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| Section | Weight | Objectives |
|---|---|---|
| AI-Assisted Security | 24% | - Operational Use of AI
|
| Securing AI Systems | 40% | - Adversarial Defense
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
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NEW QUESTION # 69
A recently deployed AI system becomes persistently unavailable. A restart temporarily fixes the issue, but the issue happens again. Upon examination of application programming interface (API) logs, an analyst finds that external calls continued to use system resources after the action completed.
Which of the following is the best way to improve availability of the system?
Answer: D
Explanation:
The persistent unavailability is caused by external calls consuming resources even after completion, indicating sessions are not being closed. Enforcing session expiration ensures unused sessions are terminated, freeing resources and improving system availability.
NEW QUESTION # 70
Which of the following describes the most significant risk associated with AI agents that make autonomous decisions?
Answer: B
Explanation:
Output bias is the best answer because an autonomous AI agent can convert biased model judgments into consequential decisions without requiring a human to review each result. CompTIA SecAI+ identifies introduction of bias as a significant AI risk and separately identifies autonomous systems as an important governance concern. Responsible AI principles such as fairness, reliability, accountability, explainability, and human oversight exist partly to control this problem.
If an agent ' s model produces systematically biased recommendations, those biased outputs can directly influence hiring, access decisions, financial transactions, security actions, or other automated processes.
Autonomy therefore increases the impact of output bias because an erroneous or unfair recommendation may become an action before a person can intervene.
Accidental data leakage is also an important AI risk, but the question emphasizes autonomous decision- making , making biased decisions the more directly associated concern. Overfitting is primarily a model- development problem in which a model memorizes training characteristics and fails to generalize effectively.
Increased workload is contrary to one of the primary motivations for using agents, which is automation.
Therefore, output bias represents the most relevant risk among the available choices.
NEW QUESTION # 71
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: A
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 # 72
SIMULATION
Instructions
Click the (+) to assign each threat category into its appropriate framework.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
An architect is modeling an agentic system to meet security standards.
Answer:
Explanation:
Explanation:
MAESTRO: Overreliance, Insecure plug-in design
OWASP Top 10: Broken access control, Identification and authentication failures OWASP Top 10 LLM: Prompt injection, Model denial of service STRIDE: Elevation of privilege, Repudiation, Supply chain vulnerabilities, Insecure design MAESTRO addresses AI-specific misuse such as excessive trust in outputs (overreliance) and unsafe plug-in design.
OWASP Top 10 maps to classic web threats: broken access control and authentication failures are key risks.
OWASP Top 10 LLM focuses on LLM-related threats like prompt injection and denial of service targeting the model.
STRIDE categories cover privilege escalation, repudiation (denying actions), supply chain risks, and insecure design flaws.
NEW QUESTION # 73
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: A
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 # 74
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