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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| Topic 2: Securing AI Systems | 40% | - Security controls for AI systems
|
| Topic 3: AI-assisted Security | 24% | - Security automation and orchestration
|
| Topic 4: AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
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NEW QUESTION # 48
A detection engineering team wants to use AI to automatically prevent vulnerable code from reaching production. Which of the following is the most effective way to accomplish this task?
Answer: A
Explanation:
Integrating an LLM into the CI/CD pipeline ensures automated code review at build time, blocking vulnerable code from reaching production. This enforces security checks consistently and effectively during the deployment process.
NEW QUESTION # 49
A healthcare company deploys an AI chatbot that implements retrieval-augmented generation (RAG) using the company ' s historical data set. The chatbot output contains patient information.
Which of the following is the most effective technique to mitigate this vulnerability?
Answer: A
Explanation:
Basic Concept: When an AI chatbot powered by RAG retrieves and outputs sensitive patient information such as names, medical histories, or identifiers, the risk of Protected Health Information (PHI) disclosure must be mitigated. CompTIA SecAI+ Study Guide covers data protection techniques for AI systems handling sensitive health data.
Why A is Correct: Masking replaces sensitive data values such as patient names, dates of birth, medical record numbers, and diagnoses with redacted or anonymized equivalents in the chatbot ' s output. Even if the RAG system retrieves records containing patient information, masking ensures that the sensitive fields are obscured before the response is presented to the user. This directly prevents PHI disclosure while allowing the chatbot to provide useful responses based on the underlying data patterns.
Why B is Wrong: Classification involves categorizing data by its sensitivity level such as public, internal, confidential, or restricted. While it identifies which data requires protection, classification alone does not transform or obscure the sensitive values in chatbot outputs.
Why C is Wrong: Data minimization is a privacy principle that limits data collection to only what is necessary for the specified purpose. While valuable as a design principle when building the RAG knowledge base, it is a data governance strategy rather than a technical output control that can be applied to mitigate existing PHI disclosure in chatbot responses.
Why D is Wrong: Normalization is a data processing technique that scales numerical values to a standard range or standardizes data formats. It is a preprocessing step for improving model training efficiency, not a data protection technique for preventing patient information disclosure in AI outputs.
NEW QUESTION # 50
A legal firm permits employees to use a company-authorized chatbot for their research and reporting. In the past year, the firm had to retract some of its released documents because of incorrect references.
Which of the following best describes the weakness that caused this issue?
Answer: C
Explanation:
Overreliance occurs when users place excessive trust in AI-generated output without performing sufficient independent verification. The scenario indicates that employees used a chatbot for research and reporting and subsequently published documents containing incorrect references. That pattern strongly indicates that AI- generated citations or factual claims were accepted without adequate human validation. CompTIA SecAI+ explicitly lists overreliance among attacks and weaknesses that security professionals must recognize when analyzing AI systems. Generative AI systems can produce hallucinated or inaccurate citations that appear credible, which is why human oversight and human validation remain important lifecycle controls. Insecure output handling instead concerns unsafe downstream processing of model output, such as executing generated code or passing untrusted output into another system without validation. Excessive agency occurs when an AI system has more autonomous permissions or capabilities than necessary. Sensitive information disclosure involves exposing protected or confidential information. None of those conditions explains publication of believable but inaccurate references as directly as overreliance. Appropriate mitigation includes independent verification, authoritative-source validation, human review, and monitoring AI output for accuracy and hallucinations before publication.
NEW QUESTION # 51
Which of the following explains the reason a cybersecurity analyst prefers a machine learning (ML) model over a statistical model for attack classification?
Answer: D
Explanation:
Basic Concept: Cybersecurity threats evolve continuously, with new attack variants emerging regularly. The choice between traditional statistical models and ML models for attack classification depends on which better handles the complexity and dynamism of the threat landscape. CompTIA SecAI+ covers ML model advantages for cybersecurity under basic AI concepts.
Why A is Correct: ML models can learn arbitrarily complex, non-linear relationships from training data and adapt to new patterns when retrained with updated data. For attack classification, this means ML can recognize sophisticated, multi-feature attack patterns that exceed the capabilities of simple statistical models and can be updated to detect new attack variants as the threat landscape evolves. This adaptability to complex and changing problems is the primary reason analysts prefer ML over static statistical approaches.
Why B is Wrong: ML model development pipelines are generally more complex than statistical models, requiring data preparation, feature engineering, model selection, training, validation, and deployment steps.
Simplicity of development is not a characteristic advantage of ML over statistical models.
Why C is Wrong: ML models typically require large amounts of training data to perform well. Statistical models often perform better than ML with small datasets. Performance with small datasets is actually an advantage of statistical models over ML, not ML over statistical.
Why D is Wrong: Community support and expert availability are ecosystem considerations rather than technical reasons to prefer ML for cybersecurity classification tasks. These factors might influence tool selection but do not explain the fundamental technical preference for ML ' s superior handling of complex attack patterns.
NEW QUESTION # 52
An organization develops a chatbot with the following requirements:
- Does not provide harmful or explicit responses
- Must use clean and professional language
- Ensures that responses are accurate
Which of the following should the organization conduct after the chatbot is fully developed but before a customer-facing deployment?
Answer: A
Explanation:
Guardrail testing and validation ensure the chatbot adheres to safety, language, and accuracy requirements before deployment. This step verifies the model will not generate harmful, explicit, or unprofessional responses in a customer-facing environment.
NEW QUESTION # 53
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