In behavioral detections, supervised machine learning models are trained on vast datasets of known malicious activities and normal user/system behaviors. This allows them to identify similar behavior and deviations in new attacks that indicate a potential threat, such as unusual file movement, unauthorized domain access, or suspicious network connections.
Anomaly detection, powered by unsupervised machine learning, is crucial for uncovering novel or zero-day threats that don't fit predefined patterns. These models establish baselines of normal activity and flag any significant departures, which can signal the use of new attack techniques.
AI complements traditional detections in a multi-layered detection architecture. It helps to reduce false positives by correlating alerts from various security tools and prioritizing them based on risk. This aggregation of alerts and risk prioritization allows human analysts to focus on the most critical threats, improving efficiency and reducing alert fatigue.
AI can automate and streamline many routine and complex tasks within the SOC, making analysts' jobs easier and more efficient. One significant use case is creating a summarization of an alert to produce an easy-to-understand explanation. Instead of sifting through raw logs and technical data, SOC analysts receive concise summaries that highlight the key aspects of an alert, enabling quicker comprehension and understanding of why the alert was triggered.
Beyond summarization, AI can also provide next steps for triage and investigation. This means offering automated recommendations for initial response actions, such as isolating a compromised endpoint or blocking a malicious IP address. For more complex incidents, AI can guide analysts through the investigative process, suggesting relevant data sources to examine and potential lines of inquiry.
As a final step in closing the loop on an investigation, AI can provide recommendations for security enhancements to prevent the attack from taking place again. By offering guidance and suggestions on recommended actions, SOC analysts can learn what steps are necessary to prevent future attacks.
By assisting with workflows, AI can help bolster SOC team members' productivity, and can help quickly bring new analysts up to speed and increase their efficiency.
An AI-enabled ecosystem facilitates seamless integration and collaboration across the entire security infrastructure. Technologies like an MCP (Multi-Cloud Platform) server can make integration easier by providing a centralized hub for managing and orchestrating various security tools and data sources. This allows various organizational AI systems to access and analyze information from disparate systems, creating a more holistic view of the threat landscape.
Furthermore, playbooks and promptbooks, developed for use with AI, help SOC analysts find the information they are looking for quickly and make the best use of AI tools like Large Language Models (LLMs). AI can dynamically adapt playbooks based on the context of an incident, providing analysts with real-time guidance and access to relevant knowledge bases. Promptbooks help SOC analysts use the right prompts and natural language queries for use with LLMs to assist in incident response, threat intelligence, summarizing complex reports, or even drafting incident response communications, further empowering SOC analysts.
Enhance threat detection
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Corelight's high-quality network evidence empowers AI to perform more accurate behavioral analysis and anomaly detection, leading to faster identification of sophisticated and evasive threats.
Augment existing SOC tools
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Corelight's network evidence can be easily integrated with existing SIEMs, XDRs, and other security tools, enriching their data and improving the overall effectiveness of your AI-driven SOC ecosystem.