The Detection and Analysis phase involves identifying incidents and determining their impact. It is crucial to validate events to understand if they constitute a security incident. Reference: [Security Guidance v5, Domain 11 - Incident Response]

In the Infrastructure as a Service (IaaS) shared responsibility model, the Cloud Service Provider (CSP) is typically responsible for securing the physical infrastructure, which includes the physical security of data centers, servers, networking hardware, and the physical security controls that protect them from unauthorized access or damage.
Encrypting data at rest is typically the responsibility of the consumer, though the CSP may offer tools to help with this. Managing application code is the responsibility of the consumer, as they control and deploy the applications on the infrastructure provided by the CSP. Configuring firewall rules is also the responsibility of the consumer, as they manage the configuration of the virtual network, including security rules like firewalls.

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.

A Service Registry lists approved services, making it easy for teams to find and integrate compliant services. Reference: [CCSK Knowledge Guide, Domain 3 - Risk and Compliance Tools]