Every story tagged Data Drift, curated for CIOs and IT leaders — ranked by source credibility, engagement, and freshness.
1 story · open in the command center
Data drift—when ML model input data changes over time—poses critical security risks as models trained on historical attack patterns fail to detect evolving threats, leading to increased false negatives and exploitable vulnerabilities. The 2024 echo-spoofing attack that bypassed email protection services demonstrates how threat actors actively exploit these model weaknesses to evade detection. Organizations relying on ML-based security systems must implement continuous monitoring using statistical tests (KS, PSI) and establish regular model retraining cycles to maintain effective threat detection as attack methods evolve.