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15/08/2026 6:09 pm
TL;DR: Agentic AI is moving from content generation into autonomous workflow execution, meaning enterprise risk now sits in what these systems are allowed to do, not just what they produce, according to AppSOC. The governance gap is that critical infrastructure is being deployed without identity, authorization, and runtime controls built for autonomous actors.
NHIMG editorial — based on content published by AppSOC: Agentic AI Has Become Critical Infrastructure. Most Companies Aren’t Treating It That Way
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 AI agents complicate least-privilege access models?
A: Because agents often use shared or long-lived NHIs, move quickly, and cross platform boundaries that human-centric review processes do not cover well.
Q: What breaks when AI agents are not governed at runtime?
A: Without runtime governance, an agent can shift behaviour after provisioning and still execute actions that were never reviewed in context.
Practitioner guidance
- Define each agent as a governed identity Create an inventory of all AI agents, MCP connections, service credentials, and data sources they can touch.
- Constrain runtime authority to task scope Issue short-lived credentials, narrow API scopes, and explicit policy checks for every sensitive action.
- Monitor agent behaviour as an access signal Log tool calls, data queries, workflow triggers, and privilege changes in a way that can be reviewed by IAM, SOC, and compliance teams.
What's in the full article
AppSOC's full article covers the operational detail this post intentionally leaves for the source:
- The vendor's step-by-step view of why agentic systems behave like infrastructure rather than ordinary software features.
- The specific runtime control questions AppSOC says teams should ask about agent permissions, monitoring, and unsafe actions.
- The article's explanation of contextual policy enforcement across role, situation, behaviour, and trust relationship.
- The secure-by-design development practices the source ties to governed prompt management and human approval for high-risk operations.
👉 Read AppSOC's analysis of why agentic AI needs infrastructure-grade security →
Agentic AI as infrastructure: are IAM controls keeping up?
Explore further
15/08/2026 6:17 pm
Agentic AI creates an identity governance problem before it creates a model-risk problem. The article correctly shifts the conversation from output safety to operational authority, because autonomous systems act inside business processes rather than beside them. That means the real question is not whether the model is well-behaved in isolation, but whether its runtime permissions, data reach, and approval boundaries are governable. Practitioners should read this as an identity programme issue first and an AI issue second.
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.
- 92% of organisations agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, according to the same report.
A question worth separating out:
Q: Which frameworks apply to AI gateway governance and agent identity?
A: OWASP NHI and Zero Trust are the most direct fits for workload identity, tool exposure, and least-privilege control. For broader AI governance, teams should also use an AI risk framework to define ownership, evidence, and accountability across the agent lifecycle. The common requirement is that runtime access must be explainable.
👉 Read our full editorial: Agentic AI as critical infrastructure demands identity-grade controls
