Risks from data (Risks of data leakage)
AI Safety Governance Framework
National Technical Committee 260 on Cybersecurity (TC260) (2024)
AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or cause loss of confidential intellectual property.
"In AI research, development, and applications, issues such as improper data processing, unauthorized access, malicious attacks, and deceptive interactions can lead to data and personal information leaks."(p. 8)
Other risks from National Technical Committee 260 on Cybersecurity (TC260) (2024) (25)
Risks from models and algorithms (Risks of explainability)
7.4 Lack of transparency or interpretabilityRisks from models and algorithms (Risks of bias and discrimination)
1.1 Unfair discrimination and misrepresentationRisks from models and algorithms (Risks of robustness)
7.3 Lack of capability or robustnessRisks from models and algorithms (Risks of stealing and tampering)
2.2 AI system security vulnerabilities and attacksRisks from models and algorithms (Risks of unreliable output)
3.1 False or misleading informationRisks from models and algorithms (Risks of adversarial attack)
2.2 AI system security vulnerabilities and attacks