They often inherit broad permissions designed for convenience, not for machine behaviour. When access is provisioned like a generic application or user account, the system can reach more data than its task truly requires. The result is predictable over-access, weak accountability, and a review process that discovers the problem only after data has already been touched.
Why This Matters for Security Teams
AI deployments over-access data when identity, authorization, and task scope are treated like generic software provisioning instead of runtime-controlled machine behaviour. That breaks least privilege fast, because an AI agent or model-connected workflow can chain tools, follow prompts in unexpected ways, and reach data that was never needed for the task. The OWASP Non-Human Identity Top 10 calls out the risk of excessive privilege and weak lifecycle control for machine identities, which is exactly where many AI deployments drift. NHIMG research also shows how quickly exposed access becomes active abuse in practice, as highlighted in the
Related resources from NHI Mgmt Group
- What breaks when an app relies on a hidden token broker for external data access?
- When should organisations re-evaluate database access controls for AI workloads?
- Should teams use separate controls for database metadata access and data access?
- What should security teams do when enterprise agents need broad data access?
