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AI is revolutionizing the world, but it also opens the door for hackers to create new types of attacks that evade traditional security measures. The key to protecting against these emerging threats is understanding what AI cybersecurity is, how different tools work together to protect against common attack paths, and how to choose the right solution for your business.

SIEM Integrations: AI-powered solutions integrate with existing SIEM platforms to detect and analyze threats in a unified platform. They leverage world-scale telemetry to improve detection accuracy and threat context. These tools are well-suited for large environments with centralized security management.

Endpoint Detection and Response: AI-powered tools combine user behavior analytics with advanced machine learning to identify suspicious activity on individual devices. They use a combination of user telemetry, device telemetry, and network flows to establish baselines for “normal” behavior and detect deviations. They are typically used in data centers and cloud environments.

Anomaly Detection: AI-powered tools use real-time pattern analysis to identify anomalous traffic patterns, at-risk servers, and ransomware threats without relying on manual thresholds or signatures. They use world-scale telemetry to help prioritize critical alerts and reduce noise so teams can focus on threat context.

Vulnerability Management: AI-powered vulnerability management tools ingest source code, runtime environment, and application context to detect and prioritize risks. They use reachability analysis to filter false positives and confirm true ones. They can automatically test and remediate vulnerabilities with a one-click auto-triage mechanism and support IDE, PR, and CI/CD workflows.