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AI cybersecurity

AI cybersecurity harnesses proactive machine learning to enhance protection from emerging threats that traditional signature-based methods may not pick up.

AI can monitor activity at the endpoint and detect malicious software based on suspicious behavior, without the need for a complete code profile. It can also monitor network traffic and detect bots, phishing attacks, and malware.

Cybersecurity AI can help companies interpret where and how threats are likely to occur, offering context for prioritizing security alerts and responding quickly to incidents. It can also identify the causes and issues of vulnerabilities to help fix them, and prevent future problems.

Machine Learning is one of the most popular types of AI for cybersecurity, and it uses data patterns to predict the likelihood of an event happening. It can be used to build models of normal behaviors, detect anomalies in real-time, or improve signature updates that are centrally distributed.

Neural networks are another type of AI used in cybersecurity, which use layered analytical nodes that process input data with a calibrated bias. These systems can learn to optimize their bias over time, a process known as deep learning.

While AI cybersecurity can be a valuable tool for organizations, there are some risks to consider before implementing it. For example, AI may not work as well if the error tolerance and decision layers are not well-trained, and it could take too long to generate the intelligence necessary. It can also be vulnerable to attack if it is infiltrated by hackers or other threat actors, which is why ENISA is working on mapping the AI cybersecurity ecosystem and providing security recommendations for those using it.