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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Related Certifications:	ISACA Advanced in AI Security Management (AAISM)
Available Languages:	English, Spanish
Exam Duration:	150 minutes
Real Exam Qty:	90
Exam Price:	USD $399
Passing Score:	450/800
Exam Format:	Multiple Choice, Scenario-based Questions
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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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 (Q426-Q431):

NEW QUESTION # 426
How can an organization best remain compliant when decommissioning an AI system that recorded patient data?

A. Perform a post-destruction risk assessment
B. Ensure a certificate of destruction is received and archived
C. Update governance policies based on lessons learned
D. Ensure backups are tested and access controls are audited

Answer: B

Explanation:
AAISM and healthcare privacy regulations (HIPAA-like guidance within AAISM contexts) stress that documented destruction of sensitive data is required when decommissioning systems.
A certificate of destruction ensures:
- proof of lawful data disposal
- auditability
- regulatory compliance
- defensibility during inspections

NEW QUESTION # 427
Within an incident handling process, which of the following would BEST help restore end user trust with an AI system?

A. The AI model's outputs are validated by team members
B. The AI model prioritizes incidents based on business impact
C. Remediation of the AI system based on lessons learned
D. AI is being used to monitor incident detection and alerts

Answer: A

Explanation:
Restoring end user trust during incident handling requires visible, immediate assurance that system outcomes are safe and appropriate. AAISM prescribes human oversight and approval gates for high-risk AI decisions, with human validation of outputs before use as a primary control to maintain trust while technical remediation is underway. Prioritization (A) and monitoring (B) aid operations but do not directly rebuild user confidence in outcomes. Post-incident improvements (D) are essential for long-term assurance but do not provide the immediate trust restoration that supervised, human-validated outputs deliver.
References: AI Security Management (AAISM) Body of Knowledge - Incident Handling & Communications; Human Oversight and Approval Gates; Trust Restoration During AI Incidents.

NEW QUESTION # 428
An internet service provider (ISP) uses a chatbot based on a public model to help resolve connectivity issues. Customers report that the chatbot's instructions lack details about the ISP's proprietary equipment. Which of the following is the BEST way to improve the user experience?

A. Input filtering
B. Output sanitization
C. Retrieval-augmented generation (RAG)
D. Use of additional public models

Answer: C

Explanation:
Retrieval-augmented generation integrates the chatbot with the organization's proprietary knowledge sources, enabling it to provide accurate and detailed responses specific to the ISP's equipment while maintaining the strengths of the base model.

NEW QUESTION # 429
Which strategy BEST ensures generative AI tools do not expose company data?

A. Testing AI tools before implementation
B. Implementing a solution prohibiting input of sensitive data
C. Conducting an independent AI data audit
D. Ensuring AI tools comply with local regulations

Answer: B

Explanation:
AAISM identifies the strongest immediate control for preventing data leakage into generative AI systems as technically restricting or blocking user entry of sensitive data.

NEW QUESTION # 430
Which of the following strategies is the MOST effective way to protect against AI data poisoning?

A. Using robust data validation techniques and anomaly detection
B. Increasing model complexity to better handle data variations
C. Incorporating more features and data into model training
D. Ensuring the model is trained on diverse data sources

Answer: A

Explanation:
AAISM directs organizations to prevent training-time attacks by hard-gating data ingestion with provenance checks, schema and label validation, sanitization, and anomaly/outlier detection prior to model training. These controls most directly block poisoned records from entering the pipeline and are prioritized over architectural complexity or sheer data volume. Diversity of sources can improve representativeness but does not reliably stop adversarial contamination.
References: AI Security Management™ (AAISM) Body of Knowledge - Adversarial ML: Training-Time Threats; Secure Data Ingestion & Validation Controls; AI Risk Treatment and Assurance. AAISM Study Guide - Poisoning Prevention Gates; Provenance, Quality, and Anomaly Screening in ML Pipelines.

NEW QUESTION # 431
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