Managing AI Risk in the Enterprise
As AI systems make increasingly consequential decisions, organizations need governance frameworks that ensure these systems are safe, fair, transparent, and compliant with emerging regulations.
Governance Pillars
1. Model Inventory and Classification
Maintain a register of all AI/ML models:- Model purpose and business owner
- Training data sources and processing
- Risk classification (high/medium/low)
- Deployment status and endpoints
2. Risk Assessment
Evaluate each model for:- Bias and fairness: Does the model discriminate against protected groups?
- Robustness: How does the model handle adversarial input?
- Privacy: Does the model memorize or expose training data?
- Transparency: Can decisions be explained?
- Safety: What's the worst-case outcome of a model error?
3. Development Standards
- Documented training data provenance
- Bias testing during development
- Security testing (adversarial robustness)
- Performance benchmarking and validation
- Peer review before production deployment
4. Monitoring and Drift Detection
- Model performance monitoring in production
- Data drift detection (input distribution changes)
- Concept drift detection (relationship changes)
- Fairness monitoring across protected groups
- Automated alerting on performance degradation
5. Incident Management
- Defined procedures for AI incidents (wrong prediction, bias discovery, data leak)
- Rollback procedures for problematic models
- Communication plans for AI-related incidents
- Root cause analysis framework
Regulatory Landscape
EU AI Act
Risk-based regulation:- Unacceptable risk: Banned (social scoring, real-time biometric)
- High risk: Mandatory conformity assessment
- Limited risk: Transparency obligations
- Minimal risk: No requirements
US Executive Order on AI Safety
- Safety testing requirements for frontier models
- Red teaming and evaluation standards
- Reporting requirements for high-capability models
Where SuperClue Fits
SuperClue does not inventory AI models or test them for bias. Its DAST scanner tests the web applications and APIs around your AI features.