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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.
NHIMG editorial — based on content published by Commvault: a podcast discussion on agentic AI, cyber resilience, trust, and data management
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do agentic AI systems complicate existing IAM and PAM controls?
A: They complicate them because IAM and PAM were built around stable identities, human-paced approvals, and entitlements that are reviewed after use.
Q: What do organisations get wrong about AI agent risk scores?
A: They often treat risk scores as reporting rather than decision input.
Practitioner guidance
- Define AI agent identity ownership Assign a business owner, technical owner, and approval boundary for every agent that can call tools or access data.
- Scope agent permissions to tasks Replace broad connector access with task-scoped permissions, short-lived tokens, and explicit tool allowlists.
- Log delegated actions end to end Capture prompt, tool call, data access, and downstream agent handoff events in one trace so investigations can reconstruct how a decision happened.
What's in the full article
Commvault's full podcast discussion covers the conversational detail this post intentionally leaves for the source:
- The full episode includes the Amazon recruiting example and the reasoning behind stopping an AI project when the outcome could not be corrected.
- It also covers the discussion of AI agents interacting with other agents, including why that complicates monitoring and trust boundaries.
- The source goes deeper on prompt attacks, multi-agent risk, and the ethical questions raised by agentic decision-making.
- Listeners get the full back-and-forth between Nathan Macintosh and Dr. Reid Blackman on how organisations should think about responsible AI adoption.
👉 Read Commvault's podcast discussion on agentic AI, trust, and cyber resilience →
Agentic AI systems: what it means for IAM and governance?
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22/07/2026 4:41 pm
Agentic AI is becoming a non-human identity problem, not just an AI safety problem. Once an agent can access tools, data, and systems, the governance question shifts from model output quality to delegated privilege and containment. That brings IAM and PAM into the center of AI governance, because an agent with persistent access behaves like an identity that needs lifecycle control. Practitioner conclusion: treat agent access as a governed identity class with explicit ownership.
A question worth separating out:
Q: Which frameworks should organisations use for autonomous AI governance?
A: Use OWASP agentic and LLM guidance for application risk, NIST AI RMF for governance structure, and MITRE ATLAS for adversarial technique mapping. Then translate those frameworks into operational controls that restrict tool access, define approval boundaries, and produce auditable runtime evidence. Frameworks help classify the risk, but enforcement must happen in execution.
👉 Read our full editorial: Agentic AI security risks outpace conventional IAM controls
