Zero-day attacks exploit unknown vulnerabilities in firmware, hardware, or software before developers have a chance to build a fix.In order to identify and prevent zero-day attacks before they have a chance to do serious harm, artificial intelligence (AI) offers a proactive and flexible method.By using machine learning (ML) and deep learning (DL) to identify unknown threats based on behavior rather than depending on known signs, AI improves cybersecurity.Network traffic, user activity, and file execution behavior are all continuously monitored by AI.Threat intelligence systems with AI capabilities analyze historical attack data to forecast potential weaknesses.AI systems are capable of tracking and analyzing user behavior patterns to spot anomalies.In order to find anomalous patterns that might point to a zero-day exploit, AI-driven anomaly detection analyzes big datasets. By mimicking software code attacks and doing code reviews to find fresh vulnerabilities, AI can improve vulnerability scanning. Potential entry points and vulnerabilities are found via Attack Surface Management (ASM).
