AI can enhance anomaly detection in cybersecurity by analyzing large volumes of data and identifying patterns that deviate from normal behavior. By using machine learning algorithms, AI can improve the accuracy of anomaly detection, reducing false positive alerts. This helps security teams focus on genuine threats while minimizing distractions from irrelevant alerts.
Assisting analysts is a valid benefit of AI, but reducing false positives directly improves anomaly detection capabilities. Threat intelligence refers to gathering and analyzing information about potential threats but isn't directly focused on reducing false positives in the same way as anomaly detection. Automated responses can be part of AI's role in cybersecurity, but reducing false positives is more directly related to improving anomaly detection.

Centralized logging aggregates logs in one location, making it easier to monitor, analyze, and comply with regulatory requirements. Reference: [Security Guidance v5, Domain 6 - Security Monitoring]

Encryption in object storage is used to secure stored data and protect it from unauthorized access, ensuring confidentiality. Reference: [Security Guidance v5, Domain 9 - Data Security]