Topic starter
15/08/2026 6:12 pm
TL;DR: As AI agents move 16x more data than human users and increasingly act with delegated authority, Obsidian Security argues that traditional IAM, static RBAC, and perimeter controls cannot keep pace with dynamic access, chaining, and prompt injection risks. The core issue is assumption collapse: access review and least-privilege models were built for stable identities, not runtime decision-making systems.
Editorial analysis by NHI Mgmt Group, based on content published by Obsidian Security: “From Agentic AI to Autonomous Risk: Why Security Must Evolve”.
By the numbers:
- AI agents move 16x more data than human users across the same applications.
Key questions
Q: What breaks when autonomous agents are governed like human users?
A: Session-based IAM breaks first, because autonomous agents can make and execute decisions between review points.
Q: Why do AI agents increase risk in SaaS environments?
A: AI agents increase risk because they can operate through existing application permissions and continue using them as tasks change.
Q: What are the signs that an agent’s effective permissions are too broad?
A: Look for agents that can move from retrieval to mutation, inherit user or service credentials without separation, or reach multiple environments with the same identity.
Practitioner guidance
- Map agent effective authority Inventory what each agent can actually reach across SaaS, databases, files, and downstream APIs, then compare that to its documented role and workflow purpose.
- Replace broad delegated access with task-scoped identities Issue separate identities for agents, avoid creator-credential inheritance, and bind each identity to a narrow task, environment, and set of data scopes.
- Add runtime controls for chained actions Use behavioural monitoring and policy checks to flag prompt injection, unusual API chaining, privilege jumps, and off-hours access that exceeds the agent's normal pattern.
Bottom line: Autonomous agents create an identity governance problem because they act with delegated authority but do not behave like stable human users or static workloads.
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15/08/2026 6:19 pm
Assumption collapse is the core governance story here: access review models were designed for identities whose privileges remain stable long enough to inspect. That assumption fails when an autonomous agent can gain, combine, and discard authority inside one task flow. The implication is that review cadence alone no longer describes control coverage.
A few things that frame the scale:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations decide whether AI agent access belongs in IAM or separate governance?
A: If an AI agent can choose actions, call tools, or move between systems during runtime, it should be governed as a distinct identity class with explicit policy and audit coverage. If it is just a scripted workflow, ordinary machine identity controls may be enough. The decision should follow behaviour, not the label attached to the system.
👉 Read our full editorial: From agentic AI to autonomous risk: IAM controls are lagging