Topic starter
15/08/2026 6:08 pm
TL;DR: API-led agent inventory misses active AI agents running outside sanctioned channels, leaving security teams with incomplete ownership, risk scoring, and enforcement data, according to Obsidian Security. The governance problem is not just visibility, but the assumption that configuration data reflects runtime behaviour across AI agents, SaaS features, and shadow deployments.
Editorial analysis by NHI Mgmt Group, based on content published by Obsidian Security: “The AI Agents Your Security Tools Can’t See”.
By the numbers:
- Across Obsidian's customer base, agent counts grew from under 500 in late 2024 to nearly 95,000 by February 2026.
- 38% of agents carry medium, high, or critical risk factors from the moment they deploy.
Key questions
Q: Where does API-based AI agent inventory fail in practice?
A: It fails whenever the agent exists outside the reporting plane, such as in SaaS-native features, personal-account use, or low-code workflows that never create a formal integration event.
Q: Why do autonomous AI agents increase governance risk even without an external attacker?
A: They can optimise for a hidden objective, suppress details, or skip controls while still appearing productive.
Q: What signs show that an AI agent inventory is incomplete?
A: Look for agents with no documented owner, agents created by departed accounts, large data movement from a single agent, and AI activity that appears inside trusted SaaS tools without a matching connection event.
Practitioner guidance
- Build a runtime AI inventory model Correlate API reports with browser telemetry, SaaS-native feature activation, and network signals so the inventory reflects active behaviour, not just configured objects.
- Separate SaaS features from integrations Track embedded AI capabilities in platforms such as collaboration and CRM tools alongside third-party connectors, because feature activation can occur without a new OAuth grant.
- Require an owner for every agent Block policy approval when an agent has no documented business owner, especially if it was created through low-code tooling or by a departed account.
Bottom line: AI agent governance breaks down when teams treat platform APIs as a full record of what is active across the estate.
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15/08/2026 6:17 pm
AI agent inventory is now a runtime governance problem, not an asset-listing problem. The article shows that API-sourced views only capture what platforms formally report, while the real exposure sits in active behaviour across SaaS features, browser sessions, and shadow deployments. That means inventory accuracy is now a precondition for entitlement mapping, risk scoring, and enforcement, not a reporting nicety. Practitioners should treat visibility as the control plane for all downstream AI governance.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should security teams govern AI features embedded in SaaS applications?
A: Treat embedded AI as a machine identity problem with data access implications. Inventory the feature, map the connected permissions, define what data it may use, and monitor retention and sharing paths. If the AI feature can read corporate content, it needs explicit approval, logging, and periodic review like any other privileged integration.
👉 Read our full editorial: AI agent visibility gaps leave enterprise governance blind to shadow risk