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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 2: Securing AI Systems | 40% | - AI System Protection
|
| Topic 3: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
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NEW QUESTION # 98
Which of the following risks most often occurs when developing an internal chatbot?
Answer: A
Explanation:
Data leakage is the most applicable risk because an internal chatbot commonly interacts with organizational information that may include confidential documents, customer information, employee records, credentials, intellectual property, and other sensitive content. CompTIA SecAI+ specifically identifies accidental data leakage as an AI risk and sensitive information disclosure as an attack or failure condition affecting AI systems. Internal chatbots can expose information when retrieval permissions are too broad, conversation context crosses security boundaries, prompts contain confidential material, or model responses reproduce information that the requesting user should not receive. Prompt injection is an important chatbot attack vector, but it describes deliberate manipulation of model instructions rather than the broader organizational risk emphasized here. Model theft concerns unauthorized acquisition or reproduction of a model. Unauthorized access concerns gaining access to resources without proper authorization and is generally addressed through model, data, agent, network, and API access controls. In contrast, information leakage can occur even when legitimate employees are using the chatbot normally. Protecting sensitive data through access controls, minimization, masking, classification, and monitoring is therefore fundamental to internal chatbot security.
NEW QUESTION # 99
A company launches an AI application to monitor cloud misconfiguration and compliance. The AI application is shutting down development servers and opening ports during a client demonstration. Which of the following actions should the company take to return to normal operations and prevent future issues?
Answer: C
NEW QUESTION # 100
A cyberthreat intelligence (CTI) analyst conducts research about a threat during a recent attack. The analyst uses an AI threat-modeling resource to find the relevant risks and utilizes causal and domain taxonomies.
Which of the following best describes which resource the analyst is using?
Answer: B
Explanation:
The MIT AI Risk Repository is specifically characterized by its use of both a Causal Taxonomy and a Domain Taxonomy. The repository consolidates AI risks from numerous frameworks and allows practitioners to classify and research risks according to how, when, and why they occur as well as the domain of harm involved. MIT states that its Causal Taxonomy addresses factors such as the responsible entity, intent, and timing, while its Domain Taxonomy categorizes risks into broader domains and subdomains. This terminology directly matches the scenario. CompTIA SecAI+ includes the MIT AI Risk Repository among the threat-modeling resources candidates must understand. MITRE ATLAS instead organizes adversarial AI activity into tactics, techniques, procedures, and case studies. The CVE AI Working Group focuses on vulnerabilities and related vulnerability-management concerns rather than causal/domain risk taxonomies.
OWASP ' s Machine Learning Security Top 10 concentrates on prominent ML security risks and defensive awareness. Because the question explicitly identifies causal and domain taxonomies, the MIT AI Risk Repository is the precise resource being described.
NEW QUESTION # 101
Which of the following technologies is used in deepfake?
Answer: D
Explanation:
Deepfakes are primarily created using GANs, where two neural networks (a generator and a discriminator) compete to produce highly realistic synthetic media, such as manipulated videos or images.
NEW QUESTION # 102
A security analyst is aware of an active penetration test in the environment. The analyst examines SIEM log data and notices the following AI system output:
Which of the following is the vulnerability that has occurred and the control the analyst should implement?
Answer: D
Explanation:
Basic Concept: AI systems can inadvertently reveal sensitive information such as PII, credentials, or internal data in their outputs when not properly controlled. Sensitive information disclosure is a critical OWASP LLM Top 10 risk. CompTIA SecAI+ Study Guide covers both vulnerability identification and appropriate data protection controls for AI outputs.
Why D is Correct: The scenario describes the AI system outputting sensitive information in its responses, which is a sensitive information disclosure vulnerability. The appropriate control is masking, which replaces sensitive data values such as credit card numbers, SSNs, or API keys with redacted or tokenized equivalents in the model ' s outputs before they are returned to users. This prevents the AI from disclosing sensitive data while still providing useful responses.
Why A is Wrong: Prompt injection involves crafting inputs to override model instructions. If the penetration test revealed sensitive information, the primary vulnerability is the disclosure of that sensitive data, not the injection mechanism itself. EDR monitors endpoint behavior, not AI output content.
Why B is Wrong: Model hallucinations produce fabricated information rather than disclosing real sensitive data. The described scenario involves actual sensitive information being revealed, not fictitious content generation.
Why C is Wrong: Jailbreaking circumvents safety restrictions but the primary harm demonstrated is sensitive data exposure. RBAC manages access permissions but does not prevent the model from including sensitive data in responses once access is granted.
Why E is Wrong: Role impersonation involves the AI pretending to be a different entity. This may be a secondary technique used by the penetration tester but the primary vulnerability described is the disclosure of actual sensitive information in the output.
NEW QUESTION # 103
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