Pass the Cloud Security Alliance Cloud Security Knowledge CCSK Questions and answers with Dumpstech

Real-time visibility allows for monitoring container behavior during runtime, helping to identify and respond to security incidents as they occur. Reference: [Security Guidance v5, Domain 8 - Cloud Workload Security]

One of the primary advantages of including Static Application Security Testing (SAST) in Continuous Integration (CI) pipelines is that it allows developers to identify code vulnerabilities early in the development process. By scanning the source code for potential security issues as it is being written and integrated into the pipeline, SAST helps to catch vulnerabilities before they make it to later stages of development or production, improving overall security and reducing the cost and effort of fixing issues later.

While SAST does not directly impact the speed of deployment, runtime performance, or user interface, its early identification of security flaws contributes to better code quality and a more secure application.

Compliance inheritance refers to the practice in cloud compliance where a customer leverages a set of pre-approved regulatory or standards-based controls that have been established and validated by a compliant cloud provider. Essentially, the cloud provider implements these controls, and the customer inherits the provider's compliance framework to meet their own regulatory requirements. This allows customers to benefit from the provider’s compliance efforts without having to implement everything themselves.

Automated compliance refers to automating compliance tasks and processes but does not describe the practice of inheriting compliance controls. Attestation inheritance is not a standard term used in cloud compliance; attestation typically refers to formally certifying or declaring compliance. Audit inheritance would relate to the inheritance of audit reports or records, but it doesn't describe the broader process of inheriting compliance controls.

Serverless environments are vulnerable to misconfigurations, which can expose sensitive data and resources, making security configurations critical. Reference: [Security Guidance v5, Domain 8 - Cloud Workload Security][16†source].

Infrastructure as a Service (IaaS) primarily provides users with storage, computing resources, and networking capabilities. In the IaaS model, cloud providers offer virtualized computing resources over the internet. Users can rent servers, storage, and networking equipment without needing to manage the physical hardware themselves. This allows for flexible scaling and resource management according to the users' needs.

FaaS focuses on serverless computing where users run code in response to events. PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. SaaS delivers fully managed applications over the internet, where users access software without managing the infrastructure.

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)​

Serverless functions are designed to run in isolated, ephemeral environments, meaning that each execution is independent and temporary. These functions are typically event-driven and executed on-demand, without the need for pre-allocated server resources. Once the function finishes executing, the environment is discarded, making it highly efficient and scalable. This architecture abstracts away infrastructure management, allowing developers to focus on the code itself.