AegisLayer Public Governance Model¶
This document defines conceptual governance controls for AI-initiated actions.
Governance Objective¶
Keep AI capability separate from execution authority through explicit trust validation and accountable decision records.
Core Governance Controls¶
- Explicit identity and authority validation
- Policy-before-execution decisioning
- Risk-based human approval for high-impact actions
- Capability-scoped connector invocation
- Evidence-by-design lifecycle records
- Continuous verification during runtime
- Fail-closed behavior on trust uncertainty
- Containment and recovery paths for incidents
Architecture Change Workflow¶
Material architecture changes should follow:
- RFC proposal and review
- ADR creation/update for accepted decisions
- Synchronization of docs, diagrams, mappings, and examples
- Validation and public-release review
Release Governance¶
Releases for this reference repository follow SemVer-style documentation governance:
- MAJOR: material architecture interpretation changes
- MINOR: additive architecture/reference artifacts
- PATCH: non-material corrections
Release artifacts should include updated version metadata, changelog, release notes, and checklist evidence.
ADR/RFC Status Governance¶
ADR statuses¶
- Accepted
- Superseded
- Deprecated
- Rejected
RFC statuses¶
- Draft
- Review
- Accepted
- Rejected
- Withdrawn
- Superseded
- Implemented
Review Roles¶
A mature governance process may involve:
- Requester
- Policy owner
- Approver
- Operator
- Security reviewer
- Auditor
- Incident responder role
Decision Outcomes¶
- allow
- deny
- allow_with_constraints
- require_approval
- escalate
Claims Boundary¶
This governance model is conceptual and educational. It does not claim that every threat can be prevented.