Topic starter
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.
Editorial analysis by NHI Mgmt Group, based on content published by Obsidian Security: “Real-Time AI Agent Monitoring: Detecting Threats Before They Escalate”.
Key questions
Q: What breaks when AI agents inherit the creator’s access without review?
A: Maker-identity inheritance breaks the separation between who built the agent and who should be accountable for its access.
Q: Why do AI agents increase IAM and PAM risk?
A: AI agents increase IAM and PAM risk because they can execute actions quickly once privilege is available, which shortens the time available to detect misuse.
Q: How do you know if AI agent monitoring is actually working?
A: It is working when you can explain why a sequence of actions was allowed, blocked, or escalated, using evidence from the full chain rather than a single request.
Practitioner guidance
- Build a complete agent inventory Discover every AI agent across SaaS, cloud, and internal systems, then document creator, permissions, data access, and integrations so you can measure effective authority rather than assume it.
- Separate creator access from agent access Review whether agents are running on the credentials of the user who created them and remove inherited privileges that exceed the workflow’s actual task scope.
- Baseline agent behavior by role and system Define normal access patterns for each agent, then alert on unexpected data volume, unusual destinations, or API sequences that fall outside those patterns.
Bottom line: AI agent monitoring is emerging because autonomous systems behave like machine identities with effective authority that can exceed the business task they were created for.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
15/08/2026 6:19 pm
AI agent monitoring is really a machine-insider governance problem: the security issue is not just detection, but whether organisations can account for what an autonomous identity is allowed to do at runtime. Once agents can choose actions across systems, the old separation between access control and monitoring breaks down. Practitioners need to treat agent telemetry as identity evidence, not just operations data.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between AI agent monitoring and traditional user monitoring?
A: Traditional user monitoring assumes a stable human actor with predictable sessions and review cycles. AI agent monitoring has to track a machine identity that can chain actions, shift context, and inherit access across systems, so the control focus moves from login events to runtime authority and behavioral drift.
👉 Read our full editorial: AI agent monitoring exposes the new visibility gap in enterprise IAM