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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-assisted Security | 24% | - Security automation and orchestration
|
| Topic 2: AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Topic 3: Securing AI Systems | 40% | - Secure AI development and operations
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
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NEW QUESTION # 152
Hotspot Question
Instructions
Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
If at any time you would like to bring back the initial state of the simulation please click the Reset All button.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.
Answer:
Explanation:
Explanation:
Cloud control plane: IAM policies, Resource policies
Prompt firewall: Injection policies, Output monitoring
WAF: Connection rate limits, Input token validation
Front-end API + Model + Vector database: Guardrails, Input quota
API gateway: Load balancing, Authentication token validation
Cloud control plane relies on IAM and resource policies to enforce centralized identity and access management as well as usage restrictions.
Prompt firewall protects against malicious input via injection policies and ensures outputs are monitored for compliance and safety.
WAF controls connection rate to mitigate DoS and validates input tokens to stop unauthorized access attempts.
Front-end API, Model, and Vector database share guardrails to restrict unsafe behaviors and input quotas to prevent overuse, making them consistent and efficient security points.
API gateway enforces load balancing for availability and validates authentication tokens at the entry point, providing secure and scalable access.
NEW QUESTION # 153
A penetration tester is assessing the controls of a deployed AI system that is designed to search and return the contents of files.
The tester runs the following:
Which of the following is the best control to prevent abuse of the system?
Answer: B
Explanation:
Basic Concept: AI systems that access file systems or databases use service accounts to authenticate.
Applying the principle of least privilege to these service accounts limits the damage that can result from prompt injection or other attacks that cause the AI to perform unauthorized file access. CompTIA SecAI+ Study Guide covers least privilege as a core AI security control.
Why D is Correct: Reducing the privilege scope of the service account implements the least privilege principle, ensuring the AI system can only access files it legitimately needs for its intended function. If an attacker uses prompt injection to abuse the file search capability, the service account ' s limited permissions prevent access to sensitive files outside the defined scope, containing the blast radius of any exploitation.
Why A is Wrong: Custom detection rules identify anomalous behavior after it occurs. They are detective controls, not preventive controls. They do not stop an attacker from successfully abusing the system; they only alert after abuse has occurred.
Why B is Wrong: VPC segmentation isolates the workload at the network level, limiting lateral movement.
However, it does not restrict what files the AI ' s service account can access within its own environment, so file abuse attacks within the segment are still possible.
Why C is Wrong: LLM guardrails filter prompt inputs and outputs for policy violations. While useful, they can potentially be bypassed through sophisticated prompt injection. Reducing service account privileges provides a defense-in-depth layer that limits damage even if guardrails are bypassed.
NEW QUESTION # 154
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: B
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 # 155
A security administrator needs to improve an AI model. During an initial investigation, the administrator notices that two successive login features are recorded every day, and then a successful login occurs after a specific time interval. All the successful login attempts have been during office hours.
Which of the following techniques should the administrator use to improve the AI model's security?
Answer: B
Explanation:
The administrator is analyzing repeated login behaviors and time-based patterns that precede successful access. Pattern recognition allows the AI model to detect these behavioral trends, improving its ability to identify anomalies or potential attacks while aligning with normal office-hour login behavior.
NEW QUESTION # 156
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 # 157
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