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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - Core AI principles and terminology
|
| AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| AI-assisted Security | 24% | - Security automation and orchestration
|
| Securing AI Systems | 40% | - Defending against AI-specific attacks
|
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NEW QUESTION # 45
An organization is concerned with the exposure of sensitive data.
Which of the following is the most relevant security concern?
Answer: A
Explanation:
Basic Concept: AI models can inadvertently memorize sensitive information from their training data. Certain attack techniques can exploit this memorization to extract private information from a deployed model, even without direct access to the training dataset. CompTIA SecAI+ Study Guide covers model inversion as an AI- specific data exposure attack vector.
Why B is Correct: Model inversion is an attack where an adversary queries a deployed AI model with carefully crafted inputs to reconstruct or infer sensitive training data. For example, an attacker could query a facial recognition model with optimized images to reconstruct faces of individuals from the training set, or query a medical diagnosis model to infer patient records used in training. This directly exposes sensitive data that was supposed to be protected.
Why A is Wrong: Overfitting is a model training quality issue where a model learns training data too specifically and performs poorly on new data. While it can indicate that sensitive data was memorized, overfitting itself is a performance concern rather than directly a data exposure attack vector.
Why C is Wrong: Data normalization is a preprocessing technique that scales numerical features to a common range to improve training performance. It is a data preparation step with no direct relevance to sensitive data exposure or privacy attacks.
Why D is Wrong: Hyperparameter tuning adjusts configuration parameters of a model to optimize its performance during training. It is an optimization technique with no relevance to protecting against sensitive data exposure attacks.
NEW QUESTION # 46
Instructions: Click the (+) to assign each threat category into its appropriate framework.
An architect is modeling an agentic system to meet security standards.
Answer:
Explanation:
See Explanation below for complete solution for this PBQ.
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a simulation item requiring interactive drag- and-drop assignment of threat categories to appropriate frameworks in the actual exam. It tests knowledge of how different AI threat frameworks categorize and address specific threat types for agentic systems.
Key Concept - Framework-to-Threat Mapping: MITRE ATLAS covers ML-specific adversarial tactics such as model evasion, data poisoning, model extraction, and prompt injection for agentic systems. OWASP LLM Top 10 addresses application-level LLM vulnerabilities such as insecure output handling, excessive agency, and supply chain risks. NIST AI RMF addresses governance-level risks across the AI lifecycle. STRIDE addresses architectural threats including spoofing, tampering, repudiation, information disclosure, DoS, and elevation of privilege.
Why This Matters: Agentic AI systems have a unique threat landscape combining traditional software vulnerabilities with AI-specific attacks. Correctly mapping threat categories to frameworks is essential for comprehensive threat modeling of systems that autonomously execute multi-step tasks with tool access and real-world consequences.
Reference: CompTIA SecAI+ Study Guide Domain 4 covers AI governance frameworks and their specific threat categories. Candidates should understand the scope and focus areas of MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF, and traditional security frameworks as they apply to agentic AI system security modeling.
NEW QUESTION # 47
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: D
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 # 48
Which of the following is the best example of an AI model that is trained to identify multiple points from input using a neural network to provide output for authentication?
Answer: C
Explanation:
Facial recognition uses neural networks to analyze multiple points or features from an input image (such as eyes, nose, mouth, and facial structure) to generate a unique identifier for authentication purposes.
NEW QUESTION # 49
An AI security administrator notices that the information referenced by the model is incorrectly formatted and missing values. Which of the following job roles would most likely be responsible for correcting this error?
Answer: C
Explanation:
A data engineer is responsible for preparing, cleaning, and formatting data pipelines. When information is incorrectly formatted or missing values, the data engineer ensures data integrity and quality before it is used by AI models.
NEW QUESTION # 50
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