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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk and Compliance | 19% | - Compliance and legal requirements
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - Core AI principles and terminology
|
| Topic 3: Securing AI Systems | 40% | - Security controls for AI systems
|
| Topic 4: AI-assisted Security | 24% | - AI for threat detection and response
|
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NEW QUESTION # 21
A security administrator must implement security controls for AI systems.
Which of the following access controls should the administrator set up first for authentication?
Answer: A
Explanation:
Basic Concept: In a layered AI system security architecture, access control must be established at each layer, beginning from the outermost point of entry. Authentication must be established at the endpoint level first, as this is the first point of interaction between users and the AI system. CompTIA SecAI+ Study Guide establishes endpoint authentication as the initial access control layer for AI systems.
Why D is Correct: Endpoint access control is the first authentication control to implement because it governs the initial connection from user devices or client applications to the AI system. All subsequent access layers including server access, model access, and data access depend on the endpoint being authenticated first.
Establishing endpoint authentication ensures that only authorized endpoints can initiate sessions and proceed through subsequent authentication layers.
Why A is Wrong: Model access controls govern who can query, update, or access the AI model ' s parameters and functions. This control layer is implemented after endpoint authentication has been established, as it applies to requests that have already been authenticated at the endpoint level.
Why B is Wrong: Server access controls manage access to the computing infrastructure hosting the AI system. While critical for infrastructure security, server-level controls are configured by administrators and are not the first authentication control for end-user access flows.
Why C is Wrong: Data access controls define what data the AI system and its users can read, write, or query.
These are implemented at a deeper layer after endpoint and potentially model authentication have verified that the requester is authorized to interact with the system at all.
NEW QUESTION # 22
A security consultant must summarize the impact of posture management on a machine learning (ML) use case. Which of the following is the most appropriate reference for this purpose?
Answer: B
Explanation:
The NIST AI RMF provides structured guidance for assessing and managing risks across the AI lifecycle, including posture management. It helps organizations align AI security practices with governance, resilience, and trustworthiness requirements.
NEW QUESTION # 23
A company uses human review for software development validation and wants to add another validation layer. Which of the following should a security administrator use to accomplish task?
Answer: B
Explanation:
AI-assisted approval adds an automated validation layer alongside human review, helping detect potential security or quality issues in software development before deployment. This strengthens the overall validation process.
NEW QUESTION # 24
A cybersecurity analyst wants to choose a machine learning (ML) model to classify log entries while providing the best explainability.
Which of the following models should the analyst use?
Answer: B
Explanation:
Basic Concept: Different ML model architectures offer varying degrees of explainability. In cybersecurity, understanding why a model classified a log entry as malicious or benign is critical for analyst trust, investigation, and regulatory compliance. CompTIA SecAI+ covers model explainability under responsible AI and basic AI concepts.
Why C is Correct: Decision trees are inherently interpretable models that classify data through a series of transparent if-then rules. Every classification decision can be traced through the exact path of conditions that led to it, showing precisely which log entry features triggered the classification. Analysts can read and understand the decision path, making decision trees the gold standard for explainable ML classification in security applications where understanding the reason for a classification is as important as the classification itself.
Why A is Wrong: Large language models are complex transformer architectures with hundreds of billions of parameters. They function as black boxes - their internal decision-making processes are not human- interpretable, making them poor choices when explainability is the primary requirement.
Why B is Wrong: Neural networks are non-linear black box models. While they can achieve high classification accuracy, their multi-layer architecture makes it extremely difficult to explain why specific decisions were made in human-understandable terms.
Why D is Wrong: Generative adversarial networks are designed for generating synthetic data, not for classification tasks. They consist of competing generator and discriminator networks and are fundamentally unsuitable for log entry classification with explainability requirements.
NEW QUESTION # 25
An administrator must conduct generative AI cost monitoring for use in the healthcare industry.
Which of the following criteria is the best way to calculate this cost?
Answer: A
Explanation:
Basic Concept: Generative AI systems in healthcare settings incur costs from multiple operational activities.
Understanding the cost drivers specific to generative AI helps administrators implementaccurate cost monitoring and controls. CompTIA SecAI+ Study Guide covers AI cost management under securing AI systems.
Why C is Correct: Storage retrieval and prompt processing are the two primary cost drivers for generative AI systems in healthcare. Storage retrieval refers to the cost of querying vector databases or document stores in RAG-based AI systems to fetch relevant patient records, clinical guidelines, or historical data for context.
Prompt processing encompasses the token-based cost of the LLM processing the combined retrieved content and user query to generate a response. Together these two activities represent the billable units that drive generative AI costs in healthcare RAG deployments, making them the most accurate basis for cost calculation and monitoring.
Why A is Wrong: Connection access and exchange gateway costs relate to network infrastructure and API gateway usage fees. While there may be minor costs associated with API calls, these are not the primary cost drivers for generative AI systems where the dominant expenses are computational token processing and data retrieval operations.
Why B is Wrong: Encryption and decryption processing costs relate to cryptographic operations for data security. While encryption is important for healthcare data protection under HIPAA, cryptographic processing overhead is minimal compared to the substantial token-based LLM processing and storage retrieval costs that dominate generative AI operational expenses.
Why D is Wrong: Catalog servicing and exchange processing are terms associated with data catalog management and data exchange infrastructure. These are not recognized primary cost components of generative AI systems in healthcare, where storage retrieval and token-based prompt processing are the established cost measurement criteria.
NEW QUESTION # 26
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