Cloud Security Alliance CCSK - Certificate of Cloud Security Knowledge v5 (CCSKv5.0)

Correct Option: A. Failure to update access controls after employee role changes

This falls under one of the most common risk factors related to cloud misconfiguration and poor change management. Misconfiguration errors often stem from insufficient change control, especially in dynamic environments like the cloud. According to CSA’s Security Guidance v4.0, poor governance of identity and access management (IAM) changes — such as not updating access privileges when user roles change — introduces serious security risks.

"Cloud computing is dynamic by nature. This places more importance on automation and proper governance, especially for identity and access control. Failure to remove or update access permissions after personnel changes leads to orphaned or over-permissioned accounts, which are prime targets for attackers."

— Domain 2: Governance and Enterprise Risk Management, CSA Security Guidance v4.0

Also highlighted in ENISA’s Cloud Risk Assessment:

"Loss of governance includes failing to maintain proper control over access privileges and role assignments. Poor change management and inadequate configuration reviews can leave systems open to unauthorized access."

— ENISA Cloud Computing Risk Assessment, Section R.2: Loss of Governance

Why the Other Options Are Incorrect:

B. Lack of sensitive data encryption: While encryption is critical, it is not directly tied to change control or misconfiguration, but rather falls under Data Security and Encryption domain.

C. Lack of 3rd party service provider specialized in patch management procedures: This refers more to vendor management and Security-as-a-Service, not internal change control or misconfigurations.

D. Excessive SBOM focus: Software Bill of Materials (SBOM) is important for supply chain transparency, but excessive focus on it isn’t a typical misconfiguration or change control risk.

In federated identity management, the identity provider (IdP) is responsible for authenticating users and making assertions about their identity to the relying party (which could be a service or application that trusts the IdP). The IdP and the relying party establish a trust relationship in advance, which allows the IdP to assert that a user is authenticated, often in the form of security tokens or assertions like SAML or OpenID Connect.

The IdP that authenticates users and makes assertions, not the relying party. The relying party does not make assertions to the IdP; the relying party relies on assertions made by the IdP. The IdP and relying party do have a direct trust relationship in federated identity management.

The primary function ofData Encryption Keys (DEK)in cloud security is toencrypt application data. DEKs are used to encrypt and decrypt specific data objects, such as files or database records, ensuring data confidentiality in cloud environments.

From theCCSK v5.0 Study Guide, Domain 10 (Data Security and Encryption), Section 10.3:

“Data Encryption Keys (DEKs) are used to encrypt and decrypt application data in cloud environments. DEKs are typically managed by key management services and applied to specific data objects to ensure confidentiality and protect against unauthorized access.”

Option B (To encrypt application data) is the correct answer.

Option A (Increase speed) is incorrect because encryption does not enhance performance.

Option C (Manage user access control) is incorrect because DEKs are for encryption, not access control.

Option D (Primary key for all resources) is incorrect because DEKs are specific to data encryption, not resource management.

During the Detection and Analysis phase of incident response, the primary objective is to validate alerts to determine whether they represent a genuine security incident, and to estimate the scope of the incident to understand the potential impact on the organization. This phase involves analyzing evidence, confirming the nature of the incident, and gathering the necessary information to move forward with containment and remediation.

Developing and updating incident response policies is important but occurs more during the preparation phase, not during the detection and analysis of an active incident. Performing detailed forensic investigations typically takes place during later phases, such as Containment, Eradication, & Recovery or Post-Incident Analysis. Implementing network segmentation and isolation may be part of the Containment phase but is not the primary focus during the Detection and Analysis phase.

Cloud Detection and Response (CDR) is an emerging capability that focuses specifically on detecting and responding to threats in cloud environments. While not deeply detailed in the core CSA Security Guidance v4.0, CDR is an evolution of traditional SIEM and endpoint detection strategies applied to cloud-native infrastructures.

In CSA Security Guidance v4.0 – Domain 9: Incident Response, it’s made clear that:

“Security monitoring and detection capabilities in the cloud must be able to identify suspicious behavior, policy violations, and misconfigurations — often across multiple layers such as infrastructure, applications, and identity.”

— CSA Security Guidance v4.0, Domain 9: Incident Response

CDR platforms typically include:

Threat detection across cloud workloads (e.g., compute, storage, IAM misuse)

Real-time alerts

Automated or manual response mechanisms

Integration with cloud-native logging services like AWS CloudTrail, Azure Monitor, or GCP Audit Logs

Incorrect options:

B is about application management, not threat detection.

C relates to cloud cost optimization tools.

D refers to cloud storage tuning, unrelated to threat detection.

Correct Option: A. Adversarial attacks

Adversarial attacks are specifically designed to deceive AI and machine learning models by feeding them crafted inputs that result in incorrect outputs. These attacks are highly effective against AI models, especially in areas like fraud detection, where accuracy is critical.

From CSA Security Guidance v4.0 – Domain 13: Security as a Service (SecaaS) and related AI-focused security discussions:

“AI models are vulnerable to adversarial inputs, where attackers introduce subtle perturbations to input data that are imperceptible to humans but cause the AI system to make wrong decisions. These attacks degrade the accuracy and reliability of machine learning models.”

— CSA Guidance on AI Security (in Security as a Service domain)

Adversarial ML is a well-recognized field of AI security, where the goal of the attacker is to intentionally corrupt or manipulate input data, thereby lowering the performance or biasing the output of the model.

Why the Other Options Are Incorrect:

B. DDoS attacks➤ Affects availability, not accuracy. DDoS can cause downtime but doesn’t interfere with model predictions.

C. Third-party services➤ May introduce supply chain or dependency risks, but they don’t directly impact the AI model’s accuracy unless involved in training data pipelines.

D. Jailbreak attack➤ More relevant to LLMs (Large Language Models) or chatbots, not structured AI fraud detection models.