SAS AI Governance Manager Features List
Centralized AI inventory & life cycle management
- Maintain a centralized inventory of AI systems, models, agents and use cases. Track AI assets from registration and approval through monitoring, review and retirement with end-to-end life cycle governance.
Standardized registration, classification & risk tiering
- Register AI assets using structured workflows that capture metadata, classify systems and assign risk tiers based on business impact, regulatory exposure and governance requirements.
Comprehensive governance & policy framework
- Define, document and enforce governance policies, standards and controls across AI initiatives. Establish consistent governance practices that support accountability, transparency and regulatory readiness.
Role-based access & controlled approval workflows
- Apply role-based permissions, segregation of duties and multistep approval workflows to ensure appropriate oversight, accountability and auditability throughout the AI life cycle.
Integrated model development ecosystem
- Integrate with SAS Viya and support AI assets developed in third-party environments. Enable seamless governance across model development, validation, deployment and monitoring processes.
Independent validation & challenger model framework
- Support independent validation through configurable testing, challenger model comparisons and documented review processes. Track findings, approvals and remediation activities from assessment through resolution.
Continuous monitoring & performance management
- Monitor AI systems and models for performance degradation, drift, stability and emerging risks. Automated alerts and configurable thresholds help stakeholders respond quickly to material changes.
Risk, controls & issue management
- Perform AI and model risk assessments, document controls, track issues and manage remediation activities through governed workflows. Maintain visibility into risk exposure across the AI portfolio.
Regulatory compliance, auditability & reporting
- Support regulatory readiness with centralized documentation, audit trails, governance reporting and executive dashboards. Demonstrate oversight, accountability and compliance with internal policies and external requirements.
Responsible AI, explainability & enterprise integration
- Promote trustworthy and responsible AI through fairness testing, bias monitoring, explainability analysis and stress testing. API-driven integration and cloud-native architecture support governance across enterprise AI ecosystems.