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02/08/2026 11:25 am
TL;DR: Enterprises are deploying AI agents, copilots, and LLM applications faster than security and compliance teams can govern them, creating gaps in ownership, runtime control, auditability, and policy enforcement across agents and MCP servers, according to Akto. The governance problem is no longer theoretical: unmanaged AI behaves like a new class of machine identity with broad permissions and limited oversight.
NHIMG editorial — based on content published by Akto: Enterprise AI Governance Best Practices for Secure AI Adoption
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 MCP-connected AI workflows create new governance risk?
A: MCP-connected workflows expand the identity perimeter because a model can act through tools and data sources rather than only through a human user session.
Q: What do organisations get wrong about governing AI use?
A: They often separate AI governance from IAM and lifecycle management, even though AI adoption depends on who can access tools, what data those tools can reach, and how access ends.
Practitioner guidance
- Inventory every AI system and MCP connector Create a governed inventory of agents, copilots, LLM applications, and MCP servers, including shadow AI discovered outside approved channels.
- Assign machine identity ownership and least privilege Bind each AI system to a named owner and a distinct identity, then restrict tool access to the minimum actions required for the approved use case.
- Enforce runtime guardrails on tool calls Inspect prompts, outputs, and function calls at the point of action so unsafe requests can be blocked before data is exposed or an unauthorised workflow executes.
What's in the full article
Akto's full guide covers the operational detail this post intentionally leaves for the source:
- A step-by-step 90-day governance roadmap for discovery, classification, ownership, and guardrail rollout.
- Operational guidance for runtime enforcement across AI agents, LLM apps, and MCP-connected workflows.
- Risk, policy, and compliance mapping that shows how governance controls align to enterprise obligations.
- Lifecycle controls for deployment, monitoring, red teaming, and retirement of AI systems.
👉 Read Akto's guide to enterprise AI governance best practices for secure AI adoption →
AI governance for agents and MCPs: what practitioners need now?
Explore further
02/08/2026 12:42 pm
AI governance is now an identity governance problem as much as a model risk problem. The article correctly treats agents, copilots, and MCP-connected systems as governed assets, not just software features. That shift matters because the security question is no longer only whether the model is safe, but whether the machine identity behind it is bounded, owned, and auditable. Practitioners should treat AI governance as part of IAM and PAM design, not as a parallel policy exercise.
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, platform team, or business function that granted and operated the agent. The identity may act independently, but governance cannot detach responsibility from the delegation chain. Programs should define ownership, escalation, and remediation paths before deployment so responsibility is clear when the agent's behaviour changes.
👉 Read our full editorial: Enterprise AI governance best practices for secure AI adoption
