Framework
A 9-step model that turns legal requirements into practical, repeatable controls.
Governance Foundations
Lay the groundwork with clear principles, defined roles, and strong ownership structures that guide responsible AI adoption.
Sourcing Integrity
Evaluate and classify the data fueling AI systems to ensure quality, accuracy, and legal compliance from the start.
Design for Transparency
Keep documentation and disclosures up to date so stakeholders understand how systems work and decisions are made.
Security & Resilience
Safeguard the AI lifecycle with end-to-end protections that reduce vulnerabilities and strengthen trust.
Fairness & Ethical Use
Audit for bias, mitigate risks, and prevent harmful or discriminatory applications of AI.
Oversight & Accountability
Define responsibilities, embed accountability, and ensure critical decisions always include a human-in-the-loop.
Compliance & Risk Management
Map laws and regulations, maintain risk registers, and be prepared to meet audit and reporting obligations.
Training & Culture
Build AI literacy across teams and create a culture of responsibility that sustains long-term governance.
Continuous Improvement
Monitor evolving laws, adapt policies, and refine practices over time to stay compliant and effective.