New Release CCSK Cloud Security Knowledge Questions

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.

An image factory is used in VM deployment to create standardized and secure virtual machine images. The primary role of the image factory is to automate the creation of these images, ensuring that all VMs deployed from the image are consistent in terms of configuration, security settings, and performance. By using an image factory, organizations can ensure that their VMs are secure (with the necessary security patches and settings), efficient (optimized for performance), and consistent (following the same configuration).

This process minimizes the risk of configuration drift and reduces manual intervention in VM deployment, leading to more efficient and secure operations.

In a cloud context, entitlement refers to the specific resources or services a user is granted permission to access based on their roles or permissions. This includes access to applications, data, or cloud services, and is typically managed through Identity and Access Management (IAM) systems, which define what users can do and what they can access within the cloud environment.

The primary benefit of implementing micro-segmentation within a Zero Trust Architecture is that it enhances security by isolating workloads from each other. Micro-segmentation involves dividing the network into smaller, isolated segments, so that even if an attacker gains access to one part of the network, they cannot easily move laterally to other parts. This is crucial in a Zero Trust model, which assumes that threats may exist both inside and outside the network, and security is enforced at a granular level for each workload.

Simplifying network design is not a benefit of micro-segmentation; in fact, it can add complexity due to the increased number of network segments. Increased network performance is not a primary outcome of micro-segmentation, which may introduce overhead. Reducing the need for encryption is incorrect because micro-segmentation doesn't eliminate the need for encryption; it works alongside encryption to provide better security.