Purview for AI Data Security: Managing Modern Risks

The rapid adoption of generative AI and large language models brings incredible productivity, but it also introduces unprecedented risks of data leakage and compliance violations. Organizations must proactively safeguard their intellectual property and sensitive customer data from being exposed through AI interactions. By mastering modern data governance, you can confidently enable AI tools while maintaining strict control over your organizational assets. This text-based course guides you through the foundational concepts of AI data security and shows you how to use Purview to discover, protect, and govern data in AI-driven workflows. You will read about identifying high-risk AI usage, enforcing protective guardrails, and aligning your security posture with modern zero-trust principles. What you'll learn: - Understand the unique data security risks introduced by generative AI and large language models. - Configure Purview sensitivity labels to protect data used in AI prompts and responses. - Identify and mitigate shadow AI risks by discovering unauthorized AI tools within your network. - Apply zero-trust principles to AI data access, ensuring users only interact with permitted information. - Monitor data exposure risks and set up alerts for policy violations in AI environments. We begin with the core terminology of AI security and zero-trust data protection. From there, you will read through step-by-step policy configurations and practical scenarios, learning how to implement real-world security boundaries for AI tools. This course is designed for IT professionals, security administrators, and compliance beginners who want to secure their organization's AI adoption. No prior experience with Purview is required. Start reading today to build a secure foundation for AI integration in your workspace.