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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| Topic 2: AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Topic 3: AI-assisted Security | 24% | - AI in security strategy and operations
|
| Topic 4: Securing AI Systems | 40% | - Security controls for AI systems
|
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NEW QUESTION # 85
A security alert triggers an agentic system. An analyst notices the following payload in the logs"
The alert includes multiple shell commands that are not typically run as part of any hardening.
Which of the following is the most effective control to implement?
Answer: C
Explanation:
The payload in the alert attempts to trick the system into executing unauthorized shell commands.
The most effective control is to implement allow-list validation (approved strings) before execution. This ensures that only predefined, safe commands are executed, blocking prompt injection attempts that introduce malicious code such as the fake patch script.
NEW QUESTION # 86
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: A,D
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 # 87
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?
Answer: B
Explanation:
During DAST, automating the generation of diverse, targeted attack payloads lets testers probe runtime inputs (e.g., XSS, SQLi, command injection) more thoroughly and discover vulnerabilities that manual or static tests might miss.
NEW QUESTION # 88
During an update, an AI system flags some potential compatibility issues and provides recommendations. An administrator reviews the recommendations before addressing the issues.
Which of the following processes describes this scenario?
Answer: B
Explanation:
Basic Concept: Human-in-the-loop is a design pattern where AI systems generate recommendations or decisions but require human review and approval before those recommendations are acted upon. This approach maintains human oversight and accountability in AI-assisted workflows. CompTIA SecAI+ Study Guide covers human-in-the-loop as a key responsible AI principle and operational pattern.
Why C is Correct: The scenario precisely describes the human-in-the-loop pattern: the AI system identifies potential issues and provides recommendations, but an administrator must review those recommendations before any action is taken. This deliberate inclusion of human judgment in the AI ' s decision or recommendation workflow ensures human oversight is maintained, which is the defining characteristic of the human-in-the-loop process.
Why A is Wrong: Data validation verifies that data meets expected quality standards and formats before being used in AI processing. It is a data quality control activity, not a workflow pattern describing human review of AI recommendations.
Why B is Wrong: Data preparation involves transforming raw data into a format suitable for AI model training or inference. It encompasses cleaning, normalizing, and formatting data, not the process of human review of AI-generated recommendations during system updates.
Why D is Wrong: Model evaluation assesses a model ' s performance against metrics such as accuracy, precision, and recall on test datasets. It is a technical assessment of model quality, not a workflow process where humans review AI-generated recommendations before acting on them.
NEW QUESTION # 89
An organization recently developed an AI-powered product and discovers that it is vulnerable to attacks in which malicious actors can alter the input, causing the system to recommend inappropriate information.
Which of the following techniques is the most effective way to secure the system against manipulation attacks?
Answer: D
Explanation:
Basic Concept: Input manipulation attacks - including adversarial examples and prompt injection - alter inputs to cause AI systems to produce unintended, harmful, or inappropriate outputs. Defending against these attacks requires mechanisms that validate and constrain both inputs and outputs at runtime. CompTIA SecAI+ Study Guide identifies guardrails as the primary defense against input manipulation in AI systems.
Why D is Correct: Guardrails implement real-time validation and filtering of both incoming inputs and outgoing recommendations. They detect manipulated inputs that deviate from expected patterns, enforce content policies on outputs, and prevent the system from producing inappropriate recommendations regardless of how cleverly the input was crafted. Guardrails provide the most comprehensive and directly applicable defense against the described manipulation attack scenario.
Why A is Wrong: Cross-validation is a model evaluation technique that assesses how well a model generalizes to independent datasets during training. It measures predictive performance but does not provide runtime protection against input manipulation attacks on a deployed system.
Why B is Wrong: Feature regularization is a training technique that adds penalties to model weights to prevent overfitting. It improves generalization during training but does not inspect or validate inputs at inference time to detect manipulation.
Why C is Wrong: Feature scaling normalizes input feature values to a standard range for training efficiency.
Like regularization, it is a preprocessing and training step that has no effect on defending against runtime input manipulation attacks on deployed systems.
NEW QUESTION # 90
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