Protect probabilistic systems with controls that recognise both deterministic and perceptual attack surfaces.
AI security cannot be reduced to conventional application security around an API. Models perceive language, structure, context and indirection probabilistically, so controls need to cover both machine-detectable attack patterns and attacks that only become meaningful through model perception.
Prompt injection as a layered problem
Deterministic pattern detection catches suspicious structures such as role-like strings, code fences, markup and structured-data constructs before model execution.
Probabilistic perceptual detection samples for attacks expressed through semantics, framing, indirection or other forms that deterministic rules cannot exhaustively enumerate.
Context determines response. Constrained inputs can fail closed; free text and retrieved content require controls proportionate to the function and risk.
Defence in depth, not misplaced confidence
No single detector can prove that a probabilistic model will behave safely on every input. The architecture combines deterministic boundaries, risk-based perceptual monitoring, supply-chain controls, leakage detection and operational response. Detection such as DLP remains a last line of defence; sensitive data appearing in model output should also trigger investigation of the upstream design.
We're here to help
Start a conversation.
Discuss your current architecture, AI, cloud or security challenge and how we can help.