Evaluating AI-powered Security Operations Center (SOC) platforms reveals differences in AI capabilities. Some solutions utilize pre-trained AI models that handle specific use cases effectively but lack adaptability, limiting them to predefined alerts. In contrast, adaptive AI platforms learn from diverse alert types and adjust to an evolving threat landscape. Security teams need to understand this distinction to build a resilient SOC capable of managing a wide array of security threats and alerts.
Pre-trained AI models in the SOC are typically developed by training machine learning algorithms on historical data from specific security use cases, such as phishing detection, endpoint malware alerts, and the like.
Modern security operations teams face a sprawling and ever-changing landscape of alerts. From cloud to endpoint, identity to OT, insider threats to phishing, network to DLP, and so many more, the list goes on and is continuously growing.
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