation tells you whether the control survives prompt injection, unsafe tool use, and emergent behaviour.
👉 Read our full editorial: AI security frameworks diverge on agentic risk, governance, and attack paths