Permissions & Governance for Document AI
Role permissions, source approvals, retention and audit trails as part of the AI application.
Permissions & Governance protect every AI answer
This page describes how custom document AI handles permissions and governance inside the workflow. The application checks permissions before retrieval and answer generation, separates approved sources from raw collections and logs who approved a source, answer or escalation.
Governance starts before the first answer
Document AI should not check permissions only after an answer has been generated. Roles, teams, tenants, document types and approval status are evaluated before retrieval.
In practice, HR documents, contract drafts, finance data or privacy requests remain visible only to entitled people. AI gets no technical shortcut around existing permissions.
Approvals, blocks and rule changes stay traceable
Sources can be approved, deferred, blocked or removed after retention expires. Sensitive answers can require a four-eye review.
The audit trail records which source was used, which rule version applied and who made a decision. This matters for legal, privacy, internal audit and operational follow-up.
AI, data and approvals in Permissions & Governance for Document AI
AI in this module is source-grounded. It does not search files indiscriminately; it uses approved documents, role permissions, metadata and business rules. Permissions before retrieval, Retention and blocking rules, Approval workflows, Audit trail become a controlled process: AI finds evidence, marks uncertainty, shows source passages and stops when human review is required.
Risky cases need explicit stop points: low model confidence, missing sources, permission conflicts, cost impact or customer-facing communication enter a review queue. That keeps speed high without giving up control, traceability or privacy.
Which data and integrations the module needs
For Permissions & Governance for Document AI to work in daily operations, the data currently scattered across spreadsheets, email, business systems and file stores has to be modelled properly. The core inputs are roles, status values, deadlines, documents, comments, owners and the rules behind Permissions before retrieval and Retention and blocking rules.
A custom build connects that data to existing systems instead of forcing teams to maintain it twice: ERP, accounting, DMS, Microsoft 365, email, ticketing systems or mobile apps can be connected depending on the process. The goal is not the longest integration list; it is a clear source of truth.
Why a custom build can beat standard software here
Standard software starts faster and can be the right choice for simple workflows. A custom solution becomes stronger when Permissions & Governance for Document AI has to fit exact roles, data ownership, approval paths, hosting requirements and internal exceptions. Then process fit matters as much as feature count.
The honest downside: a custom build needs more discovery, rollout work and prioritisation at the beginning. The upside comes afterwards: fewer workarounds, no per-seat logic, controllable hosting, owned source code and modules that can grow as requirements change.
What this solution covers
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Permissions before retrieval
AI searches only sources approved for the user role.
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Retention and blocking rules
Blocked, expired or deleted documents are no longer used for answers.
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Approval workflows
Sources, risky answers and rule changes go to defined owners.
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Audit trail
Answer, source, rule version and approval remain traceable.
Frequently asked questions
Can AI reuse permissions from Microsoft 365 or a DMS?
Yes, when the systems expose permissions reliably. Retrieval then checks which sources the current person may access.
How are deleted or blocked documents handled?
They are removed from or blocked in the index and are no longer available for answers, graph nodes or reviews.
Does every answer need manual approval?
No. Approval is planned for risky document types, uncertain results, rule changes or defined escalation cases.