Reviewer Kit
Policy control before AI execution. Evidence after every decision.
This page helps compliance, security, privacy, risk, audit, architecture, and technical validation reviewers understand BeaconGuard's placement, decision model, evidence surface, and non-replacement boundaries.
BeaconGuard is
- an inline runtime control boundary
- a policy enforcement point before AI execution
- an execution-boundary authorization layer
- a fail-closed checkpoint
- an evidence layer after allow/deny/review decisions
- a way to add control to existing workflows without replacing core systems
BeaconGuard is not
- legal review
- HIPAA compliance program
- clinical safety review
- EHR
- AML case-management system
- fraud decision engine
- transaction monitoring platform
- SIEM
- IAM
- GRC
- model monitoring
- generic API gateway
- replacement platform
What reviewers can assess here
- Where BeaconGuard sits at the AI request boundary
- How signed request metadata, workflow identity, user role, source-system trust, approved-pathway status, context tags, freshness windows, and replay controls are evaluated
- How allow, deny, and needs-review decisions are returned
- What audit-ready evidence is produced after decisions
- Which existing systems remain authoritative outside BeaconGuard