Asset inventory & ownership
Use system inventories, samples and interviews to identify locations, owners and purposes.
Data discovery & classification
Map where data resides, who owns it and how it moves, then connect classification to access, sharing and outbound protection.
Data discovery and classification
Validate data boundaries through systems, samples and ownership interviews, then translate classification into actionable protection requirements.
Discuss this approachData sources
Systems, files and shared workspaces
Discover & review
Detection rules with sample review
Classify & label
Purpose, sensitivity and ownership
Catalog & controls
A maintainable basis for protection
Key risks
- Data is dispersed across systems, files and shared workspaces.
- Inconsistent labels lead to inconsistent protection.
- Catalogs, owners and controls fall behind business changes.
Service scope
Classification rules
Define categories and sensitivity levels, including ambiguous examples and review procedures.
Detection validation
Validate detection within an agreed scope and refine false positives and missed matches through review.
Catalog-to-control mapping
Link labels to access, retention, sharing and outbound rules, with catalog updates and ownership handovers.
Delivery process
Define scope
Agree systems, data types, sampling boundaries and owners.
Define rules
Define labeling and review criteria, including how disputed labels are resolved.
Pilot & validate
Pilot discovery on representative data, review samples and adjust rules.
Handover & maintain
Deliver the catalog and control mappings with a process for additions and changes.
Deliverables
Scope, schedule and acceptance criteria are agreed following assessment.
Discuss this approachDeliverables to scope together
- Data asset inventory and ownership map
- Classification rules and sample-review records
- Protection mappings and catalog maintenance guidance
Relevant environments
- Starting a data governance program
- Consolidating data from multiple systems
- Designing sharing and outbound policies
Explore further
Data security risk assessment
Assess technical and organizational weaknesses across collection, storage, use, transfer, sharing and disposal, with prioritized remediation and verification.
Data masking and privacy
Design data minimization, masking and access protection for testing, analytics, sharing and display, validating usability and residual exposure.
Data governance
Align business ownership, data policies and technical controls, connecting discovery, assessment, loss prevention, masking and auditing into continuous improvement.