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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 2: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 3: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
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NEW QUESTION # 45
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: B
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 # 46
An AI security architect needs to conduct a security review of a new AI chatbot. Which of the following resources would the architect most likely rely on to perform a threat assessment and ensure comprehensive coverage during the review?
Answer: A
Explanation:
The OWASP Top 10 for Large Language Model Applications is the most directly applicable resource because the system under review is an AI chatbot based on LLM technology. CompTIA SecAI+ Objective 2.1 explicitly identifies the OWASP Top 10, including the LLM Top 10, as an AI threat-modeling resource. It addresses security risks specifically associated with LLM applications, including prompt injection, sensitive information disclosure, insecure output handling, excessive agency, and other AI-specific application threats.
ISO 27001 provides a broader information security management framework and is valuable for organizational security governance, but it is not specifically designed to enumerate LLM attack vectors. The EU AI Act primarily addresses regulatory obligations, risk classifications, and governance requirements rather than serving as a technical LLM threat-assessment checklist. Option A is also not the appropriate LLM-specific resource. For a security architect who needs systematic technical coverage of threats affecting an AI chatbot, the OWASP LLM Top 10 provides the most targeted framework among the listed options.
NEW QUESTION # 47
A healthcare organization plans to deploy a chatbot for appointment scheduling and patient records.
Which of the following is the first step a security administrator should take?
Answer: B
Explanation:
Basic Concept: Before implementing any security controls for an AI system, especially in a highly regulated sector such as healthcare, a risk assessment must first be conducted to understand the specific threats, vulnerabilities, regulatory obligations, and compliance requirements. CompTIA SecAI+ Study Guide emphasizes risk assessment as the foundational first step in any AI security program.
Why C is Correct: A risk assessment identifies what assets need protection, what threats exist, what regulations apply such as HIPAA for healthcare AI, and what the potential impact of various failure modes would be. In healthcare, this is especially critical given the sensitivity of patient records and strict regulatory requirements. The risk assessment results then inform and prioritize all subsequent security control implementations.
Why A is Wrong: Implementing prompt firewalls is a technical security control appropriate after risks have been identified and prioritized. Deploying controls before conducting a risk assessment may address the wrong threats or miss critical vulnerabilities.
Why B is Wrong: Role-based access management is a security control that should be designed based on identified roles and access requirements discovered during risk assessment. It is an implementation step, not the first step.
Why D is Wrong: Using a secure communication channel is a specific technical control for data in transit.
While important, it addresses only one specific risk and should be implemented as part of a comprehensive security strategy informed by a prior risk assessment.
NEW QUESTION # 48
A cybersecurity administrator must examine the cost of AI and implement controls so the research environment operates within a specified budget.
Which of the following controls is best for this situation?
Answer: D
Explanation:
Basic Concept: Operating AI systems within a budget requires direct control over the primary cost driver of LLM usage. For research environments where users may run extensive queries, token consumption management is the most effective budget control mechanism. CompTIA SecAI+ Study Guide covers token limits as the key cost management control for AI environments.
Why D is Correct: Token limits set hard caps on the maximum tokens consumed per request and per session, directly controlling the per-interaction cost of LLM API usage. In a research environment where users may submit complex, multi-part queries generating long responses, token limits prevent any single interaction from consuming disproportionate budget and enable the administrator to enforce aggregate budget constraints across all users and research activities.
Why A is Wrong: Prompt firewalls inspect and filter prompt content for security and policy compliance. They are security controls designed to prevent malicious or policy-violating prompts, not financial controls for managing token consumption or enforcing budget limits.
Why B is Wrong: API access controls manage authentication and authorization for API interactions, governing who can connect to the AI API. While restricting API access could limit who uses the system, it does not control how much budget individual authorized users consume through their research queries.
Why C is Wrong: Model guardrails enforce content policy and behavioral constraints on model inputs and outputs. They ensure safe and appropriate responses but do not limit the computational resources or tokens consumed by interactions, making them unsuitable as budget enforcement controls.
NEW QUESTION # 49
A user interface engineer adds new graphics to the latest release of an AI-integrated application. During the update, the engineer accidentally causes the model to retrain on unverified data. After the update, the model begins to return many errors.
Which of the following is the best way to mitigate future errors?
Answer: B
Explanation:
Basic Concept: When a non-ML engineer can accidentally trigger model retraining during a UI update, this indicates a lack of proper lifecycle management and change controls around the AI model. Uncontrolled retraining on unverified data is a critical vulnerability in the development and deployment process. CompTIA SecAI+ Study Guide identifies the Model Development Life Cycle as the framework for preventing such issues.
Why C is Correct: Implementing a Model Development Life Cycle (MDLC) establishes formal, controlled processes for every stage of model development and updates including data validation requirements before training, change management gates, testing and validation stages, and separation of duties between UI development and model training activities. An MDLC would have prevented the accidental retraining by requiring explicit, controlled authorization before any model training occurs.
Why A is Wrong: A WAF filters HTTP traffic at the application boundary. It does not govern internal development processes or control when and how model retraining occurs within the AI development pipeline.
Why B is Wrong: Role-based access control can restrict who has permission to trigger model retraining, which would help prevent this specific incident. However, it is one component of a broader MDLC governance framework and does not address data validation, testing stages, or the complete change management process.
Why D is Wrong: A GAN is a model architecture for generating synthetic data. It is a training technique unrelated to lifecycle governance or preventing accidental retraining from unverified data during unrelated application updates.
NEW QUESTION # 50
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