Topic starter
24/10/2026 8:51 pm
TL;DR: Attack paths across AI systems now align with access, integrity, and exfiltration risks, according to Asenion. Delegated permissions and tool use become the point where model output can turn into operational impact.
Editorial analysis by NHI Mgmt Group, based on content published by Asenion: “AI Safety+Cybersecurity R&D Tracker”.
Key questions
Q: Why do AI agents increase non-human identity risk?
A: AI agents increase non-human identity risk because they can execute many actions quickly once they inherit a credential or tool permission.
Q: How can security teams tell if AI output is becoming a security control gap?
A: A control gap appears when generated text, code, or commands are used without review in systems that can store data, execute actions, or change permissions.
Q: What breaks when indirect prompt injection is not controlled in AI systems?
A: Indirect prompt injection breaks the assumption that retrieved content is safe to use as instruction material.
Practitioner guidance
- Separate instructions from content Design AI workflows so system prompts, retrieved content, and user inputs are handled as distinct trust classes, with untrusted content unable to override policy instructions.
- Constrain agent permissions by task Limit tool use, connector scope, and token reach so an AI agent can only access the minimum accounts and actions needed for the current session or workflow.
- Inspect outputs before downstream execution Require validation on AI-generated text, code, and commands before those outputs are stored, executed, or passed into business systems that can change state.
Bottom line: AI safety taxonomies now map cleanly to access, integrity, and exfiltration risks, which means governance has to move beyond model-quality checks.
Glossary terms: Prompt Injection (Agentic), Agentic Manipulation, Model Extraction, Insecure Output Handling
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This topic was modified 6 days ago by NHI Mgmt Group
25/10/2026 11:12 am
AI governance is now an identity and access problem as much as a model problem. This tracker groups threats that move from prompt influence to control loss, which means the real risk boundary sits around credentials, tools, and delegated permissions. If an AI system can act, then its identity, privilege, and trust relationships become governance objects, not implementation details.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: When should organisations apply agentic AI governance instead of basic LLM controls?
A: Organisations should apply agentic AI governance when the system can choose tools, call APIs, or act on behalf of a person or process. Basic LLM controls are not enough once the model can change state, because the risk is no longer only text generation. Governance has to cover permissions, tool scope, and delegated authority.
👉 Read our full editorial: AI safety and cybersecurity trackers show attack paths widening
This post was modified 6 days ago by NHI Mgmt Group