Microsoft Purview Data Protection Assessment & Remediation
Assess classification, sensitivity labels, DLP, collaboration and information lifecycle controls across Microsoft Purview.
Data protection requires more than applying labels to documents.
Classification, sensitivity, data loss prevention, sharing, retention, endpoint controls and operational processes must work together without making normal collaboration impractical.
This assessment examines how Microsoft Purview controls protect information across Microsoft 365, endpoints and supported data locations.
What we assess
Information classification
- Sensitive information types
- Trainable classifiers
- Organizational data categories
- Classification strategy
- Detection accuracy
- Business context
Sensitivity labels
- Label taxonomy
- Publishing
- User experience
- Protection settings
- Encryption
- Content marking
- Container labels
- Label lifecycle
Auto-labeling
- Client-side recommendations
- Service-side auto-labeling
- Policy scope
- Detection criteria
- Simulation
- False-positive handling
Data Loss Prevention
- Exchange Online
- SharePoint Online
- OneDrive
- Microsoft Teams
- Endpoint DLP
- Policy scope
- Exceptions
- User notifications
Sharing and collaboration
- External sharing
- Guest access
- Information protection
- Collaboration boundaries
- Download and access controls
Retention and lifecycle
- Retention policies
- Retention labels
- Records requirements
- Data lifecycle
- Deletion requirements
Operations
- Policy ownership
- Incident handling
- Exception management
- User education
- Reporting
- Change management
What you receive
- Data-protection current-state assessment
- Classification and labeling review
- DLP findings
- Policy-overlap analysis
- Prioritized remediation plan
- Target protection model
- Implementation backlog
- Validation plan
Example public finding
This is an illustrative example, not a finding from a customer assessment.
PDP-016 — Sensitivity labels exist but do not represent clear protection decisions
Observation
Multiple sensitivity labels are available to users, but the distinction between labels is based primarily on naming and organizational convention rather than clear differences in protection, sharing or handling requirements.
Why it matters
A complex label taxonomy increases user uncertainty and can lead to inconsistent classification without materially improving protection.
Recommended direction
Design labels around meaningful handling and protection decisions. Reduce unnecessary choices, align labels with real business data categories, and validate user behavior before expanding automated classification.
Public methodology
Public material may include:
- Data-protection domains
- Label-design principles
- High-level maturity model
- Sample classification model
- Sanitized findings
- Public readiness checklist
- Sample report
Environment-specific assessment details and remediation guidance are provided within the agreed project scope.