Topic starter
09/07/2026 3:32 pm
TL;DR: Agentic AI systems plan, remember, and execute across enterprise systems, creating four attack paths through poisoned training data, compromised vector databases, ungoverned agent identity, and cascading bad state, according to Commvault. Traditional human-centric IAM and recovery models do not account for machine-speed delegation, context corruption, or state consistency across agents.
Editorial analysis by NHI Mgmt Group, based on content published by Commvault: “The Four Attack Vectors Your AI Security Framework Isn’t Built For”.
Key questions
Q: What breaks when autonomous coding agents are not governed like non-human identities?
A: The control model breaks because the agent can act, choose tools, and change code without waiting for a human checkpoint.
Q: Why do poisoned training data and compromised vector databases create security risk for agentic AI?
A: They corrupt the context an agent uses to decide what to do next.
Q: How should organisations govern agent-to-agent delegation?
A: They should treat delegation as a formal governance boundary, not just an integration pattern.
Practitioner guidance
- Map agent identities to lifecycle ownership Create an inventory of every agent identity, its business owner, delegated authority, and offboarding trigger so agents are governed like other non-human identities.
- Classify retrieval stores as governed security assets Apply access control, integrity monitoring, and audit logging to vector databases and training inputs because they shape agent decisions before execution.
- Add delegation-chain review to least-privilege design Evaluate where an agent can spawn sub-agents, request outputs, or trigger workflows, then constrain those paths to the smallest approved scope.
Bottom line: Agentic AI turns identity into a runtime governance problem because the system can plan, delegate and act across enterprise tools without a fresh human prompt.
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09/07/2026 3:54 pm
Agentic AI collapses the assumption that identity is a human-paced control surface: The traditional IAM model assumes access is granted to a person or service and then observed over time. That assumption fails when the actor can plan, delegate, and execute across systems without a stable human operator in the loop. The implication is that governance must move from user-centric lifecycle thinking to runtime authority management for autonomous execution.
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: Why is recovery harder when agentic AI state is corrupted?
A: Because restoring the model or data does not guarantee the agent’s authority, context, or delegation rules are clean. If identity state remains poisoned, the system can resume with the same bad access layer and recreate the failure even after technical rollback.
👉 Read our full editorial: Agentic AI identity governance gaps are widening across enterprise systems