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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| Topic 2: AI-assisted Security | 24% | - AI in security strategy and operations
|
| Topic 3: Securing AI Systems | 40% | - Secure AI development and operations
|
| Topic 4: AI Governance, Risk and Compliance | 19% | - Compliance and legal requirements
|
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NEW QUESTION # 148
An airline corporation wants to implement a chatbot application using a large language model (LLM) so its customers:
- Can ask question and receive answers about flight details.
- Have the option to upload files.
Which of the following security controls should the airline use to protect against malicious input and unauthorized use beyond the service-level agreement? (Choose two.)
Answer: A,D
Explanation:
Prompt guardrails are needed to prevent malicious or manipulated inputs (prompt injection) from causing the chatbot to provide harmful, misleading, or unauthorized responses.
Model token quotas limit the amount of input/output a user can generate, preventing abuse or excessive usage beyond the service-level agreement (SLA).
NEW QUESTION # 149
A security analyst receives an alert about an AI system and is investigating the following output:
Which of the following is the most appropriate control the analyst should recommend?
Answer: D
Explanation:
Basic Concept: Suspicious or unexpected AI system outputs are often caused by malicious or malformed user inputs that exploit the AI ' s input processing. Validating and sanitizing user inputs before they reach the AI model prevents many classes of attacks including prompt injection, data exfiltration attempts, and input manipulation. CompTIA SecAI+ Study Guide emphasizes input validation as a foundational AI security control.
Why B is Correct: Implementing user input validation applies checks and constraints to all user-submitted content before it is processed by the AI system. Input validation can enforce length limits, detect injection patterns, filter disallowed characters or command structures, and ensure inputs conform to expected formats.
By rejecting malicious or malformed inputs at the entry point, this control prevents them from reaching the model and causing the suspicious outputs observed.
Why A is Wrong: Data sanitization processes data to remove harmful elements and is closely related to validation. However, input validation is broader and more proactive, checking conformance to rules before processing, while sanitization typically operates during or after processing. Validation at the input boundary is the more appropriate first-line control.
Why C is Wrong: Monitoring logs for attack keywords is a detective control that identifies attacks after they have already affected the system. It does not prevent suspicious outputs from being generated in the first place.
Why D is Wrong: Hardening the Model Context Protocol server improves the security of the infrastructure hosting the AI components. While important for infrastructure security, it does not directly validate or inspect the content of user inputs that cause suspicious outputs.
NEW QUESTION # 150
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: A
Explanation:
Basic Concept: When an AI fraud detection model produces false positives (blocking legitimate transactions), this indicates the model ' s decision boundary is insufficiently calibrated. The model needs improved training data to better distinguish fraudulent from legitimate transactions. CompTIA SecAI+ covers model performance improvement under AI-assisted security.
Why A is Correct: Retraining the model with more data and recent transaction patterns directly addresses the root cause of false positives. Additional representative legitimate transaction data helps the model learn more accurate decision boundaries, reducing false positives while maintaining detection sensitivity for actual fraud.
This improves model accuracy without abandoning AI-based detection.
Why B is Wrong: Token usage and rate limits are cost and resource management controls for LLM APIs.
They have no relevance to improving the accuracy of a fraud detection ML model that incorrectly classifies legitimate transactions.
Why C is Wrong: Encrypting data and applying access controls are data security measures that protect confidentiality and integrity. They do not address model classification accuracy or the false positive problem in fraud detection.
Why D is Wrong: Rolling back to a traditional system abandons the capabilities of AI-based fraud detection.
The appropriate response to model performance issues is to improve the model through retraining, not to regress to less capable detection approaches.
NEW QUESTION # 151
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 # 152
Which IR document defines who must be contacted during a breach and within what timeframe?
Answer: B
Explanation:
The communications plan specifies notification roles and timelines.
NEW QUESTION # 153
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