Topic starter
21/10/2026 2:45 pm
TL;DR: 14 common AI agent risks, including autonomy creep, hallucinations, prompt injection, tool misuse, agent hijacking and privilege compromise, are identified by Saidot and paired with monitoring, oversight, testing, access controls, accountability and RAG as mitigations. The core issue is that agentic systems can act repeatedly across systems faster than traditional governance can review, so controls must shift toward runtime visibility and capability scoping.
Editorial analysis by NHI Mgmt Group, based on content published by Saidot: “The 14 most common AI agent risks — and controls to mitigate them”.
Key questions
Q: What breaks when AI agents are given broad inherited permissions?
A: Broad inherited permissions break the assumption that access is tied to a narrow business need.
Q: Why do macOS malware campaigns often become an identity and access problem?
A: Because many campaigns abuse session authority, user approval, or privileged execution to reach their objective.
Q: What are the signs that agentic AI controls are too weak?
A: The warning signs are capability exposure and late-stage detection.
Practitioner guidance
- Define capability-specific access for every agent Map each AI agent to the exact systems, data sources and actions it needs, then remove inherited permissions that are not essential to the task.
- Monitor agent actions continuously Track tool usage, execution frequency, role changes and behavioural anomalies so that repeated misuse is visible while the agent is still active.
- Gate high-risk actions with human oversight Require human-in-the-loop or human-on-the-loop approval for actions that can change records, trigger payments, alter permissions or expose sensitive data.
Bottom line: AI agents become a governance problem when they are allowed to act across tools and systems with more privilege than their task requires.
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