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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-assisted Security | 24% | - AI in security strategy and operations
|
| Topic 2: Securing AI Systems | 40% | - Defending against AI-specific attacks
|
| Topic 3: AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
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NEW QUESTION # 24
A line of business wants to onboard an application that uses a custom AI model for employee assessments. The Chief Information Officer (CIO) agrees to allow the engagement to proceed but first wants a threat model. Which of the following is the most appropriate to use for an AI threat model?
Answer: A
Explanation:
ATLAS is specifically designed for creating AI threat models. It catalogs adversarial techniques, tactics, and use cases that target AI systems, making it the most appropriate framework for assessing risks to a custom AI model in employee assessments.
NEW QUESTION # 25
A customer-facing, AI-powered chatbot has been jailbroken through prompt injections. As a result, the AI model is offering a 99% discount on the purchase of a new vehicle.
Which of the following should be implemented to enhance the model ' s robustness against such attacks?
Answer: C
Explanation:
Basic Concept: Jailbreaking through prompt injection exploits the LLM ' s tendency to follow instructions embedded in user input, overriding its intended behavior. The model was manipulated to offer unauthorized discounts, demonstrating that its operational boundaries were not properly enforced. CompTIA SecAI+ Study Guide identifies guardrails as the primary defense against jailbreaking attacks.
Why D is Correct: Guardrails are robust, layered controls that enforce behavioral boundaries on LLM inputs and outputs. They can detect and block jailbreaking attempts, enforce business logic constraints such as preventing unauthorized discounts, validate outputs against policy rules before delivery, and prevent the model from operating outside its defined scope. Guardrails are specifically designed to make models more robust against prompt injection and jailbreaking.
Why A is Wrong: Bias filtering is designed to detect and remove biased, discriminatory, or offensive content from model outputs. It addresses content fairness issues but does not prevent jailbreaking attacks that manipulate the model into performing unauthorized actions.
Why B is Wrong: A system prompt sets the model ' s base instructions and persona, but the jailbreak attack already demonstrates that the current prompt can be overridden. Guardrails provide enforcement at a layer that is more resistant to prompt manipulation than the system prompt alone.
Why C is Wrong: Log monitoring detects jailbreaking attempts after they have already succeeded. It is a detective control that enables incident response but does not prevent the model from offering unauthorized discounts in the first place.
NEW QUESTION # 26
A security team is using an AI-based tool to try to bypass organizational boundaries. The team uses AI to look at the current state and suggest different attack vectors based on the outcome of the previous ones.
Which of the following techniques is the team most likely using?
Answer: B
Explanation:
Basic Concept: Modern penetration testing increasingly leverages AI to automate the reconnaissance, exploitation, and pivoting process. AI-assisted automated penetration testing can adapt its strategy based on previous results, simulating intelligent adversary behavior more realistically than static scripts. CompTIA SecAI+ covers AI-assisted offensive security techniques.
Why D is Correct: Automated penetration testing uses AI to systematically discover and attempt to exploit vulnerabilities while adapting tactics based on the results of previous attempts. The described behavior - looking at the current state, suggesting attack vectors, and adjusting based on outcomes - precisely describes an adaptive AI-driven penetration testing tool that iteratively explores the attack surface, mimicking how an advanced persistent threat would operate.
Why A is Wrong: Manual signature matching compares network traffic or files against a database of known threat signatures. It is a passive detection technique used by defensive tools like IDS/IPS, not an adaptive offensive technique for bypassing organizational boundaries.
Why B is Wrong: Code quality testing analyzes source code for bugs, vulnerabilities, and adherence to coding standards. It is a development quality assurance activity, not an offensive security technique for testing organizational security boundaries.
Why C is Wrong: Fraud detection uses ML to identify suspicious patterns in transactions or user behavior for defensive purposes. It is a preventive security measure, not an offensive technique for penetration testing.
NEW QUESTION # 27
An organization recently created a custom model that integrates with a language model (LLM). The developer notices that the application programming interface (API) costs have increased.
Which of the following is the best control to reduce cost?
Answer: C
Explanation:
Basic Concept: LLM API pricing is primarily based on token consumption - the number of tokens processed in both input prompts and output responses. Controlling token usage is the most direct lever for managing and reducing LLM API costs. CompTIA SecAI+ Study Guide covers AI cost management and resource controls under securing AI systems.
Why D is Correct: Adjusting token limits directly caps the maximum number of tokens used per request for both input and output. By setting appropriate token limits, the organization prevents excessively long prompts or verbose responses from consuming unnecessary tokens, directly translating to lower API costs and providing hard budget control.
Why A is Wrong: Prompt templates standardize how queries are structured, which can indirectly improve efficiency. However, they do not enforce a hard cap on token usage and cannot prevent costs from escalating with large volumes or verbose responses.
Why B is Wrong: Increasing CPU and memory addresses computational infrastructure performance on the client side. LLM API costs are billed by the API provider based on token usage, not on the client ' s hardware resources.
Why C is Wrong: Reducing model size means using a smaller, less powerful model version. While this may lower cost per token, it is a model selection decision, not an ongoing operational control that can be adjusted to manage cost in real time.
NEW QUESTION # 28
A social media company with more than a million lines of code wants to reduce the mean time to fix bugs and issues. Which of the following is the most balanced AI strategy to automate the vulnerability management flow?
Answer: A
Explanation:
This approach balances automation and human oversight. AI accelerates vulnerability management by triaging issues and generating tickets, while software engineers retain responsibility for merging code changes, ensuring quality and reducing the risk of insecure or unstable code being deployed.
NEW QUESTION # 29
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