Topic starter
11/10/2026 6:56 pm
TL;DR: A Microsoft Copilot Studio agent connected through Zuma can still be stopped at runtime when policy, intent deviation, and anomaly controls evaluate each tool call separately, even though the agent has valid credentials and authorised tools, according to Saviynt. The real shift is that access approval is no longer enough; trust has to be re-evaluated at execution time.
Editorial analysis by NHI Mgmt Group, based on content published by Saviynt: “Defense in Depth in Real Time”.
Key questions
Q: What fails when an AI agent has valid credentials but unsafe runtime discretion?
A: Setup-time authorisation fails because it answers only whether the identity may connect, not whether a later action still fits the approved purpose.
Q: Why do authorised agent actions still create security risk?
A: Because authorization alone does not prove the action was appropriate.
Q: What are the signs that AI agent drift controls are not mature enough yet?
A: If every unfamiliar tool call is blocked immediately, the baseline is probably too immature to distinguish normal learning from suspicious drift.
Practitioner guidance
- Define action-level deny rules for high-risk agent tools Block destructive or irreversible functions at the gateway layer, even when the agent has valid credentials and the user behind it is authorised for related work.
- Separate structural permission from semantic approval Use intent analysis to evaluate whether a permitted sequence still matches the agent’s declared purpose, especially when the agent chains read and write tools together.
- Baseline normal agent behaviour before enforcing drift controls Keep drift and anomaly checks in monitor mode until you have enough history to distinguish ordinary work from new tool use or unusual sequencing.
Bottom line: AI agents can still be risky even when every credential check passes, because the unsafe decision often happens after access is granted.
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11/10/2026 6:57 pm
Runtime authorisation is becoming the real control plane for AI agents. Once an agent holds valid credentials, the security question shifts from identity admission to action governance. That is a different problem from human SSO or static NHI access because the model decides what to do next at execution time. Practitioners should treat each tool call as a governed event, not a by-product of a trusted session.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should teams govern AI agent tool access across policy and behaviour layers?
A: Use policy for explicit prohibitions, intent analysis for purpose, and anomaly detection for novelty. Do not treat these as overlapping versions of the same control. They answer different questions, so governance should assign ownership to each layer and test them against distinct failure modes.
👉 Read our full editorial: Defense in depth for AI agents exposed by runtime access checks