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11/08/2026 3:13 pm
TL;DR: Agentic workflows can move from user intent to external tools, APIs, and datasets before a human review, exposing a gap between identity and action that static access models were not built to govern, according to PlainID. The controlling assumption is collapsing: access review and fixed entitlements do not reliably govern within-session delegated decisions.
NHIMG editorial — based on content published by PlainID: How Microsoft and PlainID Extend Trust Through Agentic Workflows
Questions worth separating out
Q: How should security teams govern AI agents that call APIs instead of using a UI?
A: Security teams should govern AI agents by treating each callable action as a scoped entitlement, not as a general application login.
Q: Why do static IAM models fail in agentic workflows?
A: Static IAM models assume access can be judged at session start and remain valid long enough to govern the whole task.
Q: What breaks when AI agents are given standing privileges?
A: Auditability, containment, and accountability all degrade.
Practitioner guidance
- Map the delegated chain end to end Document where a human request becomes an agent decision, where the agent crosses into external systems, and which tool, API, or dataset touchpoints need independent authorization.
- Move authorization checks to the point of use Require policy evaluation immediately before API calls, tool invocations, data retrieval, and response generation.
- Bind agent actions to initiating user authority Treat the agent as a delegated executor whose effective permissions cannot exceed the initiating user’s entitlements and the active policy context.
What's in the full article
PlainID's full article covers the operational detail this post intentionally leaves for the source:
- How the policy flow handles prompt input, retrieval, tool invocation, and output across an agentic chain
- The runtime authorization decision points used to keep delegated actions within the user’s authority
- How zero standing privilege is applied as workflows move between systems and data sources
- The composite identity model used to bind human and agent context together
👉 Read PlainID's analysis of runtime authorization for agentic workflows →
Runtime authorization in agentic workflows: are your controls keeping up?
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11/08/2026 3:27 pm
Runtime authorization is becoming the missing control plane for agentic identity. The article points to a real governance gap between identity approval and downstream action, which is where agentic workflows now create risk. Static access models were built for one request to one application, not for multi-party delegation across tools, APIs, and datasets. The practical conclusion is that authorization has to move to the moment of use if governance is going to survive workflow expansion.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to SailPoint.
A question worth separating out:
Q: Who is accountable when an AI agent acts outside its intended scope?
A: The organisation is accountable, but operational responsibility should sit with a named owner and a governance process that can explain the agent’s purpose, access, and recorded actions. Without that, autonomous behaviour becomes unassignable risk rather than managed automation.
👉 Read our full editorial: Runtime authorization for agentic workflows: what changes for IAM
