Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “Enterprise AI Governance Best Practices for Secure AI Adoption”.
Key questions
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: When do autonomous systems create more governance risk than ordinary automation?
A: They create more risk when they can decide the action sequence, choose tools at runtime, and execute without human approval.
Q: What are the warning signs that GenAI governance is failing in production?
A: Common signs include rising token spend without clear business growth, poor traceability of prompt flows, inconsistent routing between models, and backend services that still contain long-lived API keys.
Practitioner guidance
- Build a continuous AI asset inventory Discover agents, copilots, LLM applications, and MCP servers continuously so shadow deployments do not escape ownership or policy coverage.
- Assign named ownership to every AI system Tie each AI asset to a responsible business owner and technical steward, with clear approval paths for changes to access, prompts, and tool scope.
- Enforce runtime guardrails for tool use Block or approve sensitive tool calls at execution time, especially where agents can reach internal APIs, customer data, or state-changing actions.
Bottom line: AI governance for enterprise agents is fundamentally about constraining runtime behaviour, not just writing policy.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
02/08/2026 12:42 pm
AI governance is now an identity problem as much as a policy problem. The article correctly shows that agents and MCP-connected systems are not just applications, they are actors that need ownership, scope, and traceability. Once an AI system can choose tools and chain actions, the security question becomes who or what is authorised to act, under what identity, and with what revocation path. Practitioners should stop treating AI governance as a board-level abstract and start treating it as runtime access governance.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: When should organisations prioritise visibility over advanced guardrails for agentic AI?
A: Visibility should come first whenever agents are being connected faster than the organisation can inventory them. If the business cannot name the agents, their owners, and their connected systems, more advanced policy layers will rest on unknown identity relationships.
👉 Read our full editorial: Enterprise AI governance best practices for secure AI adoption