Bias is a systematic distortion that causes an AI model to assess cases or groups consistently unevenly. In cybersecurity, biased training data can produce disproportionate risk assessments, missed threats, or unnecessary interventions. section 1.7 section 13.10
Fraud detection across industries
A fraud detection system trained mainly on historical data from one industry may misclassify transactions from another. The result can be unnecessary account lockouts or missed fraud attempts, creating operational risk and undermining confidence in the system. section 1.7
Manage bias as part of a broader control process:
- Validate AI outputs against data quality and real-world security context rather than treating automated assessments as inherently reliable. section 13.10 section 13.11
- Keep human oversight for high-stakes decisions, where business context, legal implications, and ethical considerations exceed the model’s understanding. section 13.10
- Combine AI with rule-based systems, signature-based detection, behavioral analysis, and human review instead of using it as a standalone solution. section 1.7
