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06/06/2026 11:11 am
TL;DR: Enterprises are deploying AI agents faster than they are building governance for them, with Gartner projecting 40% of enterprise applications will embed task-specific agents by end of 2026, up from less than 5% in 2025. The structural problem is that static IAM, quarterly reviews, and human-era delegation models cannot control runtime agent behaviour.
NHIMG editorial — based on content published by Strata Identity: agentic AI governance, runtime identity controls, and MCP enforcement
By the numbers:
- Gartner projects that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
Questions worth separating out
Q: How should security teams govern AI agents that access internal APIs and tools?
A: They should govern AI agents through runtime identity controls, not just static roles or quarterly reviews.
Q: Why do AI agents complicate zero trust and IAM models?
A: AI agents complicate zero trust because they make decisions and invoke tools at machine speed, often across multiple systems in one session.
Q: What breaks when AI agents use shared service accounts or hardcoded credentials?
A: Shared service accounts and hardcoded credentials destroy provenance and expand blast radius.
Practitioner guidance
- Inventory every agent identity and delegated path Map each agent to the human user, service account, API token, and downstream tools it can reach.
- Move authorisation to the moment of action Require policy evaluation at runtime for each tool invocation, rather than relying on provisioning-time role assignment.
- Replace shared credentials with task-scoped agent identities Issue ephemeral identities for agents and revoke them when the task ends.
What's in the full article
Strata Identity's full article covers the operational detail this post intentionally leaves for the source:
- A closer look at ephemeral token issuance and how it changes the control model for agent identities.
- The MCP Proxy and Bridge mechanics for authenticating and authorising each tool invocation.
- How OAuth On-Behalf-Of and proof-of-possession are used to preserve delegated authority across multi-hop workflows.
- Implementation context for teams using LangChain, CrewAI, OpenAI, Google Vertex, or Azure Foundry.
👉 Read Strata Identity's analysis of agentic AI governance and runtime identity controls →
Agentic AI governance gap: are your controls keeping up?
Explore further
06/06/2026 11:51 am
Agentic AI governance is an identity problem before it is an application problem. The article is right to place enforcement at the identity layer because every meaningful agent action starts with an access decision. That means security teams should stop treating agent governance as a wrapper around model usage and start treating it as a control problem that spans authentication, authorisation, and delegation. The practitioner takeaway is clear: if identity does not gate the action, governance is not actually present.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), 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: How do organisations keep delegation chains accountable in multi-agent workflows?
A: They need every hop to preserve traceability back to the original human request and approved scope. That means using delegated authorisation, proof-of-possession controls, and auditable policy decisions so downstream actions cannot silently acquire broader authority than the request justified.
👉 Read our full editorial: Agentic AI governance is lagging deployment velocity in enterprises
