AI Governance
Framework
AI systems without governance are unpredictable, unscalable, and operationally risky. We design governance layers that make AI systems safe to deploy inside real organizations.
Policy & Access Control
Every AI system operates under defined permissions, role boundaries, and execution constraints.
- Role-based access control (RBAC)
- Tool-level permissioning
- System boundary enforcement
- Execution scoping per agent
Model & System Versioning
AI systems evolve continuously — but every change is tracked, versioned, and reversible.
- Model version control
- Prompt + workflow versioning
- Rollback mechanisms
- Environment isolation (dev/staging/prod)
Data Governance & Memory Control
We control what AI systems remember, store, and retrieve across time horizons.
- Short-term vs long-term memory separation
- PII filtering & data classification
- Retrieval access policies
- Data retention rules
Observability & Auditability
Every decision, tool call, and system action is structured, traceable, and reviewable.
- Full event logging
- Conversation traceability
- Tool execution auditing
- Cost & latency tracking
Risk & Compliance Layer
AI systems are continuously evaluated against operational, legal, and security risks.
- Hallucination risk monitoring
- Security constraint enforcement
- Fallback & escalation logic
- Compliance alignment (SOC2-ready patterns)
Governance is Not a Layer — It is a System Property
Most AI systems treat governance as an afterthought — logs, policies, and controls added after deployment.
At Myria, governance is embedded into architecture, execution, and memory systems from day one — ensuring AI remains safe, observable, and controllable at scale.