Data Exfiltration via Generative Systems
AI systems that process sensitive inputs such as documents, logs, or enterprise data are vulnerable to exfiltration attacks. Without proper runtime controls, models can be coaxed into extracting and exporting confidential information through subtle prompt manipulation or through multimodal vectors.
Real-Time Behavior Constraints
Traditional DLP systems focus on pattern matching and user intent, but generative agents require real-time behavior constraint mechanisms that block unsafe outputs before they are released. Runtime inspection of both input and output modalities is essential to prevent leakage. AI agents with access to wallet metadata, private dashboards, or off-chain analytics can be coerced into leaking sensitive operational or financial information, even when underlying blockchain data is public.
Consider using
- Cyberhaven - dynamic data tracing and AI-based lineage for DLP and insider risk
- Nightfall AI - AI-based detection across SaaS, endpoints, email, browsers, and AI data flows
- Proofpoint (Acuvity) - runtime inspection and enforcement across AI apps, agents, and MCP servers
- Reco AI - SaaS AI exposure discovery, permission auditing, and exfiltration pathway detection