In depth
Data loss prevention for AI extends the classic DLP question, “is sensitive data leaving?”, to every channel an AI system uses. A prompt can carry a customer record to a model provider. An agent can place a card number in a tool argument, a tool can return a credential in its result, and an agent can send personal data to another organization’s agent. Effective DLP for AI scans each of these in both directions, understands structured content such as JSON tool arguments rather than only free text, and validates matches (for example the Luhn check on card numbers) to keep false positives manageable. It also has to decide what happens to content it cannot scan, such as a binary file or a link to an external file.
Why it matters
An AI agent moves data between systems on its own initiative, often in places no human reviews. A policy that only scans the user’s prompt misses the tool argument the agent wrote and the result it read. Scanning has to sit where the agent’s traffic flows, and it has to fail closed: content that could not be scanned is not the same as content that is clean. Ferentin runs DLP on LLM input and output, MCP tool traffic and A2A messages, refusing content it cannot scan unless policy explicitly allows it.
Common use cases
- Redacting card numbers and national identifiers from prompts before they reach a model
- Blocking credentials returned in tool results from reaching the model
- Scanning messages and files exchanged with third-party agents
- Refusing files that cannot be scanned unless policy allows them
- Recording each detection and its outcome for audit
How Ferentin handles it
Ferentin is the trust layer for AI agents. The platform centralizes identity, policy enforcement and audit across LLMs, MCP servers and AI tools. DLP for AI is one of the primitives this trust layer is designed around. See the platform overview for how it fits into the service edge, control plane and observability plane.