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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Real Exam Qty:	90
Exam Price:	USD $399
Available Languages:	Spanish, English
Certificate Validity Period:	3 years
Passing Score:	450/800
Exam Duration:	150 minutes
Related Certifications:	ISACA Advanced in AI Security Management (AAISM)
Exam Format:	Scenario-based Questions, Multiple Choice
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 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 (Q335-Q340):

NEW QUESTION # 335
Which of the following is the PRIMARY benefit of incorporating an AI component into an organization's security program?

A. Providing focus on vulnerabilities and attacks specific to AI while applying cybersecurity best practices
B. Specifying tailored AI training and learning pathways for security team members
C. Maintaining clear separation between AI security and existing information security governance initiatives
D. Ensuring consistent AI integration across all organizational business units

Answer: A

Explanation:
Incorporating an AI component ensures that security efforts address AI-specific threats and vulnerabilities while aligning with established cybersecurity practices, strengthening the overall security posture.

NEW QUESTION # 336
In managing the life cycle of agentic AI systems, which of the following would be the GREATEST concern during the destruction phase of AI models and associated data?

A. Absence of verifiable audit trails confirming secure elimination of residual AI models and data
B. Delays and challenges encountered in reassigning new AI models and data
C. Incomplete records of AI model specifications and version histories
D. Inability to preserve explainability and transparency for agentic AI systems following secure disposal

Answer: A

Explanation:
The greatest concern during the destruction phase is the inability to verify that AI models and associated data were securely and completely eliminated. Without verifiable audit trails, residual models or sensitive data may remain accessible, creating significant security, privacy, and compliance risks.

NEW QUESTION # 337
An organization is deploying an automated AI cybersecurity system. Which of the following would be the MOST effective strategy to minimize human error and improve overall security?

A. Utilizing machine learning (ML) algorithms to ensure responsible use
B. Using historical data to train AI detection software
C. Implementing manual monitoring of potential alerts
D. Conducting periodic penetration testing

Answer: B

Explanation:
Training detection models on relevant, representative historical data improves signal quality, reduces false positives, and automates triage-directly lowering human workload and error rates (e.g., alert fatigue, missed correlations). Penetration testing is valuable but episodic and does not systematically reduce day-to-day operator error. "Ensure responsible use" is a governance aim, not a concrete method to cut human error in detection. Manual monitoring increases reliance on human judgment and is prone to inconsistency.
References: AI Security Management (AAISM) Body of Knowledge: Model Development & Evaluation Controls; Data Selection and Representativeness; Operationalization to Reduce Human Error. AAISM Study Guide: Tuning Detection Systems with Historical Corpora; Alert Quality, Precision/Recall, and SOC Workflow Integration.

NEW QUESTION # 338
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. Retrieval-augmented generation (RAG)
B. Output sanitization
C. Input filtering
D. Use of additional public models

Answer: A

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 # 339
Which of the following would BEST help an organization align its AI initiatives with business objectives?

A. Complying with applicable AI-related regulations
B. Protecting enterprise information used by AI projects
C. Establishing an AI governance committee
D. Ensuring ethical use of AI technologies in projects

Answer: C

Explanation:
An AI governance committee provides cross-functional oversight to align AI strategy, investment, and risk appetite with business goals. It sets policies, prioritizes portfolios, ensures accountability, and integrates compliance, ethics, and security into decision-making. While compliance, ethics, and information protection are essential, governance is the primary mechanism that systematically connects AI initiatives to enterprise objectives.
References: AI Security Management (AAISM) Body of Knowledge: AI Governance Operating Model- Structures, Roles, and Decision Rights; AAISM Study Guide: Strategic Alignment, Portfolio Oversight, and Accountability Mechanisms.

NEW QUESTION # 340
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