How should security teams inventory AI agents across SaaS, cloud, and low-code platforms?

Why Security Teams Need an Agent-First Inventory

Inventorying AI agents is not the same as listing apps or service accounts. Agents are autonomous, goal-driven workloads that can chain tools, reuse credentials, and expand their own reach across SaaS, cloud, and low-code systems. That means the inventory has to capture who owns the agent, what it can touch, which data it can see, and when it is allowed to act. Static RBAC alone rarely gives that picture. Current guidance suggests building inventories around workload identity, execution scope, and data access, then layering policy on top of that baseline.

That matters because agentic systems are already crossing intended boundaries at scale. NHIMG research shows AI Agents: The New Attack Surface report found that 80% of organisations say agents have already acted beyond intended scope, while only 52% can track and audit the data those agents access. For teams formalising governance, the right model is closer to OWASP Agentic AI Top 10 and NIST AI Risk Management Framework than a traditional CMDB. In practice, many security teams discover shadow agents only after a stale token, overshared connector, or copied prompt flow has already touched production data.

How to Build the Inventory Across SaaS, Cloud, and Low-Code

Start with identity, then work outward. Every agent should map to a cryptographic workload identity, an owner, an execution boundary, and a defined business purpose. If an agent runs through a SaaS automation platform, cloud function, orchestration layer, or low-code builder, record the connector, token type, policy source, and downstream systems it can invoke. This is where intent-based authorisation becomes useful: the decision is made at runtime based on what the agent is trying to do, not just what role it inherited months ago.

A practical inventory should include the following fields:

• Agent name, business owner, and technical steward
• Platform of record, such as SaaS workflow, cloud runtime, or low-code environment
• Workload identity, token type, and whether secrets are static or JIT-issued
• Permissions, data sources, and tools the agent can call
• Approved intent, execution scope, and revocation path
• Logging, alerting, and audit coverage for every action

Use a single control plane where possible, but do not assume platform-native reports are complete. Many teams supplement discovery with CSA MAESTRO agentic AI threat modeling framework and the NIST AI Risk Management Framework to classify agent behaviour, while using OWASP NHI Top 10 to spot over-privileged connectors, secret sprawl, and broken access boundaries. Link each record back to one system of ownership, then continuously compare actual calls against approved intent. These controls tend to break down when low-code builders spawn hidden subflows because the parent app often masks the real identity, privilege chain, and data path.

Where Inventorying Breaks Down in Real Environments

Tighter inventory control often increases operational overhead, requiring organisations to balance visibility against deployment speed. That tradeoff is real in environments with dozens of SaaS apps, rapidly changing cloud workloads, and citizen developers creating automations outside central IT. Best practice is evolving, but there is no universal standard for this yet, especially on how to model agent sub-identities inside nested workflows.

Edge cases usually appear in three places. First, agents that inherit a human user session can look legitimate in logs while actually acting autonomously, so the inventory needs to distinguish human delegation from machine autonomy. Second, systems that rely on long-lived API keys make revocation slow and blind; JIT credentials and short TTL secrets are far safer for task-bound activity. Third, cross-platform agents often exceed the scope of a single vendor dashboard, so teams need a federated view that spans SaaS connectors, cloud service accounts, and low-code logic in one register. NHIMG’s Salesloft OAuth token breach is a reminder that token theft can turn one connector into broad data exposure, while the Snowflake breach illustrates how quickly access scope becomes the real attack surface. In mature environments, the inventory is never a one-time spreadsheet; it is a living control that must update when an agent changes tools, permissions, or mission.

Related resources from NHI Mgmt Group

• How should enterprises govern AI agents across multiple clouds and SaaS platforms?
• How should security teams manage permissions for AI agents?
• How should security teams govern AI agents that use OAuth access?
• How should security teams limit the risk from AI agents that have access to production systems?