How do security teams know whether AI tools are creating unmanaged access paths?

Why This Matters for Security Teams

Unmanaged access paths matter because AI tools rarely behave like a single application account. They often sit between users, data stores, and downstream systems, which means they can inherit privilege, reuse tokens, or call APIs in ways that are easy to miss in traditional access reviews. That creates a blind spot for governance, incident response, and auditability, especially when the tool is embedded in a workflow rather than deployed as a standalone service.

Security teams should treat this as an identity problem as much as an application problem. The key question is not only whether the AI tool is useful, but whether its access is bounded, attributable, and reversible. The OWASP Non-Human Identity Top 10 is useful here because it highlights the risks that arise when machine identities, secrets, and permissions are not governed with the same discipline applied to human access.

In practice, many security teams encounter unmanaged ai access only after a data pull, code change, or workflow action has already occurred, rather than through intentional identity design.

How It Works in Practice

The operational test is simple: trace the AI tool from entry point to effect. Security teams need to identify what identity the tool uses, where that identity is stored, what resources it can reach, and whether each access path has an owner and expiry. If the tool can read from a knowledge base, push to a repository, or trigger operational actions, each permission should be mapped to a business purpose and a control owner.

That review should include both direct and indirect access. A prompt interface may look harmless while the underlying workflow uses a service account with broad repository access, a long-lived API key, or a delegated token with no rotation. Teams should verify whether the tool is constrained by scoped credentials, whether approvals are captured before access is granted, and whether logs show who initiated the action and which identity executed it. Where possible, align the control set to NIST Cybersecurity Framework 2.0 and the NIST SP 800-53 Rev 5 Security and Privacy Controls, especially access control, audit logging, and configuration management requirements.

• Inventory every AI tool that can initiate actions or retrieve protected data.
• Map the identity behind each tool, including service accounts, tokens, and delegated credentials.
• Check whether privileges are scoped to the minimum needed and time bounded where possible.
• Require an owner, approval record, and revocation path for every productive access path.
• Validate logs for traceability from user request to AI action to target system effect.

These controls tend to break down when AI capabilities are embedded inside low-code automations, because ownership, identity boundaries, and revocation steps are often spread across multiple platforms.

Common Variations and Edge Cases

Tighter access controls often increase workflow friction, requiring organisations to balance speed of automation against the need for explicit authorization and traceability. That tradeoff becomes more visible when teams use AI assistants for code, operations, or customer support, because the business wants fast action while security needs clear guardrails.

Current guidance suggests that not every AI feature needs the same level of control. A read-only summarisation tool may warrant lighter treatment than an agent that can execute commands, modify records, or provision resources. Best practice is evolving around this distinction, but there is no universal standard for it yet. The practical test is whether the AI can cross a trust boundary or create a durable change in another system. If it can, the access path should be treated like a non-human identity with explicit governance.

Edge cases include temporary pilots, shadow deployments, and vendor-managed AI features embedded in SaaS platforms. These are especially risky because the organisation may not control the underlying identity, the credential lifecycle, or the logging depth. Teams should also watch for shared tokens and inherited permissions, since those make it difficult to revoke one AI workflow without disrupting others. The OWASP Non-Human Identity Top 10 remains a strong reference point for these cases, particularly where secret handling and lifecycle control are weak.

In short, unmanaged access paths are most likely to hide in environments with rapid experimentation, delegated automation, and unclear operational ownership.

Related resources from NHI Mgmt Group

• How do security teams know whether AI access is actually working safely?
• How should security teams expose APIs to AI systems without creating unsafe access paths?
• How can teams tell whether AI experimentation is creating hidden access risk?
• How should security teams govern AI models that can call tools and access data?