Topic starter
24/10/2026 8:53 pm
TL;DR: Token lifecycles can outrun access reviews, leaving governance to catch up after the work is done, according to Newcore. Logs that cannot reliably separate agent activity from human activity make ownership and revocation harder to prove.
Editorial analysis by NHI Mgmt Group, based on content published by Newcore: “Control Agent Access When It's Used, Not When It's Reviewed”.
By the numbers:
- 68% cannot reliably separate agent activity from human activity in their logs, according to the Cloud Security Alliance cited by Newcore.
Key questions
Q: Why do short-lived agent tokens still create governance risk?
A: Because short-lived does not mean well-governed.
Q: How do security teams know if agent governance is actually working?
A: It is working only if the team can answer three questions quickly for any agent: what it can reach, what it did recently, and whether that behaviour matches intent.
Q: What breaks when AI agent access is reviewed only after the fact?
A: After-the-fact review leaves a gap between action and containment.
Practitioner guidance
- Define task-scoped access for agents Issue permissions only for the specific job the agent is performing, and end them when the task closes rather than waiting for a later review cycle.
- Shorten token lifetimes to match execution Set AI agent credentials to expire with the task, so unused access disappears automatically instead of remaining live until quarterly certification.
- Assign an owner to every agent Record a human owner or accountable team for each agent credential so revocation, incident follow-up and audit trail closure have a clear target.
Bottom line: AI agent access becomes risky when token lifetimes outlast the task that justified them, because governance then happens after exposure instead of during execution.
Glossary terms: Authorization-time control, Task-Scoped Access, Token Lifecycle, Access Review
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This topic was modified 6 days ago by NHI Mgmt Group
25/10/2026 11:11 am
Periodic access reviews do not govern agentic access at the right time. The review model assumes access persists long enough to be observed, assessed and removed on a schedule. AI agents can complete a task and leave behind a valid token before the next review window opens. That means the governance failure is not missing review logic, but a lifecycle mismatch between certification cadence and execution speed.
A few things that frame the scale:
- Over 70% of organisations lack automated access risk analysis, user access reviews and provisioning and deprovisioning, according to Pathlock's 2025 Digital Transformation and Access Risk Report.
A question worth separating out:
Q: What is the difference between periodic access reviews and authorization-time controls for agents?
A: Periodic reviews ask whether access was still needed after the fact, while authorization-time controls decide whether access should exist right now. For AI agents, that difference matters because the work can start and end before a review cycle runs, so governance has to move to the point of use.
👉 Read our full editorial: Periodic access reviews fall behind AI agent token lifecycles
This post was modified 6 days ago by NHI Mgmt Group