Where should governance start?
Choose a bounded domain or decision with a named owner. Follow its information from source to consumer, identify failures and permissions, and agree who resolves them.
TRAVINTO / DATA & AI GOVERNANCE
Trust needs an owner.
Governance needs to work.
Data and AI governance connecting ownership, quality, access, lineage, retention, evaluation, and human approval in everyday workflows.
THE DECISIONS BEHIND THE ARCHITECTURE
Start with the question.
Make the responsibility clear.
How does governance extend into AI and agents?
Carry source permissions, classifications, versions, and retention rules into retrieval and model context. Add model and workflow evaluation, tool permissions, approval boundaries, and traceable decisions.
How can governance become useful rather than paperwork?
Connect policies to operating actions: a failed quality check has an owner, denied access is enforced, a lineage gap is investigated, and an exception has a scope and expiry.
WORK AREAS & TYPICAL DELIVERABLES
Understand the scope.
See what it should produce.
These are engagement directions; selected responsibilities and deliverables are agreed for the problem in hand.
Ownership & stewardship
Scope & typical output
Define accountable domain owners, stewards, platform operators, model/workflow owners, and approving reviewers.
Typical output: A responsibility map and issue-resolution process.
Quality & issue management
Scope & typical output
Agree fit-for-purpose checks, thresholds, exception routing, remediation ownership, and consumer communication.
Typical output: A quality rule set, issue workflow, and acceptance criteria.
Catalog, glossary & lineage
Scope & typical output
Connect source authority, business meaning, transformations, classifications, and consumers. Prioritize the lineage needed to investigate a real issue.
Typical output: A discovery and lineage scope with governed definitions.
Access, classification & lifecycle
Scope & typical output
Define permitted purposes, identity scope, sharing, retention, deletion, and reviewable exceptions across source and derived information.
Typical output: An access model, lifecycle rules, and an exception register.
AI evaluation & agent boundaries
Scope & typical output
Test groundedness, access isolation, tool behavior, misuse, escalation, and reviewer acceptance on representative cases.
Typical output: An evaluation plan, tool permission matrix, and approval policy.
Governance operations & adoption
Scope & typical output
Define review cadence, escalation, training needs, operational evidence, and measures that trigger accountable action.
Typical output: An operating model, review agenda, and improvement backlog.
A CONNECTED REFERENCE VIEW
See the responsibilities.
Follow the dependencies.
ACCOUNTABILITY
Domain owner · Steward · Platform operator · Approver
INFORMATION CONTROLS
Quality · Classification · Access · Lineage · Retention
AI & WORKFLOW CONTROLS
Evidence · Evaluation · Tool scope · Human approval
OPERATING EVIDENCE
Issues · Exceptions · Decisions · Reviews · Recovery
Banking example / the policy has a date
The domain makes
the architecture concrete.
A servicing answer can be wrong even when its citation is genuine: it may use the wrong product, policy period, or account context. Governance needs to preserve applicability and access, and assign who resolves a conflicting source.
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WHAT A USEFUL ASSESSMENT CAN LEAVE BEHIND
A shared view.
A practical next decision.
01
Domain ownership & responsibility map
02
Quality and access control requirements
03
Lineage & classification priorities
04
AI/agent evaluation and approval rules
05
Issue, exception & review operating model
A USEFUL NEXT STEP
Bring one information flow that needs clearer accountability.
Bring a representative workflow, existing definitions or controls, permitted source examples, and the people who own the result.
Discuss your priorities ↗