They should start with a unified inventory of sensitive data and the tools that can reach it. That gives the organisation a baseline for reviewing overexposure, shadow AI, and policy gaps before expanding use. Once that foundation exists, access governance and monitoring become much easier to operationalise.
Why This Matters for Security Teams
When AI adoption accelerates, IAM teams are no longer governing only human users and service accounts. They are now dealing with agents, embedded copilots, and automations that can discover data, call tools, and chain actions faster than conventional review cycles can keep up. That shifts the first priority from expanding access to understanding where sensitive data lives and which systems can reach it, as reflected in the NIST Cybersecurity Framework 2.0 focus on inventory, protection, and governance.
This matters because AI exposure is usually not a single control failure. It is a combination of over-permissioned tools, shadow AI usage, and credentials that were never intended to reach high-value data. NHIMG research on the DeepSeek breach shows how quickly embedded secrets and exposed records can widen the blast radius once AI-linked systems are left ungoverned. In practice, many security teams discover the issue only after an agent, plugin, or copied credential has already touched data that should never have been in scope.
How It Works in Practice
The best first move is to build a unified inventory that connects three things: sensitive data, the systems that store or process it, and the AI tools that can reach it. That inventory should include sanctioned copilots, internal agents, third-party integrations, and any API keys, tokens, or service accounts those tools use. Without that map, IAM policy reviews become theoretical because no one can say which access paths matter most.
From there, teams can rank exposure by business criticality and access depth. A practical approach is to identify where AI tools can read, transform, export, or act on data, then compare that reality with intended permissions. This is where many organisations discover that a low-friction deployment has quietly bypassed existing controls. The 2026 Infrastructure Identity Survey found that 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, and 70% grant AI systems more access than they would give a human employee doing the same job.
- Inventory sensitive data by class, owner, and system of record.
- Map every AI tool, agent, connector, and automation to the data and APIs it can reach.
- Flag shadow AI and unmanaged integrations before expanding permissions.
- Review static credentials, overbroad roles, and stale tokens tied to AI workflows.
- Use the inventory to drive least-privilege redesign and monitoring priorities.
Once that baseline exists, access governance becomes more operational: IAM can decide where to apply role tightening, where just-in-time access is justified, and where human approval must remain in the loop. The practical goal is not to block AI adoption, but to stop unknown access paths from becoming the default control model. These controls tend to break down in fast-moving platform engineering environments because new AI connectors are often deployed faster than security can update the inventory.
Common Variations and Edge Cases
Tighter inventory and access review often increases rollout friction, requiring organisations to balance speed of AI adoption against the overhead of classification and entitlement cleanup. That tradeoff is real, especially when teams are under pressure to pilot multiple copilots at once. Current guidance suggests starting with the highest-risk data sets and the most privileged tools first, rather than trying to catalogue every low-risk automation on day one.
There is no universal standard for exactly how much AI-specific context should be attached to IAM records yet. Some organisations track only the tool and its credentials, while others also record prompt pathways, action scopes, and downstream data destinations. Best practice is evolving toward richer context because the same agent can behave differently depending on the task. NHIMG research on Azure Key Vault privilege escalation exposure highlights why indirect access paths matter just as much as direct grants.
Security teams should also account for exceptions such as emergency automations, developer sandboxes, and vendor-managed AI services. Those environments often look low risk until they are connected to production data or reused with elevated credentials. The first-pass inventory should therefore capture both sanctioned and informal AI use, even if some entries are imperfect at the start.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Inventory and control non-human identities before AI tools reach sensitive data. |
| OWASP Agentic AI Top 10 | A-02 | Agentic systems need runtime governance once AI tools can act on data. |
| NIST AI RMF | AI risk governance starts with understanding data, access, and system context. |
Create a complete register of AI-linked NHIs, then scope each identity to the minimum required data and API access.
Related resources from NHI Mgmt Group
- How should security teams handle risks from AI browser extensions?
- How should security teams govern API keys used for generative AI access?
- How do security teams decide which legacy systems to retire first?
- How should security teams prioritise data security investment across IAM and governance programmes?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
