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| Certification Vendor: | ISACA |
|---|---|
| Exam Name: | ISACA Advanced in AI Security Management (AAISM) Exam |
| Exam Number: | AAISM |
| Exam Duration: | 150 minutes |
| Available Languages: | Spanish, English |
| Passing Score: | 450/800 |
| Exam Format: | Scenario-based Questions, Multiple Choice |
| Related Certifications: | ISACA Advanced in AI Security Management (AAISM) |
| Real Exam Qty: | 90 |
| Exam Price: | USD $399 |
| Certificate Validity Period: | 3 years |
| Sample Questions: | ISACA AAISM Sample Questions |
| Exam Way: | Online remote proctored exam or test center exam |
| Pre Condition: | Candidates must 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 # 272
During the creation of a new large language model (LLM), an organization procured training data from multiple sources. Which of the following is MOST likely to address the CISO's security and privacy concerns?
Answer: A
Explanation:
AAISM guidance highlights data minimization as a critical practice for addressing both security and privacy concerns. By ensuring that only the minimum necessary data is collected and retained, the organization reduces the risk of sensitive information being exposed or misused during training. Data augmentation expands data but does not mitigate privacy risk. Classification organizes data but does not limit exposure. Data discovery helps locate sources but does not directly reduce risks. The control that directly aligns with privacy-by-design principles is data minimization.
NEW QUESTION # 273
An organization utilizes AI-enabled mapping software to plan routes for delivery drivers. A driver following the AI route drives the wrong way down a one-way street, despite numerous signs.
Which of the following biases does this scenario demonstrate?
Answer: A
Explanation:
AAISM defines automation bias as the tendency of individuals to over-rely on AI-generated outputs even when contradictory real-world evidence is available. In this scenario, the driver ignores traffic signs and follows the AI's instructions, showing blind reliance on automation.
Selection bias relates to data sampling, reporting bias refers to misrepresentation of results, and confirmation bias involves interpreting information to fit pre-existing beliefs. The most accurate description is automation bias.
NEW QUESTION # 274
An organization concerned about the ethical and responsible use of a newly developed AI product should consider implementing:
Answer: A
Explanation:
The AAISM framework highlights that organizations adopting AI must ensure accountability structures are in place to govern ethical and responsible use. An accountability model assigns clear responsibility for decisions, outputs, and risks related to AI systems. While model cards provide transparency about a model's design and performance, they are primarily documentation tools. Vendor monitoring focuses on third-party oversight, not internal accountability. Security by design improves resilience but does not by itself address ethical use. The governance approach that most directly supports responsible and ethical AI deployment is an accountability model.
References:
AAISM Study Guide - AI Governance and Program Management (Ethical AI and Accountability) ISACA AI Security Management - Responsible AI Practices
NEW QUESTION # 275
Which of the following is the MOST effective action an organization can take to address data security risk when using generative AI features in an application?
Answer: A
Explanation:
AAISM directs organizations to manage third-party AI risks through contractual and technical controls that explicitly govern data use, retention, training/fine-tuning, isolation, and deletion. The most effective data- security action when consuming generative AI features is to require enforceable opt-out provisions that prohibit the provider from using the organization's data for training or secondary purposes and that mandate retention limits and secure deletion. Third-party audit reports (A) provide assurance but do not guarantee provider behavior for your specific data; awareness policies (B) are necessary but insufficient to control external processing; IP ownership guidelines (D) address legal rights, not data-security risk.
References: AI Security Management™ (AAISM) Body of Knowledge - Third-Party/Procurement Controls; Data Use & Retention Clauses; Training/Fine-tuning Opt-Out; Secure Deletion and Purpose Limitation.
NEW QUESTION # 276
When documenting information about machine learning (ML) models, which of the following artifacts BEST helps enhance stakeholder trust?
Answer: C
Explanation:
Themodel cardis a governance artifact that communicatesintended use, performance characteristics, limitations, fairness considerations, and ethical notesof an ML model.
AAISM governance materials highlight thatstakeholder trustcomes from transparency and explainability.
While hyperparameters and data quality controls are technical details, they lack stakeholder-facing clarity.
Model prototyping is part of development but not a governance record.
Model cards are explicitly recommended to:
* Provideexplainabilityand context for decision-making.
* Demonstrate governance, transparency, and compliance.
* Enable stakeholders (regulators, auditors, business leaders) to trust the system.
Therefore, themodel cardis the artifact that best enhances trust.
NEW QUESTION # 277
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