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15/08/2026 6:12 pm
TL;DR: AI agent monitoring has become a response to autonomous systems that can access sensitive data, move fast across APIs, and generate machine-speed risk, according to Obsidian Security’s analysis. The real issue is that existing IAM and monitoring models assume predictable execution, while agents create a visibility gap that undermines effective authority and response.
NHIMG editorial — based on content published by Obsidian Security: Real-Time AI Agent Monitoring: Detecting Threats Before They Escalate
By the numbers:
- 90% of agents carry excessive privileges that create urgent escalation risk.
- A single enterprise accumulated 2,500 agents before any inventory existed to track them.
Questions worth separating out
Q: How should security teams handle AI agent visibility?
A: Security teams must conduct an exhaustive discovery process to identify all deployed AI agents, both sanctioned and unsanctioned, across the organization.
Q: Why do AI agents create a different access-risk profile than traditional applications?
A: AI agents can chain actions, call multiple tools, and change behaviour based on context, so one credential can enable more than one operational path.
Q: What breaks when AI agent access is reviewed only after the fact?
A: After-the-fact review leaves a gap between action and containment.
Practitioner guidance
- Build a complete agent inventory first Identify every AI agent across cloud, SaaS, and internal systems, then record creator, purpose, data access, and integration points so no agent remains invisible.
- Measure effective authority instead of theoretical access Compare the permissions an agent has on paper with the systems and datasets it can actually reach at runtime, then prioritise any gap that expands blast radius.
- Detect maker mode and credential inheritance Review whether agents are running on creator credentials or inherited privileges, then strip that access down to the minimum needed for the workflow to function.
What's in the full article
Obsidian Security's full blog post covers the operational detail this post intentionally leaves for the source:
- A staged maturity model for AI agent monitoring that moves from inventory to continuous response.
- Specific integration points with SIEM, SOAR, XDR, IAM, and API gateway tooling.
- Operational definitions for effective authority, maker mode, and blast-radius analysis.
- Examples of prioritized alerting logic for risky agent combinations such as excessive privilege and orphaned ownership.
👉 Read Obsidian Security's analysis of real-time AI agent monitoring and threat detection →
AI agent monitoring: are your IAM controls keeping up with autonomy?
Explore further
15/08/2026 6:19 pm
AI agent monitoring is now an identity control, not just a detection control. The article correctly treats autonomous agents as a new category of digital identity with effective authority that can exceed the workflow they were built for. That shifts the problem from log review to governance of access, behaviour, and lifecycle. Practitioners should read this as evidence that AI agent monitoring belongs inside NHI and IAM operating models, not on the margins of security tooling.
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.
- Another 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so, which shows that policy maturity is lagging adoption.
A question worth separating out:
Q: Who should own AI agent access decisions in an enterprise IAM programme?
A: Ownership should sit with the identity and security function that can enforce policy across agent, user, and resource context, with clear escalation for high-risk actions. If no one owns the runtime decision, the organisation will default to ad hoc approvals, inherited permissions, or post hoc review, all of which are weaker than policy-driven control.
👉 Read our full editorial: AI agent monitoring exposes the new visibility gap in enterprise IAM
