CCSK Exam Dumps - Cloud Security Alliance Cloud Security Knowledge Questions and Answers

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)​

Effective cloud governance focuses on managing and overseeing cloud resources to ensure that security, compliance, and business objectives are met. Key aspects include:

Decision making: Establishing clear processes for how decisions are made regarding cloud resource usage, security measures, and compliance strategies.

Prioritization: Ensuring that critical security and compliance risks are prioritized and addressed first.

Monitoring: Continuously monitoring cloud environments for security threats, performance issues, and compliance violations.

Transparency: Ensuring that governance activities are visible to stakeholders, enabling accountability and making it easier to demonstrate compliance with laws, regulations, and internal policies.

These aspects help organizations maintain control over their cloud environments while ensuring they meet security and regulatory requirements.

One of the biggest challenges incloud security risk assessmentisthe lack of transparencyregardingcloud provider operations and security controls.

Key Issues with Limited Visibility:

Cloud providers manage infrastructure at a global scale:

Customerscannot directly inspectsecurity implementations.

Rely onthird-party attestationslikeSOC 2, ISO 27001, CSA STARinstead of direct assessments.

Multi-tenancy complexities:

Cloud customersshare infrastructurewith other tenants.

Data isolation mechanisms (e.g., virtual private clouds, encryption)must be trustedwithout direct verification.

Regulatory compliance challenges:

Organizations handling sensitive data (e.g., healthcare, finance)requirestrict controls.

Cloud providers may not offer sufficient audit logsor control overdata residency and processing.

Incident response limitations:

In traditional IT, organizations controllog access, forensic analysis, and recovery.

In the cloud,incident investigation depends on the provider’s logging and notification practices.

Thisvisibility issueis extensively covered in:

CCSK v5 - Security Guidance v4.0, Domain 4 (Compliance and Audit Management)

ENISA’s Cloud Computing Risk Assessment (Limited visibility into cloud provider security policies)​

Cloud governancefocuses onsecurity, risk management, and complianceto ensuredata protection, audit readiness, and regulatory adherence.

Key Elements of Cloud Security Governance:

Regulatory Compliance:

Organizations must comply withGDPR, HIPAA, PCI DSS, ISO 27001.

Cloud Security Posture Management (CSPM)helpsenforce complianceautomatically.

Security Policies & Controls:

Cloud governance frameworks includeIAM (Identity and Access Management), encryption policies, and workload isolation.

Organizations muststandardize security settingsacross multiple cloud environments.

Audit & Risk Management:

Implementcontinuous monitoring, security logging, and forensic readiness.

Risk-based access control policiesensuredata security across workloads.

Data Protection & Privacy:

Enforcingcloud-native security frameworks (e.g., Zero Trust, CASB, SIEM).

Data retention, access control, andincident responseareessential governance practices.

This is covered in:

CCSK v5 - Security Guidance v4.0, Domain 2 (Governance and Risk Management)

Cloud Security Alliance’s Cloud Controls Matrix (CCM) - Cloud Governance and Compliance Standards

Classification and cataloging help assign security controls andmanage data based on its sensitivity and criticality. Reference: [CCSK v5 Curriculum, Domain 9 - Data Security]