Inline authorization and evidence boundary for AI-assisted workflows.
BeaconGuard sits between regulated enterprise applications and AI/model endpoints. It deploys alongside existing systems as an inline policy proxy. Each request is evaluated before model execution using deterministic policy inputs such as signed metadata, workflow identity, user role, source-system trust, approved-pathway status, request timestamps, freshness windows, and required context tags.
Existing Systems Remain in Place
BeaconGuard does not replace the system of record, workflow application, case-management platform, EHR, AML system, fraud system, SIEM, IAM, or GRC tool. Those systems continue to own their existing functions. BeaconGuard enforces the AI request boundary before model execution and emits decision evidence after allow, deny, or needs-review outcomes.
Architecture Sequence
- Existing regulated application or workflow submits an AI-bound request
- BeaconGuard receives the request before model execution
- Signed metadata, source trust, user role, workflow identity, context tags, and policy inputs are validated
- Freshness and replay controls are checked using request IDs, timestamps, signed metadata, or deployment-specific state controls
- Deterministic Context Tagging and Approved-Pathway Verification occurs
- Deterministic policy evaluation runs
- BeaconGuard returns allow, deny, or needs-review
- Only allowed requests proceed to approved model endpoint
- Decision evidence is preserved for audit, security, privacy, compliance, and risk review
Decision Outputs
Allow
The request meets policy requirements and can proceed to the approved model endpoint.
Deny
The request fails required policy, trust, freshness, role, pathway, or context checks before model execution.
Needs Review
The request is routed for human review before model execution because policy requires approval.
BeaconGuard as a Policy Enforcement Point
BeaconGuard functions as a policy enforcement point at the AI request boundary. It can consume policy inputs from existing identity, workflow, application, governance, or source systems, but it does not become the system of record and does not modify legacy application logic.
Capability Blocks
Healthcare AI Control
BeaconGuard supports PHI-sensitive operational AI workflows by enforcing approved model pathways, deterministic context tags, user-role boundaries, source-system trust, and needs-review routing before model execution.
- Care-management summary support
- Discharge-planning assistance
- Patient-message draft review
- Utilization-review preparation
- Referral-intake or chart-abstraction workflows
Financial Workflow Control
BeaconGuard supports AI-assisted financial workflows where analyst-assist outputs, fraud review, AML/KYC review, dispute handling, or customer-impacting recommendations require deterministic request control before model execution.
- AML analyst support
- Fraud exception review
- KYC escalation support
- Dispute resolution assistance
- Account-action recommendation gating
Regulated Internal Automation
BeaconGuard supports internal AI agents and automations that need policy enforcement before interacting with production systems, sensitive data, or regulated operational workflows.
Why BeaconGuard is not just an API Gateway
Standard API gateways route traffic based on endpoints, tokens, and network policies. BeaconGuard evaluates workflow-level AI policy context before model execution, including user role, workflow identity, signed request metadata, approved-pathway status, source-system trust, freshness windows, and evidence requirements.