Does an AI agent plugged into your internal documents respect your access rights?

In brief.
To answer quickly, an enterprise AI agent indexes internal documents in a search engine. If that index does not faithfully reproduce each user’s access rights, the agent becomes a skeleton key: it returns to whoever queries it information that person could never have opened themselves. The classic example: a salesperson who asks the right question and obtains elements of the M&A file. This is one of the most serious blind spots in internal projects.

An architectural detail with major consequences

Rights live in your source systems: document management, email, team workspaces. The semantic index is a transformed copy of that content. If permissions are not propagated to that copy, then checked at each query for the user asking, the original partitioning disappears. And it has to be maintained permanently: rights change every day — arrivals, departures, confidential projects.

The subtleties that trip prototypes up

Documents with mixed rights (a confidential annex inside a public file); derived content — summaries and extracts generated by the AI — which rarely inherits the restrictions of its source; conversation logs, which may contain sensitive information wrongly returned and themselves become data to be protected. Designing, auditing and certifying that partitioning end to end is a multi-year undertaking. A simple test to hand to your CISO: create a user with restricted rights and ask them the awkward questions.

The Optivalue.ai approach

Optivalue.ai applies rights at the moment of each query, logs who obtained what, and its private-AI deployment per client avoids the additional risk of pooling: your content never sits alongside another organisation’s.

Why is the search index a problem?
Because it is a copy of the content: if it does not inherit permissions, it bypasses them.

‍Must generated answers be protected too?
Yes: summaries, extracts and logs inherit the sensitivity of their sources.

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