Topic starter
22/07/2026 4:36 pm
TL;DR: Agentic AI systems can access tools, data, and applications to complete multi-step tasks, which expands attack surface and makes prompt manipulation, overreach, and multi-agent failure more consequential, according to Commvault's podcast conversation with Dr. Reid Blackman. The governance problem is no longer whether AI can act, but whether access, monitoring, and accountability keep pace with that action.
Editorial analysis by NHI Mgmt Group, based on content published by Commvault: “AI: Agents of Good, Meet Agents of Evil.”.
Key questions
A: Security teams should treat agents as active systems, not passive chat interfaces.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: What are the warning signs that agent-to-agent workflows are becoming unsafe?
A: Look for unclear ownership, broad delegated permissions, undocumented handoffs and agents that rely on each other’s outputs without policy checks.
Practitioner guidance
- Define agent identity and ownership Assign every production agent a named owner, a documented purpose and a bounded trust domain so accountability exists before deployment.
- Constrain tool access by task scope Limit each agent to the smallest tool set and data set needed for its specific workflow, then remove broad standing access that enables lateral action.
- Separate instruction handling from irreversible actions Require step-up approval or human review before an agent can execute destructive, financial or externally visible actions.
Bottom line: Agentic AI turns access governance into a runtime problem because the system can act, delegate and chain tool use across workflows.
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22/07/2026 4:41 pm
Agentic AI creates an identity governance problem before it creates a model-risk problem. The article shows that the central issue is not whether the model can generate useful output, but whether it can be trusted with tools, data and chained action. That moves the control question into IAM, PAM and NHI governance, because the runtime actor is no longer a passive application. Practitioners should govern the agent as a privileged machine identity with explicit scope and accountability.
A few things that frame the scale:
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
A question worth separating out:
Q: How do organisations decide when an autonomous agent needs approval versus direct execution?
A: Use the sensitivity of the action, not the sophistication of the model, to decide. Reading findings may be allowed directly, but configuration changes, production access, or cross-system actions should trigger escalation or denial outside the model. The right test is whether the requested action can be safely bounded before execution begins.
👉 Read our full editorial: Agentic AI security risks outpace conventional IAM controls