Data security

Overshared documents, unclear ownership, and inconsistent handling make sensitive information difficult to protect. Establish practical data boundaries that support collaboration and AI adoption without treating every document or business process the same.

Capabilities

Identify sensitive repositories, accountable owners, excess access, and external-sharing patterns.
Define classifications and sensitivity labels around business handling needs and user workflows.
Design supported data loss prevention policies, starting with observation and controlled enforcement.
Align retention, audit, investigation, and insider-risk requirements with legal and privacy owners.
Review how assistants access protected information and where workload-specific controls differ.

Deliverables

A repository risk assessment identifying priority data, exposure paths, and remediation owners.
A classification and handling model with label definitions, examples, and ownership.
A policy rollout plan covering test cases, user impact, exceptions, and enforcement gates.
A control-to-evidence matrix and operating procedures for reviews, investigations, and policy changes.

Business value

Make protection decisions explainable and reduce avoidable oversharing. Measure classification coverage, excess access, policy effectiveness, and exception resolution while maintaining agreed collaboration needs. Configuration supports compliance processes; it does not establish compliance by itself.

Start with a data protection and readiness assessment.

Bring priority repositories, information owners, sharing patterns, and applicable obligations. Begin with a representative data scope and policy pilot; confirm legal, privacy, and licensing requirements before enforcement.

Purview, Microsoft 365, Entra, Fabric