Who sees the logs?
Access to logs should itself be restricted and audited, because logs can contain sensitive content.
AI Orchestration · Data and governance
Data Access and Governance in an AI Orchestration Layer
Orchestration is where data protection and accountability are enforced for every AI request.
Permission-aware retrieval
Search results must respect the access rights of the user or agent making the request; indexing must not bypass source permissions.
Data classes
Classify data - public, internal, confidential, personal - and map each class to allowed models, regions and retention periods.
Logging and retention
Log enough to explain results and investigate incidents, but minimise personal data and define retention periods.
Approvals and audits
High-impact actions require approval; regular audits check policies, exceptions and quality metrics.
Preparing documents for retrieval
Retrieval quality depends on the documents: remove outdated versions, keep metadata such as owner, date and validity, split long documents into meaningful sections and maintain the index when sources change. A small set of well-maintained sources usually beats a large, unreviewed archive.
Evaluating grounded answers
Test whether answers cite the right sources, whether they refuse when no source exists and whether they respect permissions. Include questions that a user should not be able to answer from their own access rights.
Regulatory context
Data protection law, sector rules and emerging AI regulation require transparency about automated processing, risk assessment and documentation. A central orchestration layer makes it easier to produce this documentation consistently across use cases.
Frequently asked questions
How is personal data protected?
Through data minimisation, masking where possible, allowed-model rules per data class and defined retention.