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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| Topic 3: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 4: AI-Assisted Security | 24% | - Security Operations Enhancement
|
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NEW QUESTION # 138
A machine learning (ML) engineer is working with a security engineer to identify the best practices for securing a system with various AI models.
Which of the following actions should the engineers suggest?
Answer: C
Explanation:
Basic Concept: Securing AI systems requires a structured, end-to-end approach that addresses security at every phase of the AI model ' s lifecycle from data collection through training, testing, deployment, and ongoing monitoring. CompTIA SecAI+ Study Guide identifies the Model Development Life Cycle as the foundational framework for AI system security.
Why B is Correct: A secure Model Development Life Cycle (MDLC) integrates security practices at every stage of AI model development specifically tailored to ML workflows. It encompasses secure data handling, training data validation, model testing for adversarial robustness, secure deployment practices, and ongoing monitoring. Unlike generic software development lifecycles, the MDLC addresses ML-specific risks such as data poisoning, model drift, and adversarial attacks.
Why A is Wrong: Guardrail testing and security validation are important components of the MDLC but represent only the testing phase. They do not encompass the full lifecycle of security practices needed from data acquisition through production monitoring.
Why C is Wrong: Implementing comprehensive security architecture is a broad statement that describes an outcome rather than a specific actionable practice. It does not provide the structured, ML-specific guidance of an MDLC.
Why D is Wrong: A secure SDLC is designed for traditional software development and covers code security, testing, and deployment. While relevant to AI application development, it does not specifically address ML model-specific risks such as training data security, model integrity, and inference-time attacks.
NEW QUESTION # 139
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 # 140
A short AI-generated video shows a celebrity ' s likeness talking about a fake public security event.
Which of the following was used to create this video?
Answer: C
Explanation:
Basic Concept: Creating realistic deepfake videos that convincingly replicate a real person ' s facial expressions, movements, and voice requires deep learning models capable of learning and synthesizing complex spatial and temporal features from existing video data. CompTIA SecAI+ covers deepfake technologies under basic AI concepts.
Why B is Correct: Convolutional Neural Networks are foundational to deepfake video generation. CNNs excel at learning spatial features from visual data and are used within deepfake architectures to analyze source and target faces, extract facial features, and synthesize realistic face swaps or face animations. Modern deepfake systems typically combine CNNs with autoencoders and GANs to generate convincing video content showing a person saying or doing things they never did.
Why A is Wrong: Statistical analysis involves mathematical methods for analyzing data distributions and relationships. It does not have the capability to generate synthetic video content or replicate a person ' s visual likeness in motion.
Why C is Wrong: An ML classifier assigns input data to predefined categories. Classification models detect and label content rather than generating new synthetic video content of a person ' s likeness. They are detection tools, not generation tools.
Why D is Wrong: Random forest is an ensemble ML method using multiple decision trees for classification and regression tasks. It works on structured, tabular data and cannot process or generate visual, spatial data needed for realistic deepfake video synthesis.
NEW QUESTION # 141
Which of the following resources most accurately covers the most widely recognized threat vectors and common vulnerabilities related to large language model (LLM) applications?
Answer: A
Explanation:
OWASP is the best answer because it publishes the Top 10 for Large Language Model Applications, a resource specifically structured around significant security vulnerabilities and attack patterns affecting LLM- based applications. CompTIA SecAI+ Objective 2.1 directly lists the OWASP Top 10 and specifically the LLM Top 10 as an AI threat-modeling resource. MITRE ATLAS is also highly relevant to AI security, but its purpose is broader: it catalogs adversarial tactics, techniques, and procedures associated with attacks against AI and machine learning systems. The MIT AI Risk Repository aggregates a broad range of AI risks rather than functioning as the recognized LLM application vulnerability Top 10. NIST AI RMF provides a risk- management framework for governing and managing AI risks but is not primarily a vulnerability catalog for LLM applications. Since the question asks for the resource that most directly captures widely recognized threat vectors and common vulnerabilities associated specifically with LLM applications, OWASP ' s LLM- focused Top 10 is the most precise match.
NEW QUESTION # 142
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:
Rate limiting restricts the number of API requests within a specific timeframe, preventing brute- force attempts that can overwhelm the AI model and cause denial-of-service conditions.
NEW QUESTION # 143
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