Free Cloud Security Alliance CCSK Practice Exam with Questions & Answers | Set: 3

Serverless computing shifts infrastructure management responsibility to the CSP, allowing customers to focus on application logic rather than infrastructure. Reference: [Security Guidance v5, Domain 8 - Cloud Workload Security]

Cloud adoption transforms how incident response (IR) is conducted. Unlike traditional IT environments, cloud environments involve shared responsibility, provider collaboration, and remote orchestration. This shift requires security teams to adjust response strategies, tools, and governance to effectively detect, analyze, and remediate incidents.

Cloud-specific tools (e.g., CSP logs, API calls, auto-scaling environments) must be incorporated into IR plans. Coordination with cloud service providers is often necessary to access logs, enforce controls, or conduct forensics.

This transformation is outlined in Domain 9: Incident Response, which stresses that effective IR in the cloud must be pre-planned and adapted to each provider and cloud model.

The principle of least privilege limits access to only necessary permissions, reducing the risk of misuse and exposure of sensitive data. Reference: [CCSK v5 Curriculum, Domain 5 - IAM]

Among AI workloads,Trainingrequires themost computational power and data resources.

Why AI Training is Computationally Intensive?

Large datasets:

AI models (e.g., deep learning, neural networks)require millions or billions of labeled data points.

Training involvesprocessing massive amounts of structured/unstructured data.

High computational power:

Training deep learning modelsinvolves runningmultiple passes (epochs) over data, adjusting weights, and optimizing parameters.

Requiresspecialized hardwarelikeGPUs (Graphics Processing Units),TPUs (Tensor Processing Units), andHPC (High-Performance Computing).

Long training times:

AI model training can takedays, weeks, or even monthsdepending on complexity.

Cloud platforms offerdistributed computing (multi-GPU training, parallel processing, auto-scaling).

Cloud AI Training Benefits:

Cloud providers (AWS, Azure, GCP) offer ML training serviceswithon-demand scalable compute instances.

Supportsframeworks like TensorFlow, PyTorch, and Scikit-learn.

This aligns with:

CCSK v5 - Security Guidance v4.0, Domain 14 (Related Technologies - AI and ML Security)

Cloud AI Security Risks and AI Data Governance (CCM - AI Security Controls)​

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]

A key aspect of cloud risk management is taking a structured approach to identify, assess, and address risks related to using cloud services. This includes evaluating potential risks such as security vulnerabilities, data privacy issues, service outages, and compliance challenges. Effective risk management helps organizations proactively mitigate potential threats, ensuring the cloud environment is secure, compliant, and resilient.

A structured approach for performance optimization of cloud services is more related to performance management, not risk management. A structured approach to establishing the different what/if scenarios for cloud vs on-premise decisions refers to decision-making scenarios, not the identification and management of risks. A structured approach to SWOT analysis) is a strategic planning tool that focuses on strengths, weaknesses, opportunities, and threats, but it is not specifically focused on cloud risk management.