AI and Machine Learning in Cyber Threat Detection

The complexity of cyberthreats is making traditional security methods less and less effective. AI and ML are becoming powerful tools for detecting and preventing cyberattacks. With the use of vast amounts of data, AI-driven systems are able to identify trends, identify anomalies, and respond to threats with remarkable speed and accuracy. AI algorithms are constantly evolving, absorbing new threats and adjusting their defenses in real time, unlike conventional rule-based systems. This proactive approach helps organizations stay ahead of sophisticated attacks like ransomware, phishing, and zero-day exploits. AI models are particularly good at behavioral analysis, network traffic monitoring, and endpoint security Direct learning and other approaches draw awareness to known dangers while independent learning identifies patterns that could otherwise go unnoticed and indicate illegal conduct. Threat response systems can also benefit from knowledge expansion. AI has limited cybersecurity potential. Despite its potential, AI in cybersecurity has limits. Bias in training data, negative attacks designed to fool algorithms and ethical issues connected with data privacy must be correctly considered. Data privacy concerns need to be thoroughly thought out.However, as AI and ML develop further, they are transforming cybersecurity and giving companies the ability to identify risks sooner & react more quickly. Latest AI-driven techniques for cyberthreat detection are examined in this study, with a focus on real-world applications, current limitations, and future directions. By understanding how AI enhances security, businesses can better prepare to protect against a problem scenario that is always evolving