A blueprint for protecting corporate IP and sensitive customer data from the non-deterministic exfiltration risks of autonomous AI agents.
In traditional DLP systems, exfiltration is blocked at the network perimeter based on file signatures or known data patterns. Agentic AI breaks this model. An agent doesn't just upload a file; it might copy a sensitive snippet into a code block, summarize a confidential spreadsheet, or pass a database secret as a system prompt to a secondary LLM.
To protect the enterprise, the agent must be isolated behind an architectural Airlock. This ensures that no data leaves the sovereign environment without passing through a multi-stage technical inspection.
Agents should never have direct access to internal LLM APIs or external providers directly. Instead, all traffic must be routed through a local Sovereign Proxy. This proxy decrypts the TLS stream and inspects the entire context window—not just the latest message—for potential leaks.
Unlike static redaction, semantic redaction understands context. If an agent is outputting a customer support ticket, the DLP layer recognizes that personal data like names or phone numbers are being leaked and substitutes them with placeholders before the data is transmitted to the model.
DLP for AI requires a complete, non-repudiable audit trail of every token. This audit trail must be stored in a sovereign vault, separate from the agent's memory, to prevent an agent from erasing its own tracks if compromised.
Enabling Agentic AI doesn't have to mean sacrificing your data sovereignty. By implementing a multi-layered DLP blueprint, you can empower your workforce with the latest AI tools while maintaining mission-critical security.