Topic starter
16/01/2026 6:11 pm
TL;DR: AI agents are becoming a governance problem as much as a technical one, because practitioners cannot secure autonomous tool access without first establishing identity, trust, and operational control, according to Okta’s analysis. The real issue is not whether AI exists in the stack, but whether identity models can handle agentic behaviour at all.
Editorial analysis by NHI Mgmt Group, based on content published by Okta: “Building Trust in Identity: Navigating the AI Frontier in Sales”.
Key questions
Q: How should security teams inventory AI agents before granting production access?
A: Start by building a register that links each agent to its owner, the identities it uses, the systems it can reach, and the data it can touch.
Q: How do AI agents change privilege management in identity programmes?
A: AI agents can select tools and act during runtime, which makes static entitlement planning less reliable.
Q: What breaks when AI agents are reviewed like human users?
A: Human review assumes access is stable long enough to be observed, approved, and recertified.
Practitioner guidance
- Define agent ownership and accountability Assign a named business and technical owner for each AI agent before it is connected to any production tool or dataset.
- Bound agent permissions to task scope Map each agent to the smallest set of tools, APIs, and data sources needed for its intended function.
- Separate human approval from machine execution Use explicit approval gates for sensitive actions that an agent can request but not execute independently.
Bottom line: AI agents are not just another workload class, because their runtime behaviour can change the identity control problem from static access management to dynamic trust governance.
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This topic was modified 4 days ago by NHI Mgmt Group
08/10/2026 4:51 pm
Trust is the real control plane for AI agents. When an agent can decide actions at runtime, the security question is no longer only authentication. It becomes whether the organisation can prove what the agent is allowed to do, on whose behalf, and under what governance boundary. For practitioners, that shifts agent security into the same discipline as identity lifecycle and privileged access control, but with far less tolerance for ambiguity.
A question worth separating out:
Q: Should IAM teams treat AI as a separate identity domain?
A: No. IAM teams should treat AI as a trigger to reassess existing identity domains, especially NHI, PAM, and access lifecycle management. The underlying controls still apply, but AI changes how quickly decisions happen and how delegation is expressed. The right response is to adapt governance models, not build a disconnected exception path.
👉 Read our full editorial: Identity security for AI agents starts with trust, not tooling