The software industry is racing to write code with artificial intelligence. It is struggling, badly, to make sure that code holds up once it ships.

Data drift happens when the statistical properties of a machine learning (ML) model's input data change over time, eventually rendering its predictions less accurate. Cybersecurity professionals who rely on ML for tasks like malware detection and network threat analysis find that undetected data drift can create vulnerabilities. A model trained on old attack patterns may fail to see today's sophisticated threats. Recognizing the early signs of data drift is the first step in maintaining reliable and efficient security systems.

For the last 18 months, the CISO playbook for generative AI has been relatively simple: Control the browser.