This framework provides an operational structure for governing AI systems that interact with data, knowledge bases, and decision-making processes.

This is not a legal standard, certification, or regulatory approval.
It is a practical governance reference designed to support responsible system design, risk awareness, and organizational accountability.

The framework applies to AI-supported systems that influence, assist, or automate decisions with legal, financial, operational, or reputational impact.

It is applicable before implementation, during system design, and after deployment.

Each AI system must have a clearly documented use case:

• purpose of the system
• decisions it supports or influences
• intended users and affected parties
• operational context and limitations

Governance requires explicit identification of:

• data sources and ownership
• knowledge base location and control
• update mechanisms and versioning
• access boundaries and read/write permissions

The framework distinguishes between:

• AI-assisted decisions
• AI-recommended actions
• AI-automated decisions

Human oversight, escalation paths, and intervention rights must be defined.

For each system:

• identify potential harms and failure modes
• assign organizational responsibility
• document accountability ownership
• define acceptable and unacceptable outcomes

Governance is operational only if enforced through:

• technical access controls
• refusal and escalation rules
• system boundaries and constraints
• monitoring and review processes

Governance is not static. Systems must be periodically reviewed in response to:

• changes in use case
• regulatory developments
• incidents or near misses
• organizational changes

Informed by:

• EU AI Act (risk-based approach)
• UK AI governance principles
• NIST AI Risk Management Framework
• ISO/IEC 42001 (AI management systems)
• ISO/IEC 27001 (information security context)