Approved sources
Identify authoritative datasets, permitted content, source ownership and conditions that exclude data from the solution.
TRUST · DATA GOVERNANCE
AI data boundaries should be deliberate and auditable.
Reliable enterprise AI depends on knowing which sources are approved, who can access them, how data flows, what is retained and who owns exceptions.
Access & minimization
Restrict access to what the use case needs, separate roles and avoid exposing unnecessary sensitive information to models or downstream tools.
Retention & lineage
Define what is stored, for how long, where it moves and how important outputs can be traced back to relevant source or process evidence.
Ownership & review
Name owners for source quality, permissions, retention decisions, sensitive-data exceptions and periodic reassessment.
DATA REVIEW
Questions to clarify before connecting AI to enterprise data.
- Which systems and datasets are approved for this use case?
- Which sensitive or regulated data categories require additional handling?
- What information is sent to external services, if any?
- What is persisted, cached, logged or retained?
- Can important outputs be traced to relevant source evidence?
- Who approves changes to data access, sources and retention?
PRIVACY & REGULATORY CONTEXT
Data obligations vary by jurisdiction and use case.
Specific privacy, residency, retention and sector requirements should be validated by the responsible customer and qualified advisers for the applicable environment. This page describes general architecture and governance considerations.
MAP DATA BOUNDARIES