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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Certificate Validity Period:	3 years
Exam Duration:	150 minutes
Exam Price:	US$459 (members), US$599 (non-members)
Exam Format:	Scenario-based, Multiple-choice
Real Exam Qty:	90
Related Certifications:	CISM CISSP
Passing Score:	450 (out of 800)
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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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	AI Governance and Program Management: This section of the exam measures the abilities of AI Security Governance Professionals and focuses on advising stakeholders in implementing AI security through governance frameworks, policy creation, data lifecycle management, program development, and incident response protocols.
Topic 2	AI Technologies and Controls: This section of the exam measures the expertise of AI Security Architects and assesses knowledge in designing secure AI architecture and controls. It addresses privacy, ethical, and trust concerns, data management controls, monitoring mechanisms, and security control implementation tailored to AI systems.
Topic 3	AI Risk Management: This section of the exam measures the skills of AI Risk Managers and covers assessing enterprise threats, vulnerabilities, and supply chain risk associated with AI adoption, including risk treatment plans and vendor oversight.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q174-Q179):

NEW QUESTION # 174
When robust input controls are not practical on a large language model (LLM) to prevent prompt injection attacks from external threats, which of the following would be the BEST compensating control to address the risk?

A. Fine-tune the system to validate the AI system's inputs
B. Review and annotate the AI system's outputs
C. Implement identity and access management (IAM)
D. Conduct human reviews of the AI system's inputs

Answer: B

Explanation:
When preventive input hardening isn't feasible for LLMs, AAISM prescribes compensating detective and corrective controls--notably human review and annotation of outputs prior to downstream action--to reduce harm from prompt injection. Output-side review gates prevent untrusted instructions from propagating, enable rapid suppression/feedback loops, and provide labeled examples for subsequent model hardening.

NEW QUESTION # 175
An organization plans to use an external vendor's AI service for customer retention analytics.
Management is worried about third-party dependency and the risk of unauthorized access to customer data. Which of the following is the BEST approach to reduce this risk?

A. Choose a vendor with a strong market reputation and integrate the AI service into enterprise architecture (EA).
B. Conduct an initial risk assessment of the vendor and follow up with AI impact assessments.
C. Add data protection requirements to the vendor contract and regularly monitor compliance.
D. Strengthen internal access controls and encrypt customer data before sharing with the vendor.

Answer: C

Explanation:
Including data protection requirements in the vendor contract establishes enforceable obligations for safeguarding customer data, while ongoing monitoring ensures the vendor continues to meet those obligations. This directly addresses both third-party dependency and unauthorized access risks in a sustainable and accountable manner.

NEW QUESTION # 176
Which of the following strategies BEST ensures generative AI tools do not expose company data?

A. Ensuring AI tools are compliant with local regulations
B. Testing AI tools before implementation
C. Conducting an independent AI data audit
D. Implementing a solution to prohibit the input of sensitive data

Answer: D

Explanation:
AAISM prioritizes preventive controls at the point of use for generative AI, specifically input- governance and DLP controls that block or redact confidential, regulated, or high-risk data before it can be sent to external models. Audits, pre-deployment tests, and regulatory conformance are necessary but do not themselves prevent an employee from pasting sensitive content into prompts. Enforcing input restrictions, pattern-based redaction, policy-aware controls, and allow- lists for approved contexts provides the highest assurance of preventing exposure.

NEW QUESTION # 177
Which of the following is the PRIMARY purpose of a dedicated AI system policy?

A. Ensuring environmental impact is minimized
B. Providing a framework to set AI objectives
C. Complying with external regulations
D. Optimizing AI accuracy

Answer: B

Explanation:
Per AAISM, an AI policy is a governance instrument that defines objectives, principles, roles, responsibilities, accountability, and control requirements for AI systems across their lifecycle. It establishes the framework within which performance, compliance, ethics, risk appetite, security, privacy, and sustainability objectives are set and operationalized. Environmental considerations (A), accuracy optimization (B), and regulatory compliance (D) are important outcomes addressed under the policy, but the primary purpose is to provide the overarching framework for objectives and controls.
References: AI Security Management (AAISM) Body of Knowledge - AI Governance Frameworks; Policies, Standards, and Procedures; Roles and Accountability in AI Programs.

NEW QUESTION # 178
Which of the following methods provides the MOST effective protection against model inversion attacks?

A. Reducing the model's complexity
B. Increasing the number of training iterations
C. Using adversarial training
D. Implementing regularization output

Answer: D

Explanation:
AAISM classifies model inversion as a privacy leakage threat where adversaries infer sensitive attributes or training records from model outputs. The recommended technical risk treatments emphasize reducing overfitting and information leakage via regularization and output-side constraints. Regularization (e.g., stronger penalties, output smoothing, confidence calibration, temperature limiting, and related techniques) reduces the model's tendency to memorize training data and curtails exploitable signal in outputs.
* A (adversarial training) targets perturbation robustness, not primary for inversion.
* B (reducing complexity) can help but is a coarse control with limited assurance versus explicit anti-leakage regularization.
* D (more iterations) typically increases overfitting and leakage risk.
AAISM further notes that privacy-preserving training and output minimization are preferred where feasible; among the listed options, regularization most directly addresses inversion risk.
References:* AI Security Management (AAISM) Body of Knowledge: Model Security-Privacy leakage threats (membership inference, inversion) and mitigation via regularization and output minimization.* AI Security Management Study Guide: Overfitting controls, calibration and confidence suppression as defenses against inference attacks.

NEW QUESTION # 179
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