VerifyWise AI Governance Lexicon
A
- AI assurance
- AI audit checklist
- AI audit scope
- AI bias mitigation
- AI compliance frameworks
- AI explainability
- AI fairness metrics
- AI governance lifecycle
- AI impact assessment
- AI incident response plan
- AI lifecycle risk management
- AI model audit trail
- AI model drift
- AI model governance
- AI model inventory
- AI model robustness
- AI monitoring controls
- AI output validation
- AI red teaming
- AI regulatory sandbox
- AI risk assessment
- AI risk management program
- AI security controls
- AI shadow IT risks
- Accountability in AI
- Adversarial attacks
- Algorithmic accountability
- Algorithmic bias
- Algorithmic decision making
- Anonymization techniques
- Anti-discrimination in AI
- Application of ISO 42001
- Attack surface in AI systems
- Auditability of AI systems
B
C
- California AI regulations
- Certification of AI systems
- Chain of accountability in AI
- Change management in AI systems
- Checklists for AI compliance
- Classification of AI risks
- Code of conduct for AI development
- Cognitive bias in AI
- Compliance assurance in AI
- Compliance-by-design for AI
- Confidentiality in AI models
- Consent management for AI
- Continuous monitoring of AI models
- Control testing for AI governance
- Critical AI systems definition
- Cyberrisk governance for AI
- Cybersecurity risks in AI
- Cybersecurity standards for AI
D
- Data annotation risks
- Data bias
- Data governance in AI
- Data integrity for AI systems
- Data minimization in AI
- Data privacy impact assessments (DPIA)
- Data quality assurance in AI
- Data retention policies for AI
- Data security for AI models
- Differential privacy in AI
- Digital ethics
- Documentation standards for AI systems
- Drift detection in AI models
- Due diligence in AI procurement
- Dynamic risk scoring for AI
E
F
G
H
I
- Impact assessments for AI
- Incident management for AI systems
- Independent AI audit requirements
- Information security policies for AI
- Insider threats and AI models
- Integrity monitoring for AI models
- Internal control systems for AI
- International AI regulations landscape
- Interpretability vs explainability
J
K
L
M
N
O
P
Q
R
- Red-teaming AI systems
- Reliability testing in AI
- Reporting obligations under AI regulations
- Residual risk reporting
- Response plans for AI incidents
- Responsible AI by design
- Responsible sourcing of AI datasets
- Risk acceptance in AI governance
- Risk controls for AI models
- Risk inventories for AI models
- Risk prioritization in AI projects
- Risk register for AI
S
- Safety assurance for AI
- Safety monitoring for AI
- Secure AI model deployment
- Secure coding practices for AI
- Security audits of AI
- Sensitive data handling in AI
- Shadow AI monitoring
- Software supply chain risks in AI
- Stakeholder engagement in AI governance
- Standard operating procedures (SOPs) for AI
- Statistical parity in AI fairness
- Strategic risk management for AI
- Sustainability of AI governance programs
- Systemic risks of AI
