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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| Topic 3: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 4: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
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NEW QUESTION # 67
Which IR document defines who must be contacted during a breach and within what timeframe?
Answer: C
Explanation:
The communications plan specifies notification roles and timelines.
NEW QUESTION # 68
Which of the following should an auditor reference when reviewing a company ' s human resources AI systems for legal non-compliance?
Answer: A
Explanation:
Basic Concept: Various regulatory frameworks govern AI use in different contexts. For auditing legal compliance in high-risk AI applications such as employment and HR, binding regulatory legislation takes precedence over voluntary standards. CompTIA SecAI+ Exam Objectives cover AI governance and compliance frameworks under Domain 4.
Why C is Correct: The EU AI Act is the world ' s first comprehensive, legally binding AI regulation. It explicitly classifies AI systems used in employment, worker management, and recruitment as high-risk AI systems, subjecting them to strict compliance requirements including conformity assessments, transparency obligations, and human oversight mandates. An auditor reviewing HR AI for legal non-compliance must reference this binding legislation.
Why A is Wrong: The OECD AI Principles are non-binding international guidelines promoting responsible AI. They offer policy guidance but carry no legal enforcement power for compliance auditing.
Why B is Wrong: The NIST AI RMF is a voluntary, risk management-focused framework. It is not a legal compliance standard and cannot be used to assess legal non-compliance.
Why D is Wrong: ISO standards such as ISO 42001 are voluntary international best practice standards. They are not legal compliance instruments with enforceable penalties for HR AI systems.
NEW QUESTION # 69
Which of the following is used to train an AI model with unstructured data?
Answer: C
Explanation:
Fine-tuning allows an AI model to adapt to unstructured data (such as text, audio, or images) by retraining it on domain-specific datasets. This process improves the model's ability to handle and generate outputs aligned with the unstructured data context.
NEW QUESTION # 70
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 # 71
Faculty members at a university are concerned about potential inherent bias and inconsistency in one department ' s AI plagiarism detection service.
Which of the following principles will most likely address their concerns?
Answer: C
Explanation:
Basic Concept: Responsible AI principles each address different aspects of trustworthy AI behavior. When stakeholders are concerned about both bias and inconsistency - specifically that the same or equivalent work might receive different treatment from the AI system - the principle of consistency is most directly relevant.
CompTIA SecAI+ covers responsible AI principles under governance.
Why C is Correct: Consistency in AI systems means the model applies the same rules, standards, and decision criteria uniformly across all inputs and user groups without variation based on characteristics unrelated to the task. An AI plagiarism detection system that produces inconsistent results across different student submissions or demographic groups fails the consistency principle, which directly addresses both the bias concern (differential treatment) and inconsistency concern the faculty have raised.
Why A is Wrong: Transparency relates to openness about how the AI system works and what data it uses.
While valuable for understanding the system, transparency alone does not ensure that the system applies its rules uniformly or consistently.
Why B is Wrong: Explainability means the system can articulate why it made a particular decision. While useful for understanding individual cases, it does not guarantee that decisions are made with equal consistency across different submissions or groups.
Why D is Wrong: Accountability identifies who is responsible for AI system decisions and outcomes. It is a governance principle about ownership and responsibility rather than about ensuring uniform application of evaluation criteria.
NEW QUESTION # 72
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