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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Available Languages: | English |
| Exam Price: | US$459 (members), US$599 (non-members) |
| Exam Format: | Multiple-choice, Scenario-based |
| Related Certifications: | CISSP CISM |
| Real Exam Qty: | 90 |
| Passing Score: | 450 (out of 800) |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 150 minutes |
| 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 # 266
Personal data used to train AI systems can BEST be protected by:
Answer: B
Explanation:
AAISM guidance on privacy-preserving AI highlights anonymization as the most effective means of protecting personal data used in training. By irreversibly removing or masking identifiable attributes, anonymization ensures that training data cannot be linked back to individuals, thereby meeting key privacy obligations under laws such as GDPR. Erasing data after training may limit exposure but does not protect it during the training process. Ensuring data quality improves accuracy but does not mitigate privacy risk.
Hashing protects data integrity but does not guarantee anonymity, as hashes can sometimes be reversed or correlated. Therefore, anonymization is the recommended control for protecting personal data in AI training.
References:
AAISM Study Guide - AI Technologies and Controls (Privacy-Preserving Methods) ISACA AI Security Management - Data Anonymization Practices
NEW QUESTION # 267
An organization is adopting an agentic AI solution from an external vendor to support its internal IT operations. To evaluate the security posture of this system, which of the following provides the MOST reliable and independently verifiable evidence of implemented security controls?
Answer: D
Explanation:
Third-party audit reports provide independent assurance that the vendor's stated controls are designed and operating effectively against recognized criteria. Such attestations (e.g., audit/assurance frameworks) are traceable, repeatable, and verifiable, and they support supply-chain risk reviews and contractual assurance.
Internal red-team reports are not independent, industry "peer reviews" are not control attestations, and whitepapers are marketing/educational materials without evidence of control operation.
References: AI Security Management (AAISM) Body of Knowledge: Third-Party & Supply-Chain Assurance; Independent Assessment and Audit Evidence; Vendor Risk Management. AAISM Study Guide:
Assurance Artifacts for AI Services; Evaluating External AI Providers.
NEW QUESTION # 268
An information security manager is tasked with preparing management reports regarding the risk mitigation controls applied to the organization's customer service chatbot. Which of the following is MOST critical to include in these reports?
Answer: C
Explanation:
Management reports should provide actionable recommendations that help decision-makers improve the effectiveness of risk mitigation controls. This information is critical for ongoing governance, risk reduction, and informed management oversight of the chatbot environment.
NEW QUESTION # 269
AI developers often find it difficult to explain the processes inside deep learning systems PRIMARILY because:
Answer: C
Explanation:
Deep learning models learn high-dimensional, non-linear representations through layered parameterization that resists simple causal narratives. The internal mechanisms (e.g., distributed feature representations and complex statistical transformations) are difficult to map to human-interpretable rules, making explanation challenging. This is the primary reason for explainability difficulty in deep learning.
Option A addresses data origin/volatility, not explainability. Option B mischaracterizes model behavior as mutable "knowledge" without logs. Option C notes probabilistic foundations but that alone does not make systems inexplicable.
References: AI Security Management™ (AAISM) Body of Knowledge: "Explainability and Interpretability- Complexity in Deep Learning," "Model Behavior, Surrogates, and Post-hoc Explanations"; AAISM Study Guide: "Interpreting High-Dimensional Representations," "Limits of Transparency in Neural Architectures."
NEW QUESTION # 270
During which phase of the AI life cycle are models assessed for security and determined to be free of malicious manipulations?
Answer: A
Explanation:
The verification phase focuses on ensuring the model has been built correctly and is free from tampering or malicious manipulation by checking integrity, security controls, and adherence to design specifications before deployment.
NEW QUESTION # 271
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