1. Understand the Purview Security and Governance System
Map the Purview Landscape and Its Security Boundaries Translate Licensing, Roles, and Governance into an Operating Model
2. Discover, Map, and Curate the Data Estate
Design Data Map Scanning and Metadata Collection Build Unified Catalog, Lineage, Quality, and Data Products
3. Classify and Protect Information
Engineer Sensitive Information Types and Classifiers Design Sensitivity Labels, Publishing, and Auto-Labeling
4. Prevent Unsafe Data Movement
Design DLP Policies from Business Scenarios Extend DLP to Endpoints, Browsers, Teams, and AI
5. Govern the Information Lifecycle
Design Retention Policies and Labels Operate Records, Events, Disposition, and Legal Holds
6. Investigate and Preserve Evidence
Use Purview Audit as Evidence Run eDiscovery Cases, Holds, Searches, and Reviews
7. Manage Human, Communication, and Compliance Risk
Operate Insider Risk and Communication Compliance Responsibly Use Information Barriers and Compliance Manager as Governed Controls
8. Protect Privacy, SharePoint, Microsoft 365, and AI
Secure SharePoint and Microsoft 365 Collaboration Paths Govern Microsoft 365 Copilot and Other Generative AI Protect Privacy and Support Data-Subject Workflows
9. Integrate, Report, and Operate Purview
Integrate Scanners, APIs, Reporting, and Multi-Cloud Sources Run Purview as a Production Security Service Turn DSPM Findings into Data Security Investigations
10. Design and Prove a Complete Purview Program
Build the Purview Target Architecture and Roadmap Capstone: Prove the Security Layer End to End
7. Manage Human, Communication, and Compliance Risk

Operate Insider Risk and Communication Compliance Responsibly

Use indicators and communication policies with proportionality, privacy, separation of duties, and human review.

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In this lesson, you will learn to:

  • Apply a repeatable method for operate insider risk and communication compliance responsibly in a licensed, governed, and testable Purview environment.

Operate Insider Risk and Communication Compliance Responsibly

This lesson develops a practical understanding of operate insider risk and communication compliance responsibly and connects design choices to supported capabilities, operational dependencies, user impact, and verifiable evidence.

Risk signals are leads, not verdicts

Insider Risk Management combines supported indicators to surface potentially risky activity such as unusual data movement or policy violations. Communication Compliance can identify messages that may require review under company policy. Neither system proves intent, misconduct, or legal liability. Their purpose is to help authorized reviewers assess context through a controlled workflow.

Begin with an approved risk scenario, population, legal basis, privacy assessment, indicators, thresholds, reviewers, and response options. Use pseudonymization and role separation where appropriate. Limit collection and review to what the purpose requires. Involve HR, Legal, Privacy, Security, and employee representation according to jurisdiction and company policy.

A person who downloads many files may be migrating approved work, responding to an incident, or preparing to leave with intellectual property. Evidence must distinguish these explanations before action.

Design a case process that protects both the company and the person

Document triage criteria, enrichment sources, reviewer access, escalation thresholds, conflicts of interest, evidence retention, and appeal or correction processes. Reviewers should record observed activity, business context, alternative explanations, confidence, and the policy basis for next steps. Avoid copying sensitive content into informal channels.

Measure signal quality, cases opened, cases closed without action, review time, repeated false positives, privacy complaints, and control changes—not employee “risk scores” in isolation. A threshold that creates many harmless cases may need better context, while a quiet queue may indicate missing prerequisites rather than low risk.

For AI use, distinguish accidental sensitive disclosure, policy misunderstanding, prompt injection, and deliberate exfiltration. The DSPM for AI guide helps connect technical AI signals to an owned remediation without converting recommendations into judgments about people.

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