Protecting AI applications starts with data and access
Connect retrieval permissions, tool boundaries and observability to keep AI within a defined scope.
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Key takeaways
- Define permitted data and tools for each role.
- Check permissions at retrieval and action boundaries.
- Keep references, state and human handoffs explicit.
Start with the intended scope
A document assistant and a record-updating agent need different permissions. Describe users, inputs and actions before choosing tools. Associate each permission with an agreed task.
Map where information enters the system, where it is retained and where an output can change another system.
Enforce access at retrieval
The interface is only one layer. Check the current user’s access before passing documents into model context. Preserve source and version information for review.
When suitable evidence is unavailable, the system should communicate that limitation and offer a responsible human review.
Bound tools by action
Separate read-only tools from data-changing tools. Validate inputs, target identity and authorization in the service. Actions requiring approval should remain pending until an authorized user confirms them.
Retrieved document content is reference data, not an instruction that grants tool permissions.
Turn perspective into your next step.
Test out-of-scope cases too
Evaluate valid requests, insufficient permissions, conflicting documents and tool failures. Inspect answers and the actions that follow.
Compare logs with actual system state to detect cases where the interface reports completion before a tool has succeeded.
Maintain quality after launch
Changes to documents, permissions and tools can affect outcomes. Retain evaluation cases, configuration versions and ownership so changes can be checked.
AQPTech proposes beginning with one user group and a focused data scope, then defining feedback review, updates and operating handover.
