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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - Core AI principles and terminology
|
| Securing AI Systems | 40% | - Secure AI development and operations
|
| AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
| AI-assisted Security | 24% | - Security automation and orchestration
|
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NEW QUESTION # 86
Before submitting code to the upstream code repository, a security administrator wants to implement the following:
* Comments describing the inputs, outputs, and purpose of code blocks
* Recommendations for code security
Which of the following should the administrator implement to address both requirements?
Answer: D
Explanation:
An AI-enabled IDE plug-in is the best choice because it operates directly in the developer ' s coding environment before code is submitted to the upstream repository. It can analyze source code while it is being created, generate or improve comments describing functions, inputs, outputs, and code-block purpose, and simultaneously provide security recommendations for potentially vulnerable or unsafe code. This directly addresses both requirements at the appropriate stage of the development workflow. CompTIA SecAI+ explicitly includes IDE plug-ins among AI-enabled tools and identifies code quality, linting, and vulnerability analysis as AI-assisted security use cases. A machine-learning code checker may identify defects but does not necessarily generate useful documentation. A CLI-based add-on could perform some analysis, but it is less naturally integrated into the developer ' s authoring workflow. A repository runner generally operates after code has reached a repository or pipeline. The IDE plug-in therefore provides the most immediate combination of documentation assistance and secure-coding guidance before submission.
NEW QUESTION # 87
An internal user enters a client credit card number into an internal generative machine learning (ML) model:
#User prompt: Customer Jane Doe has a new credit card that she wants to add to her account. The number is 5555-5555-5555-5555 Which of the following is the most effective way to prevent prompt injection attacks against a large language model (LLM)?
Answer: D
Explanation:
Guardrails are the primary security control for LLMs to prevent prompt injection attacks. They enforce rules on what inputs are accepted and how the model responds, blocking malicious or sensitive prompts (such as credit card numbers) before they can manipulate or exploit the model.
NEW QUESTION # 88
A security analyst receives an alert about an AI system and is investigating the following output:
Which of the following is the most appropriate control the analyst should recommend?
Answer: B
Explanation:
Basic Concept: Suspicious or unexpected AI system outputs are often caused by malicious or malformed user inputs that exploit the AI ' s input processing. Validating and sanitizing user inputs before they reach the AI model prevents many classes of attacks including prompt injection, data exfiltration attempts, and input manipulation. CompTIA SecAI+ Study Guide emphasizes input validation as a foundational AI security control.
Why B is Correct: Implementing user input validation applies checks and constraints to all user-submitted content before it is processed by the AI system. Input validation can enforce length limits, detect injection patterns, filter disallowed characters or command structures, and ensure inputs conform to expected formats.
By rejecting malicious or malformed inputs at the entry point, this control prevents them from reaching the model and causing the suspicious outputs observed.
Why A is Wrong: Data sanitization processes data to remove harmful elements and is closely related to validation. However, input validation is broader and more proactive, checking conformance to rules before processing, while sanitization typically operates during or after processing. Validation at the input boundary is the more appropriate first-line control.
Why C is Wrong: Monitoring logs for attack keywords is a detective control that identifies attacks after they have already affected the system. It does not prevent suspicious outputs from being generated in the first place.
Why D is Wrong: Hardening the Model Context Protocol server improves the security of the infrastructure hosting the AI components. While important for infrastructure security, it does not directly validate or inspect the content of user inputs that cause suspicious outputs.
NEW QUESTION # 89
Which of the following responsible AI standards refers to a principle that clearly states the reasons behind the decisions for a particular conclusion?
Answer: D
Explanation:
Explainability is the responsible AI principle that ensures AI systems can provide clear reasoning for their decisions, allowing users to understand how and why a particular conclusion was reached.
NEW QUESTION # 90
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 # 91
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