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| Section | Weight | Objectives |
|---|---|---|
| Infrastructure & Networking | 9% | - Network security architecture - Cloud native networking security - Virtualization security - Segmentation and isolation |
| Cloud Governance | 9% | - Policies and procedures - Strategic alignment - Cloud service provider management - Governance frameworks |
| Cloud Workload Security | 9% | - Serverless security - Workload protection strategies - Virtual machine security - Container and orchestration security |
| Data Security | 10% | - Data lifecycle management - Data classification and governance - Data residency and privacy - Encryption and key management |
| Incident Response & Resilience | 7% | - Business continuity and disaster recovery - Recovery and remediation - Incident response planning - Detection and containment |
| Organization Management | 7% | - Roles and responsibilities - Cloud security strategy - Security culture and awareness - Tenancy and resource management |
| Related Technologies & Strategies | 6% | - Zero Trust architecture - Emerging technologies and risks - Artificial Intelligence and Generative AI security |
| Risk, Audit, & Compliance | 10% | - Risk assessment and management - Audit processes and controls - Third-party risk management - Compliance frameworks and regulations |
| Cloud Computing Concepts & Architectures | 8% | - Shared responsibility model - Cloud deployment models - Cloud reference architecture - Cloud service models |
| Identity & Access Management | 10% | - Federated identity and SSO - Identity lifecycle management - Authentication and authorization - Access control models |
| Security Monitoring | 8% | - Logging and event management - Continuous monitoring - Threat detection and analysis - Alerting and response workflows |
| Application Security | 7% | - DevSecOps practices - Secure software development lifecycle - API security - Cloud application architecture |
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NEW QUESTION # 76
An organization deploys an AI application for fraud detection. Which threat is MOST likely to affect its AI model's accuracy?
Answer: C
Explanation:
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.
NEW QUESTION # 77
When a cloud customer uploads PII to a cloud provider. who becomes ultimately responsible for the security of that PII?
Answer: D
Explanation:
Under current law, the data owner is responsible for any breaches that result in unauthorized disclosure of PII; this includes breaches caused by contracted parties and outsources services. The data owner is the cloud customer.
NEW QUESTION # 78
Private clouds can be hosted off-premises as well.
Answer: B
Explanation:
It is true. This is how Private cloud is defined.
Private Cloud: The cloud infrastructure is operated solely for a single organization. It may be managed by the organization or by a third party and may be located on-premises or off-premises.
NEW QUESTION # 79
What is a primary benefit of implementing Zero Trust (ZT) architecture in cloud environments?
Answer: D
Explanation:
Zero Trust (ZT) security architecture is a modern cloud security approach that operates on the principle of "Never Trust, Always Verify." Primary Benefits of Zero Trust in Cloud:
* Minimizes Attack Surface
* Traditional security models assume trust within an internal network.
* Zero Trust eliminates implicit trust and enforces continuous verification of user identities.
* Reduces the risk of data breaches, insider threats, and lateral movement attacks.
* Strong Authentication & Access Controls
* Multi-Factor Authentication (MFA) & Just-in-Time (JIT) access are mandatory in Zero Trust models.
* Uses context-based access policies (device, location, behavior analytics) to enforce adaptive security.
* Micro-Segmentation & Least Privilege Access
* Restricts access to only necessary applications, minimizing lateral movement in cloud environments.
* Micro-segmentation isolates workloads, reducing the impact of breaches.
* Cloud-Native Zero Trust Integration
* Cloud providers (AWS, Azure, Google Cloud) offer Zero Trust Network Access (ZTNA) solutions.
* Cloud Security Posture Management (CSPM) continuously scans cloud environments for security compliance.
This aligns with:
* CCSK v5 - Security Guidance v4.0, Domain 12 (Identity, Entitlement, and Access Management)
* Zero Trust Cloud Security Architecture (CSA Zero Trust Working Group).
NEW QUESTION # 80
CCM: The Cloud Service Delivery Model Applicability column in the CCM indicates the applicability of the cloud security control to which of the following elements?
Answer: D
NEW QUESTION # 81
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