# Purview Information Governance

> Guidance for an end-to-end Microsoft Purview information governance / information protection strategy - sequencing classification, labelling, protection, lifecycle, and risk into a coherent program aligned to the Zero Trust data pillar. Covers maturity-based rollout sequencing, ownership, and metrics. WHEN: information governance, information protection strategy, data protection program, sequence Purview rollout, MIP strategy, protect and govern data end to end, Zero Trust data pillar program, Purview roadmap, data security maturity.

- Skill: `vinayaklatthe/purview-information-governance` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vinayaklatthe/purview-information-governance`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vinayaklatthe/purview-information-governance/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- License: MIT
- Author: vinayaklatthe (https://skillmd.com/u/vinayaklatthe)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vinayaklatthe/purview-information-governance

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# Microsoft Purview Information Governance (Strategy)

Information governance ties Purview classification, protection, lifecycle, and insider risk into
one coherent program so sensitive data is **known, protected, governed, and defensibly retained** -
anchored to the Zero Trust **Data** pillar. This skill is about sequencing, not feature deep-dives.

## When to use
Planning a multi-workstream Purview rollout, building a maturity roadmap, or rationalising an
in-flight deployment that is delivering features in isolation rather than outcomes.

Do not use this skill for individual feature configuration - jump to the specific Purview skill
(labels, DLP, lifecycle, IRM, DSPM for AI) once the sequencing question is settled.

## Pick the right entry point for the program
| Current state | Start here |
|---|---|
| No classification, no labels | Phase 1: Know - SITs + sensitivity label taxonomy in audit mode |
| Labels exist but adoption is low | Phase 1.5: auto-labelling in simulation + user training |
| Labels adopted, no DLP | Phase 2: Protect - DLP per workload in simulation, then enforce |
| DLP live, no retention | Phase 3: Govern - retention policies + records (file plan) |
| All of the above, Copilot rolling out | Phase 4: Manage AI risk - DSPM for AI + oversharing remediation |
| All above + insider concerns | Phase 5: IRM + Adaptive Protection |

Rule of thumb: each phase requires the previous one to be at "audit/simulation works" maturity.
You cannot block what you cannot classify.

## Approach
1. **Know your data** - Deploy classification: SITs, trainable classifiers, EDM, and use
   Content/Activity Explorer to understand what sensitive data exists and where.
   *Verify: Content Explorer shows non-trivial counts for your top SITs across SharePoint/OneDrive/Exchange.*
2. **Protect your data** - Roll out a **sensitivity label** taxonomy with marking/encryption,
   then layer **DLP** to prevent exfiltration of labelled/SIT content.
   *Verify: top-tier label adoption visible; DLP audit-mode policies producing real (not noise) matches.*
3. **Govern your data** - Apply **retention and records** policies for defensible retain/delete;
   prefer adaptive scopes so policies stay current.
   *Verify: retention policies show coverage stats per workload; disposition review running.*
4. **Manage risk** - Add Insider Risk Management and Communication Compliance for people-centric
   risk; add DSPM for AI as Copilot/genAI adoption grows.
   *Verify: IRM alerts being triaged; DSPM for AI recommendations being actioned.*
5. **Operate as a program** - Establish stewardship per business unit, monthly review cadences,
   and a metrics dashboard (coverage, label adoption, DLP incident trends, time-to-disposition).
   *Verify: a named exec owner reviews metrics quarterly and approves the next-phase scope.*

## Guardrails
- Don't start with enforcement - classification maturity must precede DLP blocking and
  auto-labelling; otherwise you will tune false positives in production.
- Govern by data sensitivity and regulation, not by what's licensed - inventory regulatory
  obligations first (GDPR, HIPAA, PCI, sector rules) and map controls back.
- Treat it as a program with owners and change management, not a one-off configuration - feature
  toggles without business sponsorship will stall at pilot.
- Pair every label/DLP rollout with user-facing communication; silent enforcement breeds
  workarounds.
- Resist scope creep: ship Phase 1 before designing Phase 4. A working Phase 1 unlocks everything.

## Common anti-patterns
- Buying E5, enabling everything, and calling that a "strategy".
- Building a 9-tier label taxonomy nobody can apply correctly.
- Deploying DLP in block mode on day one - generates incidents and political damage.
- Treating Purview as IT-owned with no business data owners or stewards.
- Skipping metrics; without coverage data, leadership cannot fund the next phase.

## Example prompts
- `Sequence a Microsoft Purview information protection rollout.`
- `Design a data protection program for the Zero Trust data pillar.`
- `How do I protect and govern data end to end with Purview?`
- `Plan an MIP strategy across classify, protect, and govern.`
- `What is the right Purview phase to start if we have no labels today?`

## Microsoft Learn
- Protect & govern data with Purview: https://learn.microsoft.com/purview/purview
- Information protection: https://learn.microsoft.com/purview/information-protection
- MIP deployment guidance: https://learn.microsoft.com/purview/information-protection-solution
- Zero Trust data pillar: https://learn.microsoft.com/security/zero-trust/deploy/data
- Data lifecycle management: https://learn.microsoft.com/purview/data-lifecycle-management
- Insider Risk Management: https://learn.microsoft.com/purview/insider-risk-management