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24/06/2026 10:41 pm
TL;DR: Gartner says purpose and intent for AI agents cannot be discovered after the fact, while 50% of successful attacks against AI agents are expected to exploit access control weaknesses by 2029, highlighting a governance gap across identity registration, ownership, and authorization. Static IAM assumptions break when agents reason, chain tools, and act at runtime without a human-paced review window.
NHIMG editorial — based on content published by Silverfort: AI agent identity governance and runtime enforcement
Questions worth separating out
Q: How should security teams govern AI agents as identities?
A: Treat each AI agent as a governed identity with a declared owner, defined purpose, and explicit scope.
Q: Why do AI agents create more risk than ordinary automation?
A: Ordinary automation follows a script.
Q: What breaks when AI agent ownership is unclear?
A: Accountability breaks first, followed by scope control and offboarding discipline.
Practitioner guidance
- Inventory AI agents as governed identities Create a register that captures purpose, owner, execution context, and the credentials each agent uses.
- Move authorization checks to execution time Require policy decisions at the moment an agent invokes a tool, reads data, or triggers a workflow.
- Separate human, NHI, and agent credentials Eliminate shared credentials between people and agents, and tie each agent to a distinct credential path.
What's in the full article
Silverfort's full blog post covers the operational detail this post intentionally leaves for the source:
- Runtime enforcement mechanics for AI agents connected through MCP and native integrations.
- The policy and observability model used to trace an agent back to a human owner and credential path.
- Examples of how execution-time decisions block tool calls when an agent exceeds its declared purpose.
- The vendor's lifecycle framing for registration, ownership, and least-privilege authorization across agent identities.
👉 Read Silverfort's analysis of AI agent identity governance and runtime enforcement →
AI agent identity governance: what IAM teams are missing?
Explore further
25/06/2026 7:29 am
Identity registration is the governing assumption that breaks first. IAM for human users assumes the identity subject is known, stable, and inventoryable before access is granted. That assumption fails for AI agents when teams discover them only after deployment, because purpose, ownership, and scope are not self-evident from runtime behaviour. The implication is that discovery alone is not governance, and post-hoc observability cannot substitute for declared identity intent.
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, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who should be accountable when an AI agent causes a security incident?
A: Accountability should sit with the human owner of the agent and the team that approved its scope and credentials. Security teams should be able to trace the action from the agent back to a named owner, a credential path, and a policy decision. If that chain is missing, the programme cannot assign responsibility cleanly.
👉 Read our full editorial: AI agent identity governance fails without runtime enforcement
