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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| Securing AI Systems | 40% | - Security controls for AI systems
|
| AI Governance, Risk and Compliance | 19% | - Compliance and legal requirements
|
| AI-assisted Security | 24% | - Security automation and orchestration
|
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NEW QUESTION # 41
A company is adopting AI and wants to create policies and procedures that include a structure for evaluating, publishing, and approving patterns for AI usage.
Which of the following should the company establish to meet this goal?
Answer: C
Explanation:
Basic Concept: Successful AI adoption at an organizational level requires a centralized governance body that standardizes AI practices, promotes best practices, and ensures consistent, safe, and effective AI deployment across the organization. CompTIA SecAI+ Study Guide covers AI organizational governance structures under Domain 4.
Why A is Correct: An AI Center of Excellence (CoE) is an organizational unit specifically designed to govern, standardize, and advance AI adoption. It develops and publishes policies, creates approved patterns for AI usage, evaluates new AI use cases, provides expert guidance, and maintains governance oversight. The CoE exactly matches the described need for a structure to evaluate, publish, and approve AI usage patterns across the organization.
Why B is Wrong: An AI legal affairs office focuses on legal compliance, intellectual property, and regulatory matters related to AI. While important for legal risk management, it does not fulfill the broader governance mandate of establishing and approving AI usage patterns and best practices across the organization.
Why C is Wrong: An AI audit department conducts post-implementation reviews and compliance assessments of existing AI systems. It is a retrospective and oversight function rather than a proactive body for developing and approving AI usage patterns and policies.
Why D is Wrong: An AI data science division is a technical team focused on building AI models and solutions. It is a development function rather than a governance structure designed to create policies, evaluate AI patterns, and provide cross-organizational oversight of AI adoption.
NEW QUESTION # 42
A company develops an AI model to diagnose patients. Hospitals access the model through an integrated application programming interface (API). The security team performs a denial-of-service (DoS) attack via brute force on the model.
Which of the following controls would have prevented this issue?
Answer: C
Explanation:
Basic Concept: API-based AI systems are susceptible to DoS attacks where excessive requests overwhelm the system ' s ability to respond to legitimate users. Rate limiting is the standard control for preventing both intentional and unintentional API abuse. CompTIA SecAI+ Study Guide covers rate limiting as a key availability control for AI APIs.
Why C is Correct: Rate limiting restricts the number of requests a client can make to an API within a defined time window. In this scenario, a brute-force DoS attack works by sending a massive volume of requests to exhaust the model ' s resources. Rate limiting would have automatically throttled or blocked the excessive request volume, preventing the attack from succeeding and preserving service availability for legitimate hospital users.
Why A is Wrong: Tokenization replaces sensitive data values with non-sensitive placeholders. It is a data security control for protecting sensitive information such as patient identifiers, not a control for managing API request volumes or preventing DoS attacks.
Why B is Wrong: Model guardrails filter and constrain model inputs and outputs for safety and policy compliance. They inspect content quality, not request volume, and cannot prevent a volume-based DoS attack.
Why D is Wrong: A prompt firewall inspects the content of prompts for malicious patterns or policy violations. Like guardrails, it analyzes content rather than controlling request frequency and cannot prevent resource exhaustion from a high-volume brute-force attack.
NEW QUESTION # 43
A vulnerability scan produces many false positives. What does this indicate?
Answer: C
Explanation:
High sensitivity → catches many issues but lowers accuracy (more false positives).
NEW QUESTION # 44
Which of the following roles best supports the implementation of AI governance, risk, and compliance (GRC)? (Choose two.)
Answer: A,B
Explanation:
Basic Concept: AI GRC implementation requires roles that combine understanding of AI technical capabilities and limitations with security risk assessment, control design, and compliance framework expertise. Identifying which roles naturally contribute to AI GRC is essential for team design. CompTIA SecAI+ Study Guide covers AI governance role responsibilities under Domain 4.
Why B is Correct: Data Scientists possess deep understanding of AI model capabilities, limitations, data requirements, and failure modes. For GRC implementation, their technical expertise is essential for identifying AI-specific risks such as bias, model drift, and data quality issues, assessing compliance implications of model design choices, and evaluating whether AI systems meet governance requirements.
Why D is Correct: Security Architects design comprehensive security frameworks and risk management strategies. For AI GRC, they translate governance requirements into technical controls, design AI security architectures that satisfy compliance obligations, assess the risk posture of AI deployments, and ensure security principles including least privilege, defense-in-depth, and audit logging are built into AI system designs.
Why A is Wrong: Desktop specialists manage user workstation hardware and software. Their role focuses on endpoint management and user support, not on the strategic risk assessment, compliance evaluation, or technical AI governance activities required for AI GRC implementation.
Why C is Wrong: Software developers write application code. While they implement security controls when directed, they typically lack the broad risk management, compliance framework expertise, and security architecture perspective needed to lead AI GRC implementation.
Why E is Wrong: SOC analysts focus on monitoring, detecting, and responding to security incidents in operational environments. Their expertise is in reactive security operations rather than the proactive governance framework design and compliance management that AI GRC requires.
Why F is Wrong: Network engineers design and maintain network infrastructure. Their expertise is in network connectivity and protocols, not in AI system governance, risk assessment frameworks, or compliance requirements.
NEW QUESTION # 45
Which of the following is most resistant to AI manipulation?
Answer: A
NEW QUESTION # 46
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