Data Security implementation for generative AI: 8-Wk Imp

Background

Using generative AI (like Microsoft Copilot, ChatGPT, Gemini, etc.) introduces a number of sensitive data risks, especially when these tools interact with enterprise data, user input, or external APIs: • Data Leakage Through Prompts • Insecure Data Access by the AI • Retention of Sensitive Data by Third Parties • Inference Attacks or Prompt Injection • Output Containing Sensitive Data • Shadow AI Use (Unapproved AI apps) Microsoft Purview for Generative AI helps organizations govern, monitor, and secure the use of generative AI tools like Copilot, ChatGPT, or custom GPT applications, especially when these tools access or generate content based on sensitive enterprise data.

Project benefits

1. Prevent Data Leaks and Exposure Avoid accidental or intentional leakage of sensitive enterprise data into AI systems or external APIs.
2. Label and Protect Data Before AI Uses It Ensure only authorized users and apps (like Copilot) can access or process protected content.
3. Audit and Monitor AI Activity Gain full visibility into how AI interacts with enterprise data — critical for forensic analysis and compliance.
4. Support Regulatory and Compliance Requirements Proves compliance with regulatory bodies during audits and protects your organization from legal exposure.
5. Map Data Lineage and Usage in AI Workflows Understand how data moves from origin (e.g., SharePoint) to AI-generated output (e.g., PowerPoint via Copilot), and assess risk across the lifecycle.
6. Govern External and Shadow AI Tool Usage Stops risky employee behavior with unsanctioned or insecure AI systems.
7. Align with Responsible AI Practices

Delivery

The project consists of the following project phases: Phase 1: Initiation & Planning Objectives: • Define project scope, goals, and success criteria • Identify AI tools in use (Copilot, Azure OpenAI, external GPTs) • Conduct a risk assessment for generative AI use • Identify stakeholders: IT, compliance, data governance, legal, security Key Activities: • Conduct workshops with security, legal, and data owners • Create AI use case inventory • Assess existing Microsoft 365 / Purview capabilities • Define target compliance requirements (e.g., GDPR, HIPAA) Deliverables: • Project charter & stakeholder matrix • High-level roadmap • Risk and compliance analysis • Budget and resource allocation Phase 2: Discovery & Assessment Objectives: • Analyze current data estate and sensitivity • Understand how data interacts with AI systems • Identify shadow AI tools or ungoverned data flows Key Activities: • Enable Microsoft Purview Data Map (if licensed) • Scan data sources (SharePoint, OneDrive, Exchange, Azure Blob, etc.) • Inventory data classifications and sensitivity levels • Use Microsoft Defender for Cloud Apps (MCAS) to detect shadow AI Deliverables: • Data inventory report • Shadow AI usage report • Sensitivity labeling baseline • Assessment of data protection gaps Phase 3: Design & Policy Definition Objectives: • Design data governance architecture for AI tools • Define labeling and DLP policies to protect AI input/output • Align with Responsible AI and regulatory guidelines Key Activities: • Create or update sensitivity labels (e.g., “Highly Confidential”) • Define DLP policies for endpoint, cloud, and Copilot traffic • Design audit strategy for AI activity monitoring • Build AI access matrix (who can prompt what data) Deliverables: • Governance architecture diagram • Label taxonomy • Policy decision matrix • Draft Purview and DLP configurations Phase 4: Implementation & Configuration Objectives: • Deploy Purview features and enforce policies • Configure monitoring and response workflows • Integrate Copilot & Azure OpenAI with security and labeling Key Activities: • Enable Microsoft Purview Information Protection, DLP, and Audit • Publish sensitivity labels and policies • Configure Endpoint DLP, Defender for Cloud Apps, and compliance alerts • Integrate with Azure OpenAI or custom GPT tools (if applicable) Deliverables: • Live DLP & labeling enforcement • Alerting dashboard • Endpoint and cloud data controls in place • Monitoring of AI prompt behavior Phase 5: Testing & Validation Objectives: • Ensure policies are working as intended • Validate that sensitive data is protected during AI usage • Confirm user experience is not overly disrupted Key Activities: • Perform test scenarios: inputting sensitive data into Copilot or GPT • Run validation reports and review DLP logs • Adjust thresholds and exclusions (e.g., training environments) Deliverables: • Test cases and results • Compliance validation report • Tuning recommendations

Next steps

Contact us to get more offer information and plan your engagement