Security teams should inventory every AI agent, workload, connector, and service account that touches AI services, then map each identity to ownership, access scope, and dependency. The goal is to see who or what is acting, what it can reach, and whether credentials are rotated, least privileged, and monitored across cloud and development environments.
Why This Matters for Security Teams
AI discovery in agentic environments is not a simple asset-inventory problem. Autonomous agents can spin up tool calls, exchange credentials, and cross service boundaries faster than manual review can keep up, which means the real question is not just what exists, but what each agent can do right now. That makes discovery a control function, not a one-time catalog exercise. Current guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework points to runtime visibility, ownership, and policy enforcement as the baseline.
NHIMG research shows why this matters operationally: in its AI Agents: The New Attack Surface report, only 52% of companies can track and audit the data their AI agents access, leaving the rest with a blind spot during investigation and compliance review. In practice, many security teams discover uncontrolled AI activity only after an agent has already accessed sensitive data or exposed credentials, rather than through intentional discovery.
How It Works in Practice
Effective discovery starts by combining cloud asset inventory, secrets scanning, workload telemetry, and identity mapping into one view. Security teams should classify every AI agent, orchestration service, connector, plugin, and service account that can invoke models or external tools. Each item should be tied to an owner, a business purpose, an access path, and the credentials or tokens it uses. For agentic systems, identity discovery must include the workload identity itself, not just the human who deployed it.
That is where static IAM checks fall short. An agent may behave differently on each task, so discovery has to capture runtime context: what prompt, tool, API, or dataset it touched; whether it used a short-lived token; and whether the action matched its intended scope. The CSA MAESTRO agentic AI threat modeling framework and MITRE ATLAS adversarial AI threat matrix both reinforce the need to map behavior, dependencies, and abuse paths, not just names in a CMDB.
Practically, teams should:
- Build an inventory of AI services, agents, connectors, and tool endpoints across cloud, SaaS, and dev environments.
- Map each identity to owner, secret source, token TTL, and permitted data domains.
- Flag any agent that can chain tools, call external APIs, or write to production systems without human approval.
- Correlate logs from model gateways, secret managers, CI/CD, and cloud IAM to detect drift.
NHIMG’s OWASP NHI Top 10 research and the NIST AI Risk Management Framework both support this shift toward identity-centered visibility. These controls tend to break down when agents are created dynamically in ephemeral developer environments because ownership, logs, and secrets often fragment before security can bind them together.
Common Variations and Edge Cases
Tighter discovery often increases operational overhead, requiring organisations to balance visibility against deployment speed and developer autonomy. That tradeoff is real, especially when teams run multiple agent frameworks, temporary sandboxes, or vendor-managed copilots. Best practice is evolving, but there is no universal standard for discovery depth yet.
Some environments need extra caution. In regulated or highly segmented networks, discovery must include shadow AI use, unmanaged OAuth grants, and service principals created outside standard onboarding. In multi-agent workflows, one agent may only appear low risk until it inherits another agent’s token or tool access. That is why discovery should distinguish between direct permissions and delegated reach. Where possible, compare inventory results with Moltbook AI agent keys breach lessons and the Ultimate Guide to NHIs — 2025 Outlook and Predictions to identify where static inventories miss living identities. The goal is not perfect certainty; it is fast detection of identity drift before an agent becomes an untracked path to sensitive systems.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, CSA MAESTRO and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A2 | Covers agent identity, tool access, and runtime abuse paths in autonomous systems. |
| CSA MAESTRO | TRM-1 | Maps agentic workflows and trust relationships needed for discovery and control. |
| NIST AI RMF | GOVERN | Requires accountability and oversight for AI systems, including agent inventories. |
| OWASP Non-Human Identity Top 10 | NHI-01 | Addresses discovery and inventory of non-human identities and their secrets. |
| NIST CSF 2.0 | ID.AM-1 | Asset management applies directly to AI agents, connectors, and service accounts. |
Maintain an up-to-date asset inventory that includes all AI-related identities and dependencies.
Related resources from NHI Mgmt Group
- How should security teams reduce human approval for agentic AI without losing control?
- How should security teams use agentic AI to validate exposures without losing human control over risk decisions?
- How should security teams govern machine identity credentials in agentic AI environments?
- How should security teams implement zero trust for non-human identities in federal environments?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 27, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
