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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-assisted Security | 24% | - Security automation and orchestration
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| Topic 3: Securing AI Systems | 40% | - Security controls for AI systems
|
| Topic 4: AI Governance, Risk and Compliance | 19% | - Compliance and legal requirements
|
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NEW QUESTION # 63
Which of the following helps in managing potential security issues related to model training?
Answer: D
Explanation:
Basic Concept: Managing security risks in AI model training requires a comprehensive framework specifically designed for AI risk identification, assessment, and mitigation across the entire AI lifecycle including data collection, training, and deployment. CompTIA SecAI+ Study Guide identifies NIST AI RMF as the primary resource for AI-specific risk management.
Why A is Correct: The NIST AI Risk Management Framework is purpose-built for managing risks throughout the AI lifecycle. It provides structured guidance for identifying, assessing, and mitigating risks specific to AI systems including training data quality, model bias, data poisoning, and training pipeline vulnerabilities. Its AI-specific scope makes it the most appropriate framework for managing model training security issues.
Why B is Wrong: ISO 27001 is an information security management system standard focused on general IT security controls and risk management. It does not specifically address AI model training risks, data pipeline integrity, or ML-specific vulnerabilities.
Why C is Wrong: The OECD provides high-level AI governance principles and policy recommendations at an international level. It offers ethical and policy guidance but does not provide operational risk management guidance for securing AI model training processes.
Why D is Wrong: GDPR is a European data protection regulation focused on personal data privacy, consent, and individual rights. While relevant to training data governance, it does not address the technical security risks of model training pipelines or ML system vulnerabilities.
NEW QUESTION # 64
An AI architect reviews AI utilization and wants to improve the user experience. Which of the following should the architect review within the logs?
Answer: B
Explanation:
To improve user experience, the architect should review model accuracy in the logs. High accuracy ensures users receive relevant, reliable responses, directly impacting satisfaction and effectiveness of the AI system.
NEW QUESTION # 65
Instructions: Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.
Answer:
Explanation:
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a HOTSPOT/simulation item requiring interactive selection in the actual exam. It tests the candidate ' s ability to map appropriate security controls to AI system components such as API gateway, model endpoint, data layer, and authentication layer.
Key Concept - Appropriate Controls by Component: For an API gateway connecting an AI system, typical controls include API key authentication, rate limiting, TLS encryption, and input validation. For the model endpoint, controls include IAM role-based access, audit logging, and guardrails. For data access components, encryption at rest and data masking are appropriate. For the authentication layer, MFA and expiring session tokens are relevant.
Why This Matters: The CompTIA SecAI+ Study Guide emphasizes defense-in-depth for AI system integration, ensuring each architectural layer has dedicated, appropriate security controls. The principle of least privilege should guide access control assignments at each component, while availability controls such as rate limiting protect against abuse.
Reference: CompTIA SecAI+ Exam Objectives Domain 2 (Securing AI Systems) covers AI system component security controls. Candidates should study the mapping of controls to infrastructure components including API gateways, model serving endpoints, data stores, and identity management layers. In the live exam, select the most specific and directly relevant control for each component based on the component ' s function and risk profile.
NEW QUESTION # 66
An organization wants to reduce vulnerabilities after deployment. The organization decides to incorporate an AI-assisted early detection and vulnerability identification process in its development workflow.
Which of the following AI-assisted functions is the best option?
Answer: A
Explanation:
Basic Concept: Reducing post-deployment vulnerabilities requires catching security issues as early as possible in the development workflow. AI-assisted tools that analyze code during development provide the earliest possible intervention point. CompTIA SecAI+ Study Guide covers AI integration in secure development under AI-assisted security.
Why A is Correct: AI-assisted code linting analyzes source code in real time during development to identify security vulnerabilities, insecure coding patterns, policy violations, and quality issues before code is compiled or committed. By catching vulnerabilities at the coding stage - the earliest possible point in the development workflow - AI code linting prevents vulnerable code from progressing to testing, staging, or production, directly reducing post-deployment vulnerabilities at their source.
Why B is Wrong: Incident management handles security events and incidents after they have occurred in production. It is a reactive capability focused on response and recovery rather than early-stage vulnerability identification in the development workflow.
Why C is Wrong: Automated deployment/rollback automates the process of pushing code to production and reverting to previous versions when issues are detected post-deployment. It is a deployment safety mechanism rather than an early detection tool during the development phase.
Why D is Wrong: System auditing reviews and records system activities and configurations for compliance verification. It is primarily a detective and compliance control for systems that are already deployed, not an early development-phase vulnerability identification tool.
NEW QUESTION # 67
A data set containing medical information is put into a machine learning (ML) model that is designed to predict specific illnesses for a population. In the process of verifying the reliability of the system, the compliance officer realizes that the system cannot reliably predict illnesses for certain segments of the population.
Which of the following types of risk is most applicable to this case?
Answer: B
Explanation:
Basic Concept: AI models trained on unrepresentative data can produce systematically inaccurate results for certain population groups. This is a form of algorithmic bias where the model ' s performance varies significantly across demographic segments, creating disparate outcomes. CompTIA SecAI+ Exam Objectives cover bias as a core AI governance and risk concept.
Why A is Correct: Bias in AI occurs when a model produces systematically skewed results for certain groups due to biased training data, flawed data collection, or model design choices. In this healthcare scenario, the inability to reliably predict illnesses for specific population segments indicates the training data likely underrepresented those segments, causing the model to learn inadequate patterns for them. This is a critical bias risk with serious health equity implications.
Why B is Wrong: Consistency refers to the model producing the same output given the same input across different runs or time periods. The problem described is not about inconsistent outputs for the same input but about systematically poor performance for specific population groups.
Why C is Wrong: Transparency refers to openness about how the AI model operates, what data it uses, and how it makes decisions. The compliance officer has already assessed the system, suggesting sufficient transparency exists to identify the performance gap.
Why D is Wrong: Inclusiveness is a design principle ensuring AI systems are designed to serve all users regardless of background. While related to the outcome, the specific risk type described - differential predictive accuracy across population segments - is most precisely categorized as bias.
NEW QUESTION # 68
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