Buyers Guide For AI Threat Detection And Response
MixMode, Tuesday, October 17,2023
Cybersecurity has significantly transformed by integrating Artificial Intelligence (AI) technologies. AI has revolutionized threat detection by enabling organizations to analyze vast amounts of data, identify patterns, and detect anomalies that may indicate potential cyber threats.
Early Approaches
Expert Systems: In the early days of AI in cybersecurity, expert systems were developed to mimic human expertise and knowledge. These rule-based systems used predefined rules to identify known threats and vulnerabilities.
Machine Learning (ML) Era
Anomaly Detection: ML algorithms gained prominence in the 2000s, enabling the development of more advanced threat detection systems. Anomaly detection techniques were employed to identify deviations from normal patterns of user behavior, network traffic, or system activities. ML algorithms could learn from historical data and adapt to new threats.
Signature-Based Detection: ML algorithms were also used for signature-based detection, where patterns and signatures of known threats were identified and matched against incoming data. This approach effectively detected known threats but struggled with identifying unknown or zero-day attacks.