Data Loss Prevention (DLP) encompasses the strategies, tools, and processes organizations use to detect and prevent sensitive data from leaving the organization through unauthorized channels. In the AI context, DLP is critical for preventing data leakage through AI tools like ChatGPT, Claude, and coding assistants.
AI-aware DLP solutions monitor several vectors: browser-based AI tool interactions (prompts typed into web-based AI services), API calls to AI services, file uploads to AI platforms, clipboard operations copying data into AI tools, and AI browser extensions that may access page content.
DLP detection methods include: pattern matching (detecting SSNs, credit card numbers, API keys), keyword matching (flagging confidential project names), machine learning classifiers (identifying sensitive content contextually), document fingerprinting (detecting fragments of protected documents), and exact data matching (comparing against databases of protected information).
Modern AI governance platforms extend traditional DLP by providing: real-time prompt scanning before submission to AI tools, context-aware classification of AI interactions, granular policy enforcement per AI tool, user coaching and warnings before data exposure, and detailed audit trails of all AI data interactions.
