Provide a structured approach for developing, deploying, and monitoring AI systems.
Promote fairness, transparency, and accountability.
Identify and mitigate risks associated with AI models and data.
Establish clear roles, policies, and controls.
Improve AI quality and performance over time.
Define ethical, operational, and security rules.
Ensure quality, accuracy, and reliability.
Evaluate risks across AI lifecycle stages.
Ensure data quality, privacy, and compliance.
Track AI performance and identify biases.
Maintain records for accountability and audit readiness.
Increase user and stakeholder confidence.
Align with global AI governance frameworks.
Minimize errors, bias, and operational failures.
Streamline AI development and deployment.
Enhance AI reliability and performance long-term.