Workflow Examples
BeaconGuard applies one inline runtime control boundary across healthcare, financial services, and regulated internal automation. Existing systems remain in place while AI-bound requests are controlled before model execution.
Healthcare AI Operations Review
BeaconGuard evaluates deterministic context tags, source-system metadata, signed request attributes, user role, workflow identity, approved-pathway status, and policy conditions before model execution. It does not replace the EHR or clinical workflow. It interposes a fail-closed AI request boundary so existing healthcare workflows can remain in place while AI-bound requests are controlled and auditable.
If context tagging is needed, the host application or an optional preprocessor can generate tags before the request reaches BeaconGuard. BeaconGuard then enforces policy against those tags and other deterministic request attributes.
- Approved-pathway verification before patient-related context reaches a model endpoint
- User role and workflow identity checks before execution
- Needs-review routing for privacy, security, compliance, or architecture review
- Audit-ready evidence after allow, deny, or needs-review decisions
Financial Crime / Fraud / AML Review
In this workflow, AI can assist analysis for cross-border AML, KYC, fraud, or financial dispute review, but no model inference proceeds without BeaconGuard policy evaluation and preserved decision evidence.
- Application Input: The financial or analyst application submits a cross-border case for AI-assisted summarization or recommendation.
- Gateway Intake: BeaconGuard receives the request before model execution.
- Trust and Replay Validation: Signed metadata, source-system trust, request ID, timestamp, and freshness inputs are checked.
- Deterministic Policy Evaluation: The request is checked against governed policy constraints for required AML, KYC, or workflow evidence.
- Control Outcome: BeaconGuard returns allow, deny, or needs-review after deterministic policy enforcement.
- Decision Evidence: A structured decision record is retained for security, risk, compliance, and audit review.
Regulated Internal Automation Review
An internal automation or AI agent attempts to perform an action against a regulated system. BeaconGuard checks policy before execution, blocks unauthorized actions, and records evidence for operational review.
- Checks user role, workflow identity, environment, requested action, and change window
- Validates signed request metadata, approved automation pathway, and freshness inputs
- Blocks automation outside approved role or operating conditions
- Routes higher-risk actions for human approval
- Preserves decision evidence for security, risk, operations, and audit review
Decision Evidence
Each workflow emits structured enforcement records only after BeaconGuard has decided allow, deny, or needs-review. Reviewers inspect the decision signal, request metadata, policy inputs, freshness checks, and evidence record to determine exactly what control path executed.