Compliance and Audit

BeaconGuard Assurance is designed to support regulatory compliance, auditability, and evidence preservation for AI-enabled systems operating in regulated environments.

The system is built around deterministic evaluation, immutable artifacts, and explicit provenance.

Compliance Objectives

BeaconGuard addresses common compliance requirements by design:

• Deterministic authorization outcomes
• Explicit, versioned policy intent
• Tamper-evident audit records
• Replayable decision evidence
• Separation of policy, enforcement, and models

These properties enable post-hoc verification without relying on probabilistic explanations.

Audit Record Model

Every authorization evaluation produces a structured audit record containing:

• Timestamp
• Policy snapshot identifier and version
• Normalized request inputs
• Decision outcome and reason codes
• Evaluation provenance

Audit records are append-only and designed for long-term retention.

Evidence Preservation

BeaconGuard treats audit output as evidence, not telemetry.

Key properties:

• Records are immutable once written
• No in-place modification or deletion
• Policy snapshots referenced by cryptographic identity
• Inputs captured in normalized, replayable form

This enables auditors to reconstruct decisions exactly as they occurred.

Deterministic Replay

Deterministic replay allows an auditor or investigator to:

• Re-run a historical request
• Using the original policy snapshot
• With the original normalized inputs
• And obtain the same decision outcome

Replay is fundamental to:

• Incident investigation
• Regulatory response
• Third-party verification

Failure Visibility

Failure conditions are explicitly recorded.

If an evaluation fails due to missing artifacts, verification errors, or internal faults:

• The decision fails closed
• The failure category is recorded
• No permissive fallback is allowed

This ensures that compliance gaps are visible rather than hidden.

Scope Boundaries

BeaconGuard compliance guarantees apply to:

• Authorization decisions
• Policy evaluation
• Audit record generation

They do not extend to:

• Model training data
• Model internal reasoning
• Downstream application behavior beyond authorization

These boundaries are explicit to avoid over-claiming compliance coverage.

Intended Reviewers

This documentation is intended for:

• Compliance and risk teams
• Internal and external auditors
• Security reviewers
• Regulatory stakeholders

Related Sections

• Policy Model
• Enforcement Runtime
• System Architecture