AI Data Loss Prevention (AI DLP)

Legacy DLP was built for email, endpoints, and file shares. AI workloads add new data paths: prompts to public LLMs, retrieval contexts, vector stores, and agent tool calls. AI DLP brings policy and inspection into those paths.

AI DLP capabilities include:

• Prompt inspection: Detecting PII, secrets, and regulated data in user prompts.
• Response inspection: Blocking sensitive content in model and agent output.
• RAG inspection: Filtering retrieval content based on classification and policy.
• Tool argument scanning: Catching sensitive payloads passed to agent tools or MCP servers.
• Policy mapping: Aligning AI flows with GDPR, HIPAA, and other regulatory schemes.

AI DLP only works when it is paired with strong data classification and clear policy. Without those inputs, runtime decisions either over-block (blocking legitimate work) or under-block (missing real leaks), so investing in data foundations pays off quickly.

Programs that operate AI DLP well also instrument both successful and blocked events, building the evidence base that compliance and audit teams now expect.

How PointGuard AI Helps

PointGuard's AI Data Protection applies AI DLP across prompts, responses, retrieval, and tool calls, and ties findings back to AI Governance evidence and remediation workflows. The combined controls keep sensitive data inside its intended trust boundary even as AI workflows multiply.

Learn More

• OWASP Top 10 for LLM Applications
• NIST AI 600-1 Generative AI Profile
• EU AI Act official text