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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Securing AI Systems | 40% | - Defending against AI-specific attacks
|
| AI-assisted Security | 24% | - AI for threat detection and response
|
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NEW QUESTION # 143
A financial organization implements a new AI-based fraud detection system to flag suspicious transactions. A security analyst discovers that it occasionally blocks legitimate transactions.
Which of the following is the best recommendation?
Answer: B
Explanation:
False positives occur when the AI model lacks sufficient or representative training data.
Retraining the model with more diverse and recent transaction patterns improves accuracy, reducing the chance of legitimate transactions being incorrectly flagged.
NEW QUESTION # 144
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: B
Explanation:
The described behavior - iteratively assessing the environment and adapting attack paths based on prior outcomes - matches automated penetration testing, where AI-driven tools simulate attack chains and adjust tactics dynamically.
NEW QUESTION # 145
A recent release of an AI software update exposes confidential customer information due to storage misconfiguration.
Which of the following data security controls will help maintain confidentiality despite the data leak?
Answer: B
Explanation:
Basic Concept: When a storage misconfiguration leads to data exposure, the question is which encryption type would have protected the confidentiality of data stored in that misconfigured storage. The three states of data
- at rest, in transit, and in use - each require different encryption mechanisms. CompTIA SecAI+ Study Guide covers encryption states and their applicability to AI data protection.
Why D is Correct: Encryption at rest protects data stored in databases, file systems, and storage media by encrypting it so that even if unauthorized parties gain access to the storage through a misconfiguration, the data remains unreadable without the decryption key. Since the exposure resulted from a storage misconfiguration that allowed access to stored data, encryption at rest would have maintained confidentiality of the customer information despite the misconfiguration granting storage access.
Why A is Wrong: Model encryption specifically protects AI model weights and parameters from unauthorized access. It does not protect customer data stored in databases or data stores associated with the AI system.
Why B is Wrong: Encryption in transit protects data moving between components over networks. It does not protect data stored at rest in misconfigured storage that is accessed directly rather than over a network connection.
Why C is Wrong: Encryption in use (homomorphic encryption or confidential computing) protects data while it is being actively processed in memory. It addresses runtime processing security, not the confidentiality of data stored in misconfigured storage that is not currently being processed.
NEW QUESTION # 146
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
Answer: D
Explanation:
Basic Concept: Reconnaissance is the information gathering phase of an attack. LLMs can be enhanced to perform more effective reconnaissance by giving them access to current, specific information beyond their training data cutoff. CompTIA SecAI+ covers AI augmentation techniques including RAG under AI-assisted security.
Why A is Correct: RAG enhances an LLM by connecting it to an external knowledge base or real-time data sources that it can query during inference. For reconnaissance purposes, a RAG-enabled LLM can access up- to-date organizational information, technical documentation, and intelligence feeds that go beyond its static training data. This makes the LLM significantly more capable for gathering current, targeted intelligence about specific organizations or infrastructure.
Why B is Wrong: Creating a web scraper is a basic data collection technique. While AI can help write scraper code, the scraper itself is a simple script that does not enhance the LLM ' s intelligence or reasoning capabilities for sophisticated reconnaissance.
Why C is Wrong: Instructing an AI assistant to query as an administrator is a prompt manipulation attempt.
An LLM cannot actually gain elevated permissions through a prompt instruction; this describes social engineering or privilege escalation via prompting, not a performance enhancement technique.
Why D is Wrong: Prompting a chatbot to describe naming patterns is a basic use of an existing LLM ' s knowledge. It does not strengthen or enhance the model ' s capabilities; it merely queries what the model already knows from training data, which may be outdated or generic.
NEW QUESTION # 147
An architect is using the firm ' s recommended large language model (LLM) to find an internal solution for content management.
Given the following:
Which of the following controls is the best for mitigating this issue?
Answer: A
Explanation:
Basic Concept: LLM hallucinations occur when the model generates plausible-sounding but factually incorrect or fabricated information. For internal content management solutions where accuracy is critical, detecting and handling hallucinated responses before they are acted upon is essential. CompTIA SecAI+ Study Guide covers response validation as a mitigation for hallucination risks.
Why B is Correct: Response validation implements checks that verify the accuracy and relevance of LLM- generated responses before they are presented to users or acted upon. This can involve cross-referencing responses against authoritative internal data sources, using a secondary model to evaluate response accuracy, or implementing confidence scoring that flags low-confidence responses for human review. Response validation directly addresses the hallucination problem by catching inaccurate responses before they cause harm.
Why A is Wrong: Model training addresses hallucinations at the model level by providing more accurate training data or fine-tuning. While effective long-term, it requires significant time and resources and does not provide immediate protection against hallucinations in the currently deployed model.
Why C is Wrong: Access controls manage who can query the LLM and what resources they can access. They do not inspect or validate the accuracy of the model ' s responses, so they cannot mitigate hallucination risks.
Why D is Wrong: Integrity monitoring tracks whether data or systems have been tampered with or changed unexpectedly. It is relevant for detecting unauthorized modifications but does not validate whether LLM- generated content accurately reflects reality or internal authoritative data.
NEW QUESTION # 148
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