Artificial intelligence (AI) allows real-time threat analysis and detection of suspicious behavior, improving responsiveness to cyberattacks.
AI-based solutions help companies automate system monitoring and strengthen critical data security.
AI-powered extended detection and response (XDR) systems correlate data from multiple sources (network, endpoint, cloud, applications) to identify complex threats that would escape traditional security solutions.
User and entity behavioral analytics (UEBA) uses machine learning to establish baseline behavioral profiles. Any significant deviation from these models triggers alerts, allowing the detection of compromised accounts or internal threats.
Automatic patch generation represents a major advance. AI systems analyze software vulnerabilities and generate functional patches, reducing the time between discovery of a flaw and its fix from days to hours.
AI attack simulation allows continuous testing of defenses. Unlike traditional one-off penetration tests, these systems permanently generate realistic attack vectors, identifying weaknesses before cybercriminals exploit them.
Defensive AI must however face offensive AI. Cybercriminals also use machine learning to develop adaptive malwares that evolve to bypass defenses, creating an algorithmic arms race in cyberspace.
