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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing AI Systems | 40% | - AI System Protection
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| Topic 3: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Topic 4: AI-Assisted Security | 24% | - Operational Use of AI
|
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NEW QUESTION # 95
A security analyst finds that the AI system is under a denial-of-wallet attack.
Which of the following should the analyst enforce to protect the company? (Choose two.)
Answer: B,C
Explanation:
Basic Concept: A denial-of-wallet (DoW) attack deliberately generates excessive API calls or token consumption to exhaust an organization ' s AI budget. Since LLM providers charge based on tokens processed, attackers can cause significant financial damage by driving massive usage. CompTIA SecAI+ Study Guide addresses financial abuse vectors in AI systems.
Why E is Correct: API rate controls limit the number of requests a user or application can make within a defined time period. By capping request frequency, rate controls directly prevent attackers from generating the massive API call volume needed to execute a denial-of-wallet attack.
Why F is Correct: Output token controls cap the maximum number of tokens the model can generate per response. Since billing is based on tokens consumed including outputs, limiting output tokens directly caps the cost per request, preventing attackers from triggering extremely long, expensive responses.
Why A is Wrong: Endpoint access controls manage device or network access. They do not directly limit token consumption or API call volume that drives denial-of-wallet costs.
Why B is Wrong: A CDN distributes content geographically to improve performance and absorb traffic. It does not control LLM API billing or token consumption.
Why C is Wrong: Model fine-tuning adjusts model parameters for improved performance on specific tasks. It is a training process that does not address active cost-exhaustion attacks.
Why D is Wrong: Modality controls restrict which input types such as text, images, or audio a model accepts.
While useful for reducing attack surface, they do not directly address the rate or volume of API calls in a DoW attack.
NEW QUESTION # 96
A team of data scientists is ready to release a model for enterprise use. The team wants to protect the model from unintentional changes or tampering. Which of the following is the most appropriate action?
Answer: B
Explanation:
Protecting the model from tampering requires controlled and auditable access. By integrating the model with an API secured by IAM roles, only authenticated and authorized users or systems can interact with it, ensuring integrity and preventing unauthorized changes.
NEW QUESTION # 97
Which of the following is used to train an AI model with unstructured data?
Answer: D
Explanation:
Basic Concept: Unstructured data such as free-form text, images, and audio does not have predefined labels or rigid schema. Training an AI model effectively on unstructured data requires techniques that can leverage patterns within the data itself or adapt a pre-trained model to new data types. CompTIA SecAI+ covers AI training methodologies under basic AI concepts.
Why B is Correct: Fine-tuning takes a pre-trained foundation model that has already learned rich representations from massive unstructured datasets and further trains it on a specific, potentially smaller unstructured dataset. This adapts the model to a new domain, task, or data type without requiring labeled data for every training example. Fine-tuning is the most practical and effective approach for working with unstructured data in modern AI development.
Why A is Wrong: Statistical learning typically refers to classical machine learning approaches that often assume structured, numerical data with defined features. These methods generally struggle with high- dimensional unstructured data without significant preprocessing.
Why C is Wrong: Supervised learning requires labeled training data where each example has an associated correct output label. Applying supervised learning to unstructured data requires extensive manual labeling, which is the opposite of working with raw unstructured data.
Why D is Wrong: Reinforcement learning trains models through reward signals based on actions taken in an environment. It is designed for sequential decision-making tasks and is not the standard approach for learning representations from unstructured data at scale.
NEW QUESTION # 98
Developers introduce new features to their generative AI product in an effort to stand out from the competition and offer more value to customers.
Which of the following most accurately explains the risks when enabling more functionality?
Answer: A
Explanation:
Basic Concept: The relationship between AI system capabilities and security risk is a fundamental concept in AI governance. As AI models gain more functionality and capabilities, their potential for misuse, unintended consequences, and attack surface expansion grows proportionally. CompTIA SecAI+ Study Guide addresses capability-risk proportionality under AI governance.
Why D is Correct: The risks of a generative AI product are proportional to its capabilities. Each new feature expands what the model can do, which simultaneously expands what adversaries can manipulate it to do, what sensitive operations it can be directed to perform, and what unintended harm it can cause. A model that can generate text, images, execute code, and call external APIs has dramatically greater risk potential than one that can only generate text. Risk grows with capability scope.
Why A is Wrong: Risks do not remain constant when new features are added. New features introduce new attack vectors, expand the model ' s action space, and create new opportunities for misuse. Each addition fundamentally changes the system ' s risk profile.
Why B is Wrong: While risks do increase with new features, saying they simply increase does not capture the precise relationship. The increase is proportional to the nature and scope of the capabilities added, not a uniform increment for any feature addition.
Why C is Wrong: While risks can be measured qualitatively, stating that risks are measured qualitatively is a statement about measurement methodology rather than an explanation of how risks change when functionality is enabled. It does not accurately describe the relationship between capability and risk.
NEW QUESTION # 99
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
Answer: D
Explanation:
RAG augments an LLM with external, up-to-date information retrieval, improving its factual accuracy and contextual scope-capabilities that directly strengthen the model's effectiveness for reconnaissance.
NEW QUESTION # 100
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