The Practical Executive's Guide to Data Loss Prevention

DLP is not a new discipline. But the problem it solves has changed in ways that make the tools and approaches of five years ago look dangerously incomplete.
Organizations now face three distinct AI-driven exposure risks: shadow AI that employees adopt without IT visibility; embedded AI built into sanctioned platforms like Microsoft 365 and Salesforce, which interacts with sensitive data in ways existing policies were never designed to govern; and agentic AI, autonomous systems that access data, call external services and act without a human in the decision loop. Each represents a different kind of gap. Together they have fundamentally changed what it means to control sensitive data.
Whether you are deploying DLP for the first time, extending coverage to new channels or moving to a new vendor solution, this updated 10-step guide gives you a practical framework for every stage of the journey.
 

You'll learn how to:

• Build an information risk profile and identify your highest-priority use cases: AI enablement, insider risk, data sprawl or compliance 
• Choose the deployment model that fits your regulatory environment: cloud-native, on-premises or hybrid 
• Discover and classify sensitive data, including AI-generated outputs, before building policies around it 
• Extend policies to every channel sensitive data uses, including AI tools and agentic workflows