Whitepaper
Unlock safe AI adoption with proactive data loss prevention
Learn how to build a practical data loss prevention strategy so your organization can discover sensitive data, classify it consistently, apply the right protections, and adopt AI with confidence.
Executive summary
Teams are creating and sharing more information than ever across Jira, Confluence, and AI-powered workflows. Some of that information is sensitive, but many organizations do not have a clear view of where it lives, how it is classified, or whether the right controls are in place.
A strong data loss prevention strategy helps close that gap. It gives security and IT teams the visibility to find sensitive data, the structure to classify it consistently, and the controls to protect it without slowing work down.
This guide walks through a practical four-step framework for building a DLP strategy and shows how Guard helps operationalize it across your organization.
Key takeaways
Why strong data security foundations are becoming more urgent as AI adoption, regulatory pressure, and data sprawl increase
How to build a practical DLP strategy with a four-step framework: discover, classify, control, and monitor
How Guard helps operationalize DLP across Jira and Confluence with content scanning, classification, policy enforcement, alerts, and remediation workflows
How DLP foundations extend into AI workflows with Rovo Chat security, connector data security, and Rovo security posture