Systematic and unfair discrimination or prejudice that occurs in automated decision-making systems, often resulting from biased training data or flawed algorithm design.
Algorithmic bias in insider threat systems can lead to discrimination against certain employee groups, creating legal and ethical risks while reducing security effectiveness. Biased models may over-flag certain demographics while missing genuine threats from others. Organizations must implement bias testing, diverse training datasets, and regular model auditing to ensure fair and effective insider threat detection. The EU AI Act and similar regulations increasingly require bias testing and algorithmic transparency.