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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| Securing AI Systems | 40% | - Adversarial Defense
|
| AI-Assisted Security | 24% | - Operational Use of AI
|
| AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
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NEW QUESTION # 116
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 # 117
A security administrator wants to prevent prompt injection attacks and ensure responses have sanitized output.
Which of the following provides a primary compensating control for these requirements?
Answer: B
Explanation:
Basic Concept: Preventing prompt injection and ensuring output sanitization requires a control that can inspect both the semantic content of incoming prompts and the safety of outgoing responses. This requires an intelligent, context-aware filtering layer specifically designed for LLM traffic. CompTIA SecAI+ Study Guide identifies LLM firewalls as a primary control for prompt security and output safety.
Why C is Correct: An LLM firewall is specifically designed to inspect, filter, and sanitize both incoming prompts and outgoing AI responses. It can detect and block prompt injection attempts using pattern matching, semantic analysis, and behavioral heuristics, while also sanitizing output to remove sensitive data, harmful content, or policy violations before responses reach users. This dual capability makes it the primary control addressing both requirements simultaneously.
Why A is Wrong: Least privilege restricts what resources and actions users and systems can access. It reduces the potential impact of successful attacks but does not inspect prompt content for injection attempts or sanitize model outputs.
Why B is Wrong: Encryption protects data confidentiality in transit and at rest. It does not analyze prompt content for malicious patterns or filter AI-generated responses for unsafe content. Encrypted traffic can still carry prompt injection attacks.
Why D is Wrong: Rate limiting controls request frequency. While it can slow down automated injection attack campaigns, it does not inspect the content of individual prompts to detect injections, nor does it sanitize output responses. Malicious prompts can still succeed within rate limits.
NEW QUESTION # 118
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: D
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 # 119
An organization wants to reduce vulnerabilities after deployment. The organization decides to incorporate an AI-assisted early detection and vulnerability identification process in its development workflow.
Which of the following AI-assisted functions is the best option?
Answer: B
Explanation:
Basic Concept: Reducing post-deployment vulnerabilities requires catching security issues as early as possible in the development workflow. AI-assisted tools that analyze code during development provide the earliest possible intervention point. CompTIA SecAI+ Study Guide covers AI integration in secure development under AI-assisted security.
Why A is Correct: AI-assisted code linting analyzes source code in real time during development to identify security vulnerabilities, insecure coding patterns, policy violations, and quality issues before code is compiled or committed. By catching vulnerabilities at the coding stage - the earliest possible point in the development workflow - AI code linting prevents vulnerable code from progressing to testing, staging, or production, directly reducing post-deployment vulnerabilities at their source.
Why B is Wrong: Incident management handles security events and incidents after they have occurred in production. It is a reactive capability focused on response and recovery rather than early-stage vulnerability identification in the development workflow.
Why C is Wrong: Automated deployment/rollback automates the process of pushing code to production and reverting to previous versions when issues are detected post-deployment. It is a deployment safety mechanism rather than an early detection tool during the development phase.
Why D is Wrong: System auditing reviews and records system activities and configurations for compliance verification. It is primarily a detective and compliance control for systems that are already deployed, not an early development-phase vulnerability identification tool.
NEW QUESTION # 120
Which of the following improves the observability and auditing of an AI system?
Answer: C
Explanation:
MLOps provides structured monitoring, logging, and version control for AI models. This improves observability and ensures detailed auditing of model behavior, data pipelines, and changes throughout the AI lifecycle.
NEW QUESTION # 121
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