Deterministic Control for Probabilistic Models
BeaconGuard keeps model behavior out of the authorization layer by placing deterministic control before any model execution.
The Control Problem
Large Language Models (LLMs) are probabilistic generation engines. They cannot serve as the authorization boundary for regulated financial operations. Relying on prompt engineering to govern cross-border AML or high-risk fraud workflows creates unquantifiable operational risk.
Zero-Trust AI Ingress
BeaconGuard Assurance enforces a deterministic control boundary before model inference. Requests are evaluated for cryptographic trust, structural validity, and policy compliance before the LLM is invoked. Untrusted, malformed, or replayed requests are blocked from reaching the model.
Untrusted / malformed / replayed request
Ingress validation runs before policy evaluation.
BeaconGuard deterministic boundary
Trust, structure, and policy checks execute in the same controlled control layer.
Blocked before model inference
Denied requests do not proceed to model output.
Trusted / valid / policy-compliant request
Inbound trust checks pass and request enters enforcement.
BeaconGuard deterministic boundary
Model invocation only after allow decision.
The model remains outside the control boundary and only receives requests that pass deterministic enforcement.
Fail-Closed Enforcement
When required compliance conditions are not met, BeaconGuard executes a deterministic, fail-closed denial. The system prevents the AI from manufacturing an unauthorized forward path and preserves human-reviewed operational authority.
Deterministic Deny
Missing trust proofs, invalid structure, or policy gaps resolve to deny, not permissive fallback.
Blocking Path Control
Blocked requests never reach model output and do not proceed as inferred exceptions.