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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-assisted Security | 24% | - AI in security strategy and operations
|
| Topic 2: AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - Core AI principles and terminology
|
| Topic 4: Securing AI Systems | 40% | - Defending against AI-specific attacks
|
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NEW QUESTION # 41
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?
Answer: C
Explanation:
During DAST, automating the generation of diverse, targeted attack payloads lets testers probe runtime inputs (e.g., XSS, SQLi, command injection) more thoroughly and discover vulnerabilities that manual or static tests might miss.
NEW QUESTION # 42
An IT company implements an adaptable chatbot that learns from user prompts. Based on the conversation shown - where User 2 injected false information about a company acquisition that caused the chatbot to give incorrect responses to User 3 - which of the following compensating controls should an administrator implement to mitigate the issue?
Answer: D
NEW QUESTION # 43
A disgruntled employee changed the company policies that a chatbot references in order to create confusion and disrupt the business.
Which of the following AI-generated vulnerabilities is the employee exploiting?
Answer: B
Explanation:
Basic Concept: AI systems that rely on knowledge bases, vector databases, or reference documents are vulnerable to attacks that corrupt or manipulate that source data. When an adversary deliberately modifies the data an AI uses, this is a form of data poisoning. CompTIA SecAI+ Study Guide covers data poisoning as a core AI vulnerability.
Why C is Correct: Data poisoning is an attack where an adversary intentionally corrupts or manipulates the data that an AI system uses for training, inference, or reference. In this scenario, the employee modified the company policies document that the chatbot uses as its knowledge base, causing the chatbot to provide incorrect, misleading, or confusing information to users. This is a classic indirect data poisoning attack targeting the AI ' s reference data rather than its model weights.
Why A is Wrong: Data reduction refers to techniques that decrease the volume or dimensionality of data for processing efficiency. It is a data engineering concept, not an attack vector or vulnerability classification.
Why B is Wrong: Data masking replaces sensitive data values with anonymized equivalents to protect privacy. It is a data protection control used legitimately, not an attack that an employee would exploit to cause disruption.
Why D is Wrong: Data leaking involves unauthorized disclosure of sensitive information from an AI system or its associated data stores. The employee ' s action of manipulating data is an integrity attack, not a confidentiality violation involving leakage of data to unauthorized parties.
NEW QUESTION # 44
Which of the following is the most impactful security risk associated with the use of a generative AI chatbot?
Answer: B
Explanation:
Basic Concept: Generative AI chatbots interact with users in natural language and may access organizational knowledge bases, databases, or prior conversations. The conversational nature of these systems creates unique risks around sensitive information disclosure. CompTIA SecAI+ Study Guide ranks data leakage as the primary security concern for generative AI chatbots.
Why B is Correct: Data leakage occurs when a generative AI chatbot inadvertently reveals sensitive information including PII, confidential business data, intellectual property, training data, or system configurations in its responses. This can happen through prompt injection attacks, insufficient output filtering, or the model memorizing and reproducing sensitive training data. The impact is immediate, potentially irreversible, and can result in regulatory violations, competitive disadvantage, and reputational damage.
Why A is Wrong: Overly permissive access is a contributing factor that can exacerbate data leakage but is an access control design issue rather than the most directly impactful runtime risk of operating a generative AI chatbot.
Why C is Wrong: Weak encryption is a data protection concern for data in transit or at rest. While important, it is a configuration issue separate from the generative AI chatbot ' s core operational risks and is not specific to chatbot technology.
Why D is Wrong: Model validation ensures a model performs as expected before deployment. While important for quality assurance, it is a development lifecycle activity rather than an ongoing operational security risk associated with running a chatbot.
NEW QUESTION # 45
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: C
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 # 46
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