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19/08/2026 3:30 pm
TL;DR: Claude Fable 5 can turn vulnerability discovery into an API call, but Ory argues the real control question is how to authorise the next move safely and deterministically. For agentic systems, probabilistic approval layers create governance ambiguity, not assurance, because runtime access decisions must be in-loop, explicit, and auditable.
NHIMG editorial — based on content published by Ory: Claude Fable 5 can find your vulnerabilities. What authorizes its next move?
Questions worth separating out
Q: How should security teams separate AI agent access control from runtime action authorization?
A: Treat them as two different control planes.
Q: Why do AI agents create a different authorisation problem from ordinary automation?
A: Ordinary automation follows predefined rules, so the access path is known before execution starts.
Q: What breaks when a classifier is used as the approval layer for agent actions?
A: The organisation loses deterministic control.
Practitioner guidance
- Separate risk scoring from enforcement Use classifiers to rank agent behaviour, then pass only deterministic policy decisions into the enforcement layer.
- Map agent actions to distinct policy rules Treat discovery, retrieval, verification, and remediation as different action classes with different permissions and audit expectations.
- Review every AI agent for in-loop authorisation Check whether an agent can request, combine, and execute actions without a human or policy gate between steps.
What's in the full article
Ory's full blog post covers the operational detail this post intentionally leaves for the source:
- The exact authorisation pattern Ory describes for agent actions inside the Ory Keto control model
- The practical distinction between probabilistic classification and deterministic policy enforcement in agent workflows
- Implementation context for agentic AI and MCP security that this analysis only frames at the governance level
- The product-level identity and permissions architecture behind Ory's approach to agent authorisation
👉 Read Ory's analysis of AI agent authorisation and deterministic policy →
AI agent authorisation gaps: are your controls keeping up?
Explore further
19/08/2026 4:13 pm
Deterministic authorisation is now the baseline requirement for agentic identity. Once an AI system can decide its next action at runtime, a probabilistic layer is no longer enough to govern access. Identity control must answer the same inputs with the same decision every time, or the organisation has no enforceable boundary. The implication is that agent governance has moved from scoring behaviour to enforcing policy.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, which helps explain why runtime controls cannot rely on development discipline alone.
A question worth separating out:
Q: Who should be accountable for AI agent security incidents?
A: Accountability should sit with the team that owns the agent's business function and permission model, not with a single security tool owner. If the organisation cannot name who approved the agent's scope, who can revoke it, and who reviews runtime exceptions, the governance model is incomplete.
👉 Read our full editorial: Deterministic authorisation for AI agents is now a governance issue
