AI-based zero-day attack detection focuses on identifying previously unseen or unknown attack patterns that may exploit security weaknesses before specific detection signatures or known attack indicators are available. It involves analyzing application requests, access-control behavior, input patterns, and abnormal interactions to identify deviations from expected application behavior that may indicate emerging or unfamiliar attacks. By combining unsupervised machine learning-based anomaly detection with self-supervised deep learning techniques, this approach helps recognize unusual API access patterns, detect potentially malicious command-injection behavior, and identify threats that may not match existing attack signatures. The resulting findings support early threat identification, investigation of suspicious activities, improved detection of previously unknown attack techniques, and the implementation of appropriate defensive measures to reduce the potential impact of emerging application security threats.