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Zero Trust Cybersecurity for Autonomous AI Models & LLM Endpoints

Protecting corporate AI agents from prompt injection, memory poisoning, data exfiltration, and adversarial guardrail bypasses.

Securing the AI Execution Surface

As enterprise AI agents gain access to internal APIs, database connections, and autonomous action channels, the threat surface shifts dramatically.

Common Attack Vectors

  1. Direct & Indirect Prompt Injection: Injecting malicious instructions via external Web pages or ingested email documents.
  2. Data Poisoning: Infiltrating vector database embeddings to distort model decision outputs.
  3. Tool Misuse: Manipulating agent function calling parameters to delete files or exfiltrate credentials.

Defensive Architecture: The Sanitize-Evaluate-Execute Enclave

We implement an outward-facing Guardrail Enclave that validates every input and output tensor before executing downstream function calls.