For a long time, cybersecurity was primarily a reactive discipline. Cybersecurity experts would step in after an incident, analyzing event logs, identifying vulnerabilities, and implementing fixes to mitigate the impact of attacks. Defense systems relied mainly on predefined rules, known threat signatures, and traditional monitoring mechanisms.<\/p>\n\n\n\n
But this approach has shown its limitations in the face of rapidly evolving cyber threats. Attacks have become more sophisticated, faster, and, above all, harder to detect. From ransomware and zero-day attacks to automated phishing and large-scale exploitation of software vulnerabilities, cybercriminals now use advanced techniques, some of which are powered by artificial intelligence.<\/p>\n\n\n\n
At the same time, the attack surface has expanded significantly. The widespread adoption of the cloud, connected devices, remote work, and distributed digital infrastructures exposes organizations to constant risks. Every device, every API, and every data stream becomes a potential entry point.<\/p>\n\n\n\n
In this context, complexity is skyrocketing. Information systems generate millions of security events every day, making comprehensive human analysis impossible. According to IBM Security, the average cost of a data breach reached $4.45 million in 2023<\/strong>, a record high that illustrates the scale of the economic stakes involved in cybersecurity1<\/a><\/sup>.<\/p>\n\n\n\n The figures illustrate this shift:<\/p>\n\n\n\n Given this situation, a paradigm shift is needed. Cybersecurity can no longer be purely reactive; it must become predictive, capable of anticipating threats before they materialize.<\/p>\n\n\n\n The field is thus entering a new era. Cybersecurity experts are no longer limited to protecting systems; they must now manage complex digital environments, where artificial intelligence has become a key tool for detecting, analyzing, and neutralizing increasingly autonomous threats.<\/p>\n\n\n\n\n