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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - Adversarial Defense
|
| AI-Assisted Security | 24% | - Operational Use of AI
|
| AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
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NEW QUESTION # 85
Which of the following is the first step before using an online data set?
Answer: B
Explanation:
A third-party risk management assessment is the best first step because an externally obtained online dataset introduces risks concerning source trustworthiness, provenance, licensing, privacy, integrity, security, and regulatory compliance. CompTIA SecAI+ requires candidates to understand third-party compliance evaluations as part of AI governance and also emphasizes trustworthiness and authenticity during the AI lifecycle ' s data-collection stage. Before incorporating an externally sourced dataset into model development or training, the organization should determine who created or controls the data, whether its collection and distribution are legitimate, whether contractual or usage restrictions exist, and whether sensitive or malicious content could be present. A legal review may subsequently be required for licensing, copyright, privacy, and contractual issues, but third-party risk assessment provides the broader initial evaluation. A feasibility study addresses whether the proposed project is practical from a business perspective rather than whether the dataset is trustworthy. Model design review occurs later and focuses on architecture and implementation.
Consequently, third-party risk management provides the most comprehensive initial gate before introducing an online dataset into the organization ' s AI lifecycle.
NEW QUESTION # 86
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: B
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 # 87
A line of business wants to onboard an application that uses a custom AI model for employee assessments.
The Chief Information Officer (CIO) agrees to allow the engagement to proceed but first wants a threat model.
Which of the following is the most appropriate to use for an AI threat model?
Answer: A
Explanation:
Basic Concept: Threat modeling for AI systems requires a framework specifically designed to address AI- specific attack techniques, tactics, and procedures. General cybersecurity or governance frameworks do not capture the unique adversarial attack surface of AI and ML systems. CompTIA SecAI+ Exam Objectives identify MITRE ATLAS as the primary AI threat modeling resource.
Why B is Correct: MITRE ATLAS (Adversarial Threat Landscape for AI Systems) is specifically designed as an AI and ML threat modeling framework. It catalogs real-world adversarial tactics, techniques, and procedures targeting AI systems, enabling security architects to identify and assess threats unique to ML models such as data poisoning, model extraction, and evasion attacks. It is the industry standard for AI- specific threat modeling.
Why A is Wrong: Responsible AI is a set of ethical principles and governance guidelines for developing and deploying AI systems fairly and safely. It addresses ethics and fairness, not technical adversarial threat modeling.
Why C is Wrong: The OECD provides non-binding policy recommendations and principles for AI governance at an international level. It does not provide technical threat modeling taxonomies or AI-specific attack catalogs.
Why D is Wrong: ISO standards such as ISO 42001 establish management system requirements for AI governance. They are compliance and management frameworks, not threat modeling tools for identifying adversarial AI attack vectors.
NEW QUESTION # 88
Which of the following job roles in an organizational governance structure develops a model from business use cases?
Answer: C
Explanation:
Basic Concept: In AI governance, each role holds distinct responsibilities. Understanding these roles is core to CompTIA SecAI+ Domain 4 (AI Governance, Risk, and Compliance).
Why D is Correct: The Data Scientist is responsible for translating business use cases into working AI/ML models. They analyze business requirements, identify the appropriate machine learning approach, and develop models that fulfill specific business objectives. According to the CompTIA SecAI+ Study Guide, data scientists bridge raw data and actionable AI solutions by building and validating models derived from business-driven needs.
Why A is Wrong: A Platform Architect designs and manages the infrastructure and technical platforms hosting AI systems. Their focus is architectural design of the environment, not model development from business use cases.
Why B is Wrong: An AI Risk Analyst identifies, evaluates, and mitigates risks associated with AI adoption.
Their role is governance and risk-oriented, not model creation.
Why C is Wrong: An MLOps Engineer operationalizes, deploys, monitors, and maintains AI models in production. They take models already built by data scientists and ensure reliable operation at scale, not develop them from business use cases.
NEW QUESTION # 89
An airline corporation wants to implement a chatbot application using a large language model (LLM) so its customers can ask questions and receive answers about flight details and have the option to upload files.
Which of the following security controls should the airline use to protect against malicious input and unauthorized use beyond the service-level agreement? (Choose two.)
Answer: C,D
Explanation:
Basic Concept: LLM-based chatbots accepting user-uploaded files face two critical risk categories: malicious input injection and resource or cost abuse. CompTIA SecAI+ Study Guide highlights prompt security controls and resource management as key defensive layers for public-facing LLM applications.
Why A is Correct: Prompt guardrails intercept and filter user inputs and model outputs, blocking malicious prompts, prompt injection attempts, and harmful file content before affecting model behavior. Since users can upload files, guardrails are essential for sanitizing and validating that content before processing.
Why D is Correct: Model token quotas directly limit how much of the LLM ' s processing capacity a user can consume. This prevents abuse beyond the SLA, including denial-of-wallet attacks or resource exhaustion through excessively large inputs or repeated requests.
Why B is Wrong: Role-based access controls manage who can access what resources. While useful for internal systems, they do not address malicious input content or enforce LLM resource consumption limits for a public-facing chatbot.
Why C is Wrong: Firewall rules operate at the network layer and can block unauthorized IPs or ports but cannot inspect or filter the semantic content of prompts or control token-level LLM usage.
NEW QUESTION # 90
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