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Aim for measurable progress:

When it comes to AI cybersecurity, you must measure the results you achieve. Look for tools that provide clear, tangible risk reduction in three key areas:

Speed:

Compared to human analysts who spend 20 minutes assessing each alert, AI processes and correlates thousands of events in milliseconds to identify critical ones. That speeds up SOC operations and improves threat detection and response times.

Accuracy:

As AI models train, they use massive amounts of data to look for characteristics of cyberattacks – things like suspicious network behavior or unusual login attempts. It also searches for vulnerabilities and signs of compromise, such as suspicious files, unauthorised access to systems, or malware activity. It then analyzes the data to figure out what the threats are, how serious they may be, and which systems are at risk of a breach. If the threat is a real attack, AI can automatically respond, blocking access, isolating compromised devices, or stopping malware in its tracks.

Continuous learning systems use feedback from security analysts to continuously improve threat detection and reduce false positives. They also retrain and update the model frequently to avoid being outwitted by sophisticated adversaries. For example, an AI SOC platform uses threat telemetry to adjust its risk thresholds to account for unique business and operational risks. This ensures that threat detection and response remains accurate, reducing risk over time. Look for tools that offer human-assisted autonomy: they allow security experts to review and approve AI-driven decisions, preserving their expertise, judgment, and intuition for high-level decision-making.