Online CCSK Questions Video

Compensating controls are implemented when the required controls for a cybersecurity framework cannot be met due to technical or practical limitations. These controls are alternative measures that provide similar protection or risk mitigation. Compensating controls help to ensure that the security posture remains strong even when the primary controls cannot be applied.

Detective controls focus on identifying security incidents after they occur but do not replace required controls. Preventive controls aim to prevent security incidents from occurring but may not always be possible or practical to implement in certain situations. Administrative controls include policies and procedures but do not address the need for compensating measures when technical controls cannot be met.

The NIST (National Institute of Standards and Technology) defines the essential characteristics of cloud computing as:

On-demand self-service: Users can provision and manage computing resources automatically without requiring human intervention from the service provider.

Broad network access: Cloud services are accessible over the network through standard mechanisms, enabling access from various devices and locations.

Resource pooling: Cloud providers pool computing resources to serve multiple consumers, with resources dynamically assigned and reassigned according to demand.

Rapid elasticity: Cloud resources can be rapidly scaled up or down to meet varying demand.

Measured service: Cloud services are metered, and customers pay based on their usage, which allows for cost efficiency.

These characteristics define how cloud computing services are provided and accessed, focusing on flexibility, scalability, and efficiency.

Data classification is a fundamental security practice used to protect sensitive information based on risk, confidentiality, integrity, and regulatory requirements.

Key Factors in Data Classification:

Data Sensitivity:

Organizations classify data based on how sensitive it is:

Public (e.g., marketing material).

Internal Use Only (e.g., business plans).

Confidential (e.g., financial reports).

Restricted/Highly Confidential (e.g., personal healthcare records, credit card details).

Compliance & Legal Requirements:

Certain data types have strict compliance laws:

PII (Personally Identifiable Information) → GDPR, CCPA

Financial Data → PCI DSS

Healthcare Data → HIPAA

Cloud providers must ensure security policies align with compliance frameworks.

Impact on Security Controls:

Highly sensitive data requires encryption at rest and in transit.

Access control must be enforced with least privilege and IAM policies.

Risk Management:

Proper data classification helps organizations define security policies such as:

Retention policies (How long data should be stored?).

Backup and disaster recovery strategies.

This is outlined in:

CCSK v5 - Security Guidance v4.0, Domain 11 (Data Security and Encryption)

Cloud Controls Matrix (CCM) - Data Security and Data Classification Standards​

The correct answer is D. Virtual Machines (VMs). In the context of cloud computing, Virtual Machines (VMs) are software-based emulations of physical computers. They run an operating system (OS) and applications just like a physical machine would. VMs are often hosted on physical servers using hypervisors, which allow multiple VMs to run on a single physical machine, thereby sharing resources like CPU, memory, and storage.

Why Virtual Machines (VMs) are Suitable for Data Processing:

Full OS Environment: VMs provide a complete operating system environment, making them suitable for running complex data processing tasks that require specific OS configurations.

Isolation: Each VM operates independently, providing isolation between different workloads, which is essential when processing sensitive or diverse data sets.

Scalability: Cloud providers offer VM scaling options to meet the demands of data processing workloads.

Compatibility: VMs can run legacy applications that may not be compatible with newer cloud-native technologies.

Why Other Options Are Incorrect:

A. Platform as a Service (PaaS): PaaS provides a platform for developing and deploying applications without managing underlying infrastructure. It is not directly related to VM-based processing.

B. Serverless Functions (FaaS): Serverless computing abstracts the infrastructure and is used for running discrete functions rather than emulating entire machines.

C. Containers: Containers package applications and dependencies but share the host OS kernel. They are lightweight compared to VMs and do not fully emulate physical computers.

Real-World Example:

If a company moves a data processing application that was traditionally run on an on-premises physical server to the cloud, they might choose VMs on services like AWS EC2, Azure Virtual Machines, or Google Compute Engine to maintain the same OS environment and application compatibility.