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AI cybersecurity combines data and intelligence to search for patterns and indicators of compromise in massive volumes of traffic, endpoints, and other sources. It can detect anomalies, alert teams to suspicious activity, and thwart cyberattacks by isolating devices and stopping malware. It also helps identify high-risk areas of the network, giving security professionals the confidence to refocus efforts on what matters most.

A key use case for AI is to bolster existing endpoint detection and response (EDR) tools with real-time pattern analysis. AI solutions ingest and analyze threat data to build profiles of what normal behavior looks like for users—like how they access files or log in—and flag any actions that deviate from that baseline. This can include strange login times or data access from a different location, which could signal the presence of a cyberattack.

With sophisticated hacking tools and stealth infiltration techniques, organizations need a balance of human and AI security operations. While full automation can be useful in speeding up the response to low-risk issues, it comes with risks such as blocked users and a loss of vigilance.

Advanced AI SOC platforms ingest and integrate threat data across the entire enterprise, helping to uncover hidden threats and accelerate mean time to respond (MTTR). They prioritize actionable alerts by filtering out noise and surfacing high-fidelity detections. They can also reduce the number of false positives and enable security teams to quickly assess a risk before implementing mitigation recommendations.