The core OS layer that enforces AI governance at runtime. RIOS makes governance decisions verifiable, auditable, and mathematically provable. It's the foundation of AI-Balance.ai. Manages authority boundaries, drift detection, and hard-reset capabilities.

A hierarchical model that prevents silent authority expansion. ARH ensures AI systems cannot exceed their granted authority boundaries without explicit human approval. Uses ratcheting mechanism to tighten (not loosen) authority controls over time.

The immutable foundation layer that enforces hard boundaries on AI authority. ContA defines non-negotiable governance rules that cannot be overridden or disabled. Similar to constitution in government: highest law that nothing can violate.

HAI (Human Authority Involvement) + APR (Authority Preservation Ratio) must equal 1.0. This mathematical invariant guarantees authority cannot be delegated silently. If HAI=0.1, then APR must=0.9. If either changes, the invariant triggers alerts.

Pre-built, certified governance configuration for specific compliance requirements. Each pack is versioned, auditable, and ready for immediate deployment. Examples: GDPR Consent Pack, HIPAA Compliance Pack, APRA CPS230 Operational Risk Pack.

Real-time monitoring system that detects model drift in <100ms. Alerts when AI systems deviate from governance constraints. Uses statistical analysis, behavior monitoring, and authority tracking.

Immutable, cryptographically verified log of all governance events. Required by APRA CPS230, GDPR, and EU AI Act for regulatory compliance. Includes: decisions made, authority changes, drift events, access logs.

The authoritative schema that defines all governance packs in machine-readable format. Similar to Walmart MP_ITEM_SPEC_4.0 or npm package.json for governance. Enables standardization, versioning, and automated validation.

Automated catalog of all AI/ML models in an organization. Required by APRA CPS230 and GDPR for governance oversight. Tracks: model name, version, owner, risk tier, compliance status, audit trail.

Classification of AI models by governance risk level (Low, Medium, High, Critical). Determines which governance packs apply and audit frequency. Mandatory for APRA CPS230 and EU AI Act compliance.

Framework for managing third-party AI vendors and ensuring they maintain governance standards. Includes SLA enforcement, compliance verification, and audit rights. Prevents vendor scope creep and ensures accountability.

Emergency mechanism to immediately revoke all AI authority and restore human control. Mandatory in APRA CPS230 and must be guaranteed to work within 30 seconds. Cannot be disabled, disabled, or delayed by any component.

Australian Prudential Regulation Authority's guidance on managing risks from AI/ML. Mandatory for Australian banks starting December 2025. Requires: operational risk controls, model inventory, audit trails, hard-reset capability.

EU privacy law with requirements for automated decision-making, right to explanation, and data subject protections. Applies globally to EU customer data. Requires: consent, explainability, data access rights, audit trails.

European Union legislation that classifies AI by risk level. High-risk AI requires documentation, testing, and human oversight. Bans certain high-risk applications (facial recognition, social scoring).

U.S. framework for managing AI risks across government and critical infrastructure. Increasingly adopted by private sector for governance standardization. Covers: risk identification, assessment, mitigation, monitoring.

U.S. healthcare privacy law with requirements for AI systems handling protected health information. Requires: encryption, access controls, audit trails, breach notification.

U.S. law requiring financial accountability, including controls over automated decision systems. Requires: internal controls documentation, audit trails, management responsibility.

U.S. financial privacy law with requirements for AI systems handling customer financial data. Requires: information security, privacy policies, authentication controls.

EU regulation on digital operational resilience for financial entities. Requires: resilience testing, incident reporting, third-party risk management.

Australian Securities and Investments Commission guidelines for AI use in financial services. Focuses on: governance, risk management, consumer protection, market integrity.

High-level organizational unit representing a major governance area. Examples: Healthcare Domain, Financial Services Domain, Technology Domain. Each domain has specific compliance requirements and governance packs.

Operational pathway within a domain for governance enforcement. Example: Treatment Authority Channel (healthcare), Credit Decision Channel (banking). Channels define how authority flows through systems.

Specific AI system or decision point within a channel. Example: Diagnosis AI Node, Loan Approval Node, Credit Scoring Node. Nodes are where HAI+APR invariants are enforced.

Level of delegated authority to an AI system (Startup, Scale, Enterprise, Production). Determines governance intensity and hard-reset latency requirements. Higher tier = more authority = more governance overhead.

Seven core governance building blocks (Authority identification, Responsibility mapping, Evidence preservation, Threshold enforcement, Authorization verification, Boundary assertion, Authority audit). All governance packs use ARETABA primitives in some combination.