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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-assisted Security | 24% | - AI for threat detection and response
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| Topic 3: AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Topic 4: Securing AI Systems | 40% | - Security controls for AI systems
|
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NEW QUESTION # 123
A financial organization implements a new AI-based fraud detection system to flag suspicious transactions. A security analyst discovers that it occasionally blocks legitimate transactions.
Which of the following is the best recommendation?
Answer: A
Explanation:
Basic Concept: When an AI fraud detection model produces false positives (blocking legitimate transactions), this indicates the model ' s decision boundary is insufficiently calibrated. The model needs improved training data to better distinguish fraudulent from legitimate transactions. CompTIA SecAI+ covers model performance improvement under AI-assisted security.
Why A is Correct: Retraining the model with more data and recent transaction patterns directly addresses the root cause of false positives. Additional representative legitimate transaction data helps the model learn more accurate decision boundaries, reducing false positives while maintaining detection sensitivity for actual fraud.
This improves model accuracy without abandoning AI-based detection.
Why B is Wrong: Token usage and rate limits are cost and resource management controls for LLM APIs.
They have no relevance to improving the accuracy of a fraud detection ML model that incorrectly classifies legitimate transactions.
Why C is Wrong: Encrypting data and applying access controls are data security measures that protect confidentiality and integrity. They do not address model classification accuracy or the false positive problem in fraud detection.
Why D is Wrong: Rolling back to a traditional system abandons the capabilities of AI-based fraud detection.
The appropriate response to model performance issues is to improve the model through retraining, not to regress to less capable detection approaches.
NEW QUESTION # 124
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: C
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 # 125
A cybersecurity administrator generates patching reports using AI, but the process takes a long time. Which of the following is the best way to increase performance?
Answer: A
Explanation:
Basic Concept: AI systems that repeatedly query external data sources for similar information during a single report generation process spend significant time on redundant network requests. Caching frequently accessed data locally eliminates this overhead. CompTIA SecAI+ Study Guide covers AI performance optimization strategies in security operations contexts.
Why B is Correct: Downloading the full CVE database locally before starting the cross-referencing process eliminates the need for multiple individual external API calls as the AI processes each OS version ' s patch list. Instead of making thousands of small external queries to look up CVE information for each patch-OS combination, the AI can query the locally cached database internally. This transforms multiple slow external network operations into fast local lookups, dramatically reducing report generation time.
Why A is Wrong: Using an MCP server to run multiple LLM queries simultaneously could improve throughput through parallelization. However, the fundamental bottleneck is external CVE database queries, not LLM processing capacity. Parallelizing LLM calls does not eliminate the external query latency.
Why C is Wrong: Specifying summarization algorithms in the system prompt affects how the AI structures its output. It does not address the time-consuming external data retrieval process that is the actual performance bottleneck in this cross-referencing workflow.
Why D is Wrong: Increasing token limits prevents session restarts for long contexts but does not address the external query latency that makes the report slow to generate. The bottleneck is data retrieval speed, not token limit constraints causing session breaks.
NEW QUESTION # 126
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: C
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 # 127
Which of the following attacks is most enabled by AI-generated content?
Answer: D
Explanation:
Basic Concept: AI-generated content including personalized text, synthetic voice, and deepfake video has dramatically enhanced the effectiveness and scalability of social engineering attacks. Understanding how AI amplifies specific attack types is key to CompTIA SecAI+ basic AI concepts in the cybersecurity context.
Why B is Correct: Phishing attacks are most dramatically enabled by AI-generated content. AI can generate highly personalized, grammatically perfect phishing emails tailored to individual targets using publicly available information. It can create convincing deepfake audio and video for voice phishing (vishing) and video phishing, replicate executive communication styles for business email compromise, and generate phishing campaigns at massive scale. The quality and personalization that previously required skilled human social engineers can now be automated with AI.
Why A is Wrong: Model poisoning is a specific attack against AI systems that corrupts training data to manipulate model behavior. While sophisticated, it is a targeted AI security attack rather than a broad cybercrime enabled by AI-generated content at scale.
Why C is Wrong: Ransomware is malware that encrypts victim data and demands payment for decryption keys. While AI can assist in ransomware development, ransomware deployment relies on code execution and network propagation techniques more than AI-generated content.
Why D is Wrong: Remote code execution involves exploiting vulnerabilities to run arbitrary code on a target system. It relies on technical vulnerability exploitation rather than AI-generated content. AI might assist in finding vulnerabilities, but RCE is not primarily enabled by content generation.
NEW QUESTION # 128
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