Be a DLP Hero: How to Quickly Deliver Value from Your DLP Program and Set It Up for Future Success

The white paper "Be a DLP Hero: How to Quickly Deliver Value from Your DLP Program and Set It Up for Future Success," written by Kevin Garvey and published by SANS Institute in May 2025, provides a practical framework for launching, operationalizing, and governing a data loss prevention (DLP) program. The paper outlines an iterative approach to DLP that prioritizes quick, high-impact wins over one-size-fits-all tooling, covering program design, executive buy-in, common objections, and the people, process, and technology needed to sustain a program long-term.

Key recommendations:

• Start small: launch a DLP program by first identifying an organization's most valuable data and understanding where it is stored and how it moves
• One-size-fits-all DLP tools are a risk; DLP strategy should be tailored to an organization's specific business operations, compliance needs, and data classification
• A four-step framework anchors program design: understand the business, learn the data, execute the program, and govern it continuously
• Executive buy-in should be sought from the CTO, chief compliance officer, chief privacy officer, general counsel, enterprise risk management, communications, and business unit heads
• Common roadblocks to DLP adoption include unclear ownership, perceived cost, perceived complexity, and the assumption that cloud or SaaS vendors already protect data by default
• A bifurcated ownership model, where technical teams operate DLP tooling while compliance teams architect the content and policy, can resolve ownership disputes
• DLP programs depend equally on people (in-house staff or a managed security service provider), process (documentation and policy updates), and technology (design, implementation, and continuous monitoring)
• Open communication with end users, including explaining the "why" behind new controls, reduces the risk that users will devise unapproved workarounds
• AI adoption introduces new DLP considerations, since data fed into private large language models can be poisoned or expose unapproved data such as personally identifiable information, and outputs should be reviewed by a human
• Governance should track metrics tailored to each stakeholder group, including mean time to detect and respond, DLP agent coverage, toolset uptime, and data types discovered

The paper frames DLP not as a single tool deployment but as an ongoing program that matures through incremental stages, with success measured differently by technical, security, legal, compliance, and risk stakeholders. Its central argument is that organizations that start narrow and iterate, rather than attempting comprehensive coverage from day one, are more likely to sustain long-term buy-in and reduce data-oriented risk. Guidance in the paper draws on experience architecting and governing enterprise DLP programs across technology, compliance, risk, and security functions.