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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Passing Score: | 450 (out of 800) |
| Exam Duration: | 150 minutes |
| Real Exam Qty: | 90 |
| Exam Format: | Multiple-choice, Scenario-based |
| Related Certifications: | CISM CISSP |
| Certificate Validity Period: | 3 years |
| Exam Price: | US$459 (members), US$599 (non-members) |
| Available Languages: | English |
| Recommended Training: | ISACA Official AAISM Training |
| Exam Registration: | ISACA AAISM Registration |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Computer-based; available at authorized PSI testing centers or via remote proctoring |
| Pre Condition: | Hold an active CISM or CISSP certification |
| Official Syllabus URL: | https://www.isaca.org/credentialing/aaism/aaism-exam-content-outline |
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NEW QUESTION # 373
An organization needs large data sets to perform application testing. Which of the following would BEST fulfill this need?
Answer: C
Explanation:
According to AAISM study guidance, the most direct and effective way to obtain large volumes of diverse data for application testing is through open-source data repositories. These repositories provide freely available, well-documented, and often standardized data that supports testing and benchmarking in a compliant manner. Model cards document AI behavior but do not provide data. Incorporating search content may introduce legal, privacy, and quality risks. Data augmentation is useful for expanding existing sets but does not provide the breadth or size required when starting with insufficient data. The recommended best practice for sourcing large testing datasets is therefore the use of open-source repositories.
References:
AAISM Study Guide - AI Technologies and Controls (Data Sources and Testing Practices) ISACA AI Security Management - Data Governance and Compliance in AI Testing
NEW QUESTION # 374
How can an organization BEST protect itself from payment diversions caused by deepfake attacks impersonating management?
Answer: B
Explanation:
AAISM's risk management framework stresses that the most effective defense against deepfake-enabled fraud, such as payment diversion, is resilient payment approval processes. This includes multi-step verification, segregation of duties, and independent confirmations for high-value transactions. Employee training, policies, or limiting payment frequency may reduce exposure, but they cannot guarantee prevention.
Only process-based controls enforce structural safeguards that prevent fraudulent instructions from being executed, even if a deepfake impersonation attempt is successful.
References:
AAISM Exam Content Outline - AI Risk Management (Fraud and Deepfake Risk) AI Security Management Study Guide - Transactional Resilience and Controls
NEW QUESTION # 375
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?
Answer: A
Explanation:
AAISM clarifies that model owners hold responsibility for ensuring corrective actions are implemented after AI audits. They are accountable for:
- model behavior
- compliance gaps
- security improvements
- governance alignment
NEW QUESTION # 376
Which BEST addresses hallucination risk in AI systems?
Answer: D
Explanation:
AAISM states that human oversight is the strongest control for hallucination risks, especially in high-impact decisions. Humans validate outputs, correct errors, and override unsafe model responses.
Automated validation (C) helps but cannot fully detect hallucinations. Chunking (B) improves information handling but not hallucination mitigation. Content enrichment (D) does not reduce hallucinations.
References: AAISM Study Guide - Human-in-the-Loop (HITL) Controls; Hallucination Mitigation.
NEW QUESTION # 377
Which of the following BEST enables an organization to strengthen information security controls around the use of generative AI applications?
Answer: D
Explanation:
For generative AI, the primary enterprise security exposure is data and content exfiltration or policy violations at output, including leakage of sensitive data, toxic content, or regulatory non- compliance. AAISM prescribes policy-aligned output monitoring (e.g., DLP checks, PII/PHI detection, toxicity/safety filters, watermark/attribution checks) integrated into inference gateways to enforce organizational policies and evidence compliance.
NEW QUESTION # 378
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