AI Data Governance
AI data governance that keeps your warehouse
clean, classified, and audit-ready.
It watches. You approve the fixes.
An AI agent that continuously classifies data, monitors quality across your warehouse, flags access drift, keeps the catalog current, and assembles compliance evidence — with a human approval step before any policy, permission, or record is changed.
Code 3MONTHFHALF50% off your first 3 months on Cloud. Applied automatically at checkout.
- Not quarterly audits
- Always-on
- You approve every change
- Review-first
- Warehouse, dbt, catalog
- Your data stack
- Your AI keys
- BYOK
How a day runs
Yesterday's schema changes, today's flagged risks and evidence.
Every morning the agent turns overnight data movement into classified, checked, audit-ready findings.
- 01
Scan
New tables, schema drift, failed dbt tests, permission changes
- 02
Classify & check
PII tagging, quality issues, anomalies, access drift
- 03
Review
You approve, edit, or dismiss each finding — in one place
- 04
Apply / log
Catalog updates, tickets, and evidence packs are written back
What the AI data governance agent does
A governance team's daily motion — classified, checked, handed off.
It doesn't wait for the quarterly audit. It watches the warehouse continuously, prepares the findings and evidence, and gives you a clean handoff with a human checkpoint.
Classify data & tag PII
Profiles new and changed tables, tags PII and sensitive fields, and maps what each column holds against your classification policy.
Monitor data quality
Watches for nulls, duplicates, schema drift, and stale freshness across critical tables and dbt models — before downstream users hit it.
Detect anomalies
Flags unexpected row-count swings, distribution shifts, and broken tests that signal a pipeline or source problem.
Review access & flag drift
Compares live permissions against policy and prior grants, surfacing over-broad access and roles that drifted since the last review.
Keep the catalog current
Drafts and updates table, column, and lineage documentation so the catalog reflects the warehouse as it actually is today.
Prep compliance evidence
Assembles GDPR, SOC 2, and HIPAA evidence — control status, data maps, access reviews — ready for your auditor and your sign-off.
Setup
Connected to your data stack in minutes.
No engineering sprint. Connect your warehouse and catalog, point it at your policies, and set the approval rules you're comfortable with.
- 01
Connect warehouse, dbt, and catalog
Link Snowflake, BigQuery, or Databricks, your dbt models and tests, and your catalog. The agent reads schemas and context — no code, no pipeline rebuild.
- 02
Set policies and approval rules
Point it at your classification, quality, and access policies, and decide what the agent can do on its own versus what needs your sign-off.
- 03
Review and apply
Each day it surfaces classified findings, catalog edits, and evidence. You approve in one place; it writes updates and drafts tickets.
What it observes
Inputs the agent reads in.
It works from your real data estate — not a generic checklist.
Warehouse tables & schemas
Table structures, column types, row counts, and freshness from Snowflake, BigQuery, or Databricks.
dbt models & tests
Model definitions, test results, and documentation to understand transformations and where quality checks pass or fail.
Access & permission configs
Roles, grants, and row/column policies to compare live access against what your policy allows.
The data catalog
Existing table and column docs, owners, and tags so it updates rather than duplicates what's already documented.
Prior audit findings
Past issues, remediations, and open items so it tracks what's resolved and what's still outstanding.
Policy definitions
Classification, retention, quality, and regulatory rules that define what 'compliant' means for your team.
What it produces
Outputs the agent hands you.
Finished, reviewable findings — not raw alerts you have to triage from scratch.
Classification & PII maps
A clear map of where sensitive and regulated data lives, tagged by column and mapped to your classification policy.
Data-quality issue reports
Nulls, duplicates, schema drift, and freshness gaps, each scored by severity and pointed at the affected model.
Anomaly & access-drift alerts
Row-count and distribution anomalies plus over-broad or drifted permissions, ready for your review.
Updated catalog docs
Draft descriptions, ownership, and lineage edits that bring the catalog back in sync with the warehouse.
Compliance evidence packs & tickets
GDPR, SOC 2, and HIPAA evidence bundles plus drafted remediation tickets for each open data-quality issue.
Works with your data stack
- Snowflake
- Google BigQuery
- Databricks
- dbt
- PostgreSQL
- Airtable
- Notion
- Slack
Human approval boundary
It prepares the findings. You decide what changes.
The agent never alters data, permissions, or policy on its own unless you allow it. By default, anything that touches access, records, or the catalog waits for you.
Runs on its own
- Scans tables, schemas, and dbt tests for issues
- Classifies data and tags PII
- Detects anomalies and access drift
- Drafts catalog docs, evidence, and remediation tickets
Waits for your approval
- Changing access policies or permissions
- Quarantining or altering data
- Committing catalog or policy changes
- Closing an audit or remediation item
Use cases
Where teams put the governance agent to work.
Audit readiness
Keep GDPR, SOC 2, and HIPAA evidence current year-round so audits are a review, not a fire drill.
PII discovery
Find and tag sensitive data the moment it lands, so nothing regulated slips into the warehouse untracked.
Quality monitoring
Catch schema drift, nulls, and freshness gaps before dashboards and models silently break.
Access reviews
Turn the dreaded periodic access review into a continuous, drafted diff against policy.
AI governance agent vs a scanning tool vs manual audits
Not another dashboard of alerts. A teammate that does the review.
Scanning tools raise alerts you still have to triage. Manual audits are thorough but only happen quarterly. An AI governance agent watches continuously, prepares the findings and evidence — and stops at your approval line.
| Factor | Scanner / manual audit | Orchestra AI data governance |
|---|---|---|
| Coverage cadence | Quarterly, or noisy real-time alerts | Continuous, prioritized findings |
| Classifies data | Rules you hand-tune | Profiles and tags against your policy |
| Keeps the catalog current | Manual, drifts fast | Drafted after every change |
| Human approval | All-or-nothing automation | Built-in review before any change |
| Audit evidence | Assembled by hand each cycle | Kept current and ready |
| Cost | Analyst headcount or per-seat SaaS | Flat monthly fee |
FAQ
Common questions about AI data governance.
What is an AI data governance agent?
It's an AI agent that continuously handles the governance motion — classifying data and tagging PII, monitoring data quality, flagging access drift, keeping the catalog current, and preparing compliance evidence. Orchestra's agent is review-first: it prepares every finding and a human approves anything that changes data, permissions, policy, or the catalog.
Is this just another data-quality scanner?
No. Scanners raise alerts that your team still has to interpret, prioritize, and act on. The agent reads your real context — warehouse schemas, dbt tests, policies, prior audit findings — scores each issue by severity, drafts the remediation ticket or catalog fix, and hands it to you for approval. It's the triage and the drafting, not just the alerting.
Can it change our data or permissions on its own?
Only if you let it. By default the agent classifies, monitors, and drafts, then waits for your approval before changing any access policy, quarantining or altering data, or committing catalog and policy changes. You set the boundary — fully review-first, or auto-apply for specific low-risk actions.
Which regulations and frameworks does it support?
It assembles evidence for GDPR, SOC 2, and HIPAA — control status, data maps, PII inventories, and access reviews — mapped to your own policy definitions. Because it reads your actual warehouse and access configs, the evidence reflects the real state of your data, not a static questionnaire.
What does it connect to?
Snowflake, BigQuery, and Databricks for your warehouse, dbt for models and tests, PostgreSQL for operational data, Airtable and Notion for catalogs and policies, and Slack for findings, alerts, and approvals. It reads schemas and configs directly — no pipeline rebuild.
Keep exploring
Pair data governance with the rest of your team.
Turn constant audits into continuous, approved governance.
Spin up a free account and deploy your AI data governance agent — or book a demo and we'll set it up with you.
No code. Human approval before anything changes data, access, or the catalog. Cancel any time.