DLP for AI
Data Loss Prevention for AI Agents
Every LLM call is a potential data leak. DLP for AI prevents sensitive data -- PII, PHI, financial data, credentials -- from leaving your organization through agent operations.
What is DLP for AI?
Data Loss Prevention (DLP) prevents sensitive data from leaving the organization through unauthorized channels. Traditional DLP watches email, endpoints, and cloud storage. DLP for AI agents watches a new channel: every LLM API call.
When an AI agent sends a prompt to an LLM provider, that prompt may contain customer PII, protected health information, financial data, or internal credentials. DLP for AI intercepts these calls, redacts sensitive data before it reaches the provider, and restores it on the way back.
Beyond redaction, DLP for AI includes data classification (knowing what types of sensitive data your agents handle), data lineage (tracking which agents encountered what data and where it went), and risk scoring (identifying which agents have the highest data exposure).
Why it matters in the agentic era
Every LLM call is a potential data leak -- prompts containing sensitive data leave your network and reach a third-party model provider. Traditional DLP watches email and endpoints. Agent DLP must watch every LLM API call, every agent memory write, and every inter-agent message.
Autonomous agents compound the risk. A human might send one email with PII per day. An agent might make hundreds of LLM calls per hour, each potentially containing sensitive data from your CRM, EHR, or financial systems. The attack surface is orders of magnitude larger.
How MeetLoyd implements DLP for AI
- PII redaction (9 types) -- Built into the LLM Gateway. Pre-LLM tokenization replaces sensitive data with tokens; post-LLM restoration puts it back. The LLM provider never sees real PII.
- Data classification inventory -- Categorizes data flowing through agents: PII, financial, health, credentials, internal. Know exactly what types of sensitive data each agent handles.
- Data lineage tracking -- Per-classification source tracking showing which agents encountered what data, when, and where it went.
- Risk heatmap -- Per-agent exposure scores (0-100), severity-weighted. Instantly see which agents pose the highest data risk.
- Policy effectiveness -- Block rate percentage and unhandled pattern detection. Know if your DLP policies are actually working.