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As the threat landscape evolves, cybersecurity teams must rely on more sophisticated defenses. AI can help them refocus their attention on what matters most: protecting data, minimizing risks and avoiding damage.

The best security AI tools offer proactive protection by detecting and addressing threats before they have the chance to take hold and spread across your environment. The most advanced platforms also automate repetitive tasks, allowing security analysts to focus on critical investigations and response.

Machine learning (ML) is an application of AI that enables systems to learn and improve without explicit programming. ML is commonly used for user and entity behavior analysis (UEBA) in cybersecurity, which detects and stops attacks by analyzing patterns of activity.

To improve mean time to detection and response, look for ML-powered solutions that provide valuable context to alerts and suggest next steps for analysts. ML-powered tools can also make it easier for analysts to prioritize alerts and reduce the number of false positives they receive.

Some ML-powered tools offer automation of repetitive, manual processes like monitoring and scanning so that SOC teams can focus on more complex investigative work. These capabilities can free up time to respond quickly to a wide range of threats, from ransomware to malware to phishing.

It is important to keep in mind that attackers will also target the AI tools defenders use and corrupt training data to skew results. To help mitigate this risk, look for solutions that audit and quantify privacy risks throughout the AI lifecycle. For example, the LF Adversarial Robustness Toolbox is a Python library that helps you assess the strength and resilience of your ML models against adversarial attack types like prompt injection and data poisoning.