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NEW QUESTION # 293
Which of the following controls would MOST effectively mitigate worst-case service disruption scenarios affecting an AI-based application system?
Answer: A
Explanation:
Including AI-specific disruption scenarios in the organization's Disaster Recovery Plan (DRP) ensures preparedness for worst-case events affecting AI systems. The AAIA™ Study Guide recommends tailoring business continuity and DR strategies to cover model unavailability, data corruption, and AI-specific dependencies.
"AI disruptions can arise from unique causes such as model corruption, adversarial attacks, or drift.
Integrating these into the DRP allows for effective, scenario-specific responses and minimizes downtime." Tabletop exercises (A) are valuable for preparedness but are less comprehensive than scenario planning. Kill chains (B) are security-specific. KRIs (C) aid in monitoring but don't ensure recovery. Therefore, D offers the most robust mitigation.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Business Continuity and Disaster Recovery Planning for AI"
NEW QUESTION # 294
A bank uses a video-based know your customer (KYC) verification process. Cybercriminals exploit this process by using deepfake technology to impersonate bank customers. Which of the following countermeasures is the BEST way for the bank to mitigate this risk?
Answer: A
Explanation:
Liveness detection is the most effective countermeasure against deepfakes in video-based verification. AI- based liveness detection analyzes facial movement, micro-expressions, and other biometric cues to differentiate real humans from manipulated video content.
"To protect against identity spoofing and deepfake exploitation, biometric systems must incorporate liveness detection protocols capable of detecting synthetic imagery or falsified video data." Encryption (C) protects data at rest and in transit but does not prevent impersonation. Discontinuation (D) may not be necessary if effective countermeasures like B are in place.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Biometric Security and AI Authentication Methods"
NEW QUESTION # 295
An IS auditor notes the combined number of records utilized within the training, validation, and testing data sets exceeds the total number of records in the original data set. Which of the following is MOST important for the auditor to determine?
Answer: A
NEW QUESTION # 296
Which of the following is the MOST important AI data governance method to protect privacy and data security?
Answer: B
Explanation:
Periodic monitoring is the MOST critical governance mechanism for protecting privacy and data security in AI systems, especially those handling sensitive or personal data. ISACA's AAIA guidance emphasizes that data governance must be continuous because AI systems evolve, data drifts, risk exposures shift, and privacy threats increase over time.
Periodic monitoring ensures:
Detection of unauthorized access
Identification of anomalous data use
Confirmation that privacy controls remain effective
Early detection of data misclassification or leakage
Verification of compliance with retention, deletion, and minimization requirements
NEW QUESTION # 297
An IS auditor is planning for an audit of an organization ' s use of AI to screen applicants for open positions.
Which of the following is MOST important to include within the audit scope?
Answer: D
Explanation:
In recruitment AI, the integrity and fairness of the output are almost entirely dependent on the " Sources of data used. " If the training data comes from historical records that contain human bias (e.g., only hiring from certain universities or genders), the AI will codify and automate that bias. The ISACA AAIA™ manual states that auditing the " Data Source " and " Data Lineage " is the most effective way to identify the root cause of algorithmic discrimination. While data retention (Option B) is a general privacy concern, it is secondary to the high-impact risk of biased decision-making inherent in the data sources.
NEW QUESTION # 298
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