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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 2: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| Topic 4: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
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NEW QUESTION # 159
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 # 160
Which of the following ensures the integrity of data usage in an AI system?
Answer: A
Explanation:
Basic Concept: Data integrity in AI systems requires not only that data is accurate at a point in time, but that its entire history of transformation and usage can be traced and verified. Tracking how data has been used and transformed throughout the AI system lifecycle provides ongoing integrity assurance. CompTIA SecAI+ Study Guide covers data governance controls including lineage for AI integrity.
Why D is Correct: Data lineage tracks and documents the complete journey of data from its origin through every transformation, processing step, and use within an AI system. By recording what happened to the data, when, by whom, and through which processes, data lineage provides the audit trail needed to ensure data integrity throughout the AI system ' s data usage lifecycle. It enables verification that data has been used as intended and has not been improperly modified at any stage.
Why A is Wrong: Data masking replaces sensitive data values with anonymized equivalents to protect privacy. It is a confidentiality control that modifies data values rather than a mechanism for ensuring or tracking data integrity across the system.
Why B is Wrong: Data cleansing removes or corrects errors, inconsistencies, and noise in datasets to improve data quality. It is a data preparation activity that improves data accuracy at a point in time but does not track data usage or provide ongoing integrity assurance throughout the AI system lifecycle.
Why C is Wrong: Data verification confirms that data meets expected quality standards and validates its accuracy at a specific check point. While important for quality assurance, it provides a point-in-time check rather than continuous tracking of data usage and transformations as data lineage does.
NEW QUESTION # 161
An organization is developing and implementing AI features into a customer service application.
Which of the following practices should the organization put the place before releasing the application for customer trials?
Answer: C
Explanation:
Before releasing AI features for customer trials, it is critical to protect sensitive information that may be used during testing. Data masking and sanitization ensure customer or corporate data is anonymized or obfuscated, reducing the risk of data exposure while still allowing realistic evaluation of the AI system.
NEW QUESTION # 162
An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?
Answer: D
Explanation:
Checksums ensure data integrity by detecting any unauthorized or accidental changes to the dataset. This provides a reliable way to confirm that sensitive attributes such as age, ethnicity, and diabetes status remain unaltered during development.
NEW QUESTION # 163
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: B
Explanation:
Guardrails restrict and control how an AI model processes and responds to inputs, making them the most effective defense against manipulation attacks such as prompt injection or malicious input alteration.
NEW QUESTION # 164
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