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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.
NHIMG editorial — based on content published by Obsidian Security: AI 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, according to Obsidian Security.
- Research shows 90% of agents hold excessive privileges relative to their actual workflow requirements.
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do AI agents complicate least-privilege design?
A: AI agents complicate least-privilege design because their tool use can change dynamically while the underlying permissions remain persistent.
Q: What breaks when AI agents inherit the creator’s access without review?
A: Maker-identity inheritance breaks the separation between who built the agent and who should be accountable for its access.
Practitioner guidance
- Map effective authority for every deployed agent Document which systems each agent can actually reach, not just which roles it was assigned at provisioning time.
- Replace persistent tokens with short-lived runtime credentials Use certificate-based authentication, workload identity federation, and automatic rotation so agent access expires with the task.
- Bind agent actions to runtime policy checks Apply contextual controls for data sensitivity, request volume, and anomaly score so an authenticated agent is still evaluated before each sensitive operation.
What's in the full article
Obsidian Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Example architectures for certificate-based authentication and workload identity federation for agents
- Detailed policy logic for attribute-based and policy-based access control in SaaS workflows
- The full incident-response checklist for suspected agent compromise, including credential revocation and forensic preservation
- Configuration examples for SIEM and SOAR integration to detect anomalous agent behaviour
👉 Read Obsidian Security's analysis of AI agent security and autonomous risk →
AI agents and autonomous risk: are IAM controls keeping up?
Explore further
15/08/2026 6:19 pm
Agentic AI security is now an identity governance problem, not just a model safety problem. Once an AI system can decide when to act, which tools to use, and how to chain those actions, the old separation between authentication and authorisation starts to collapse. The issue is no longer whether the model is accurate, but whether the identity behind it can be governed at runtime. Practitioners need to treat agent behaviour as an access-control event, not a software feature.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, which leaves nearly half operating without a complete compliance and investigation view.
A question worth separating out:
Q: Who is accountable when an AI agent accesses sensitive data it was not meant to use?
A: Accountability sits with the team that approved the agent, its connectors, and its policy boundaries, not with the runtime behaviour alone. Organisations need ownership for intent, permissions, monitoring, and validation so they can prove whether the agent stayed inside its approved purpose. Without that, audit and regulatory response become retrospective guesswork.
👉 Read our full editorial: From agentic AI to autonomous risk: IAM controls are lagging
