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15/08/2026 6:12 pm
TL;DR: AI agents are creating new security vulnerabilities through prompt injection, token compromise, excessive privilege, and data exfiltration, while traditional controls struggle to govern systems that learn, adapt, and act independently, according to Obsidian Security. The governance gap is structural: existing IAM assumes stable, reviewable access, but agent behaviour changes at runtime.
NHIMG editorial — based on content published by Obsidian Security: AI Security Top AI Agent Security Risks and How to Mitigate Them
By the numbers:
- AI agents move 16x more data than human users performing equivalent tasks, which dramatically expands the blast radius of any single compromised agent.
- Research across enterprise deployments shows that 90% of agents hold excessive privileges, creating a gap between configuration and effective authority.
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 service accounts and AI agents create different identity risk than employees?
A: Service accounts and AI agents create different risk because they are not managed through HR lifecycle events, yet they often hold broad technical permissions and can act at machine speed.
Q: What breaks when organisations rely on periodic access reviews for AI systems?
A: Periodic access reviews break when the identity scope changes between review cycles.
Practitioner guidance
- Map every agent to an identity owner Assign a business and technical owner to each deployed agent, then require a named approver for access changes, workflow expansion, and production enablement.
- Replace static credentials with short-lived authentication Use certificates, hardware-backed secrets, or tightly scoped short-lived tokens for agent access.
- Review effective authority, not just assigned roles Compare configured permissions with the actual systems, datasets, and actions an agent can reach across SaaS and cloud services.
What's in the full article
Obsidian Security's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step guidance for securing AI agent authentication with certificates, short-lived tokens, and automatic rotation.
- Example policy logic for dynamic authorization decisions across data sensitivity, business hours, and anomaly score.
- Concrete monitoring fields to send into SIEM and SOAR for agent telemetry, including prompts, outputs, and authorization events.
- Implementation checklist for testing prompt injection resistance, token compromise scenarios, and model manipulation paths.
👉 Read Obsidian Security's analysis of AI agent security risks and mitigations →
AI agent security risks: are your IAM controls keeping up?
Explore further
15/08/2026 6:19 pm
AI agents should be treated as machine insiders, not as ordinary applications. The article describes systems that read, decide, and act across multiple enterprise services, which places them inside identity governance rather than beside it. That matters because the risk is defined by authority, not by form factor. Practitioners should read agent security as an NHI governance problem with operational consequences for IAM, PAM, and SaaS access.
A few things that frame the scale:
- AI agents move 16x more data than human users performing equivalent tasks, according to AI Agents: The New Attack Surface report.
- 90% of agents hold excessive privileges in enterprise deployments, according to AI Agents: The New Attack Surface report.
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: AI agent security risks expose gaps in traditional IAM controls
