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ISACA AAISM Exam Syllabus Topics:

Section	Weight	Objectives
AI Risk Management	31%	- Third-party and supply chain risk management 1. Supply chain security controls 2. Vendor risk evaluation - AI risk assessment and treatment 1. Risk thresholds and mitigation 2. Risk identification and analysis - AI threat and vulnerability management 1. Threat modeling for AI systems 2. AI vulnerability assessment
AI Governance and Program Management	31%	- AI policies and procedures 1. AI-related strategy development 2. Policy implementation and compliance - AI security program management 1. Incident response and business continuity 2. Program development and maintenance - Stakeholder considerations and governance frameworks 1. AI governance models and oversight 2. Industry frameworks and regulatory requirements - AI asset and data lifecycle management 1. Data governance 2. AI asset inventory and management
AI Technologies and Controls	38%	- Trust, safety and compliance controls 1. Responsible AI practices 2. Compliance and governance validation - AI security monitoring and operations 1. Operational AI controls 2. Security monitoring and logging - AI security architecture and design 1. Secure AI system architecture 2. AI infrastructure protection - Data management and privacy controls 1. Data integrity and protection 2. Privacy and ethical AI controls

Section

Weight

Objectives

AI Risk Management

31%

- Third-party and supply chain risk management

1. Supply chain security controls
2. Vendor risk evaluation

- AI risk assessment and treatment

1. Risk thresholds and mitigation
2. Risk identification and analysis

- AI threat and vulnerability management

1. Threat modeling for AI systems
2. AI vulnerability assessment

AI Governance and Program Management

31%

- AI policies and procedures

1. AI-related strategy development
2. Policy implementation and compliance

- AI security program management

1. Incident response and business continuity
2. Program development and maintenance

- Stakeholder considerations and governance frameworks

1. AI governance models and oversight
2. Industry frameworks and regulatory requirements

- AI asset and data lifecycle management

1. Data governance
2. AI asset inventory and management

AI Technologies and Controls

38%

- Trust, safety and compliance controls

1. Responsible AI practices
2. Compliance and governance validation

- AI security monitoring and operations

1. Operational AI controls
2. Security monitoring and logging

- AI security architecture and design

1. Secure AI system architecture
2. AI infrastructure protection

- Data management and privacy controls

1. Data integrity and protection
2. Privacy and ethical AI controls

>> Valid AAISM Exam Duration <<

Quiz ISACA - AAISM - ISACA Advanced in AI Security Management (AAISM) Exam Updated Valid Exam Duration

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ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q222-Q227):

NEW QUESTION # 222
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. Inability to preserve explainability and transparency for agentic AI systems following secure disposal
C. Incomplete records of AI model specifications and version histories
D. Delays and challenges encountered in reassigning new AI models and data

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 # 223
An organization has implemented a natural language processing model to respond to customer questions when personnel are not available. A pre-implementation security assessment revealed attackers could access sensitive company data through a chat interface injection attack. Which of the following is the BEST way to prevent this attack?

A. Manually reviewing AI model outputs
B. Conducting regular information security audits
C. Implementing input validation and templates
D. Ensuring continuous monitoring and data tagging

Answer: C

Explanation:
To prevent prompt/interface injection, AAISM prioritizes preventive technical controls at the boundary: input validation/sanitization, structured templates/system prompts, allow/deny lists, and context isolation. These measures constrain user-supplied content and block adversarial instructions from being interpreted as system directives.

NEW QUESTION # 224
An organization is looking to purchase an AI application from a vendor but is concerned about the security of its data. Which of the following is the MOST effective way to address this concern?

A. Initiate discussions between the organization's and the vendor's legal teams
B. Ensure vendors disclose how the application uses the organization's data
C. Assess the vendor's publicly available AI usage policy
D. Mandate an AI security audit by an external auditor before procurement

Answer: B

Explanation:
AAISM's approach to third-party and vendor risk for AI systems stresses data usage transparency as a primary control. The guidance explains that organizations must obtain clear documentation on "what data is collected, how it is processed, stored, retained, and whether it is reused for training or shared with other parties." Option C directly addresses this by requiring the vendor to disclose how the application uses organizational data, enabling appropriate risk assessment, contractual controls, and technical safeguards. An external audit (A) can be useful but may be costly and not always feasible pre-procurement. Legal discussions (B) are important but ineffective without clarity on data flows. Publicly available policies (D) are often high- level and marketing-oriented, lacking the specificity required for proper risk evaluation. Therefore, obtaining explicit data usage disclosures from the vendor is the most effective starting point.
References: AI Security Management™ (AAISM) Study Guide - Third-Party AI Risk and Data Sharing; Vendor Governance Requirements.

NEW QUESTION # 225
Which of the following is MOST important to monitor in order to ensure the effectiveness of an organization's AI vendor management program?

A. Vendor reviews of external AI threat reports
B. Vendor participation in industry AI research
C. Vendor results in compliance training programs
D. Vendor compliance with AI-related requirements

Answer: D

Explanation:
The AAISM framework specifies that the primary metric of effectiveness in vendor management is the vendor's compliance with AI-related requirements defined in contracts and governance frameworks. This provides measurable assurance that vendors adhere to agreed-upon privacy, security, and ethical standards. Reviews of threat reports, training results, or research participation are supplemental and may support continuous improvement, but they do not establish compliance accountability. Governance requires a direct focus on whether contractual and regulatory obligations are being fulfilled. Therefore, vendor compliance with AI requirements is the most important monitoring focus.

NEW QUESTION # 226
Which of the following BEST enables an organization to strengthen information security controls around the use of generative AI applications?

A. Validating AI model training data
B. Monitoring AI outputs against policy
C. Ensuring controls exceed industry benchmarks
D. Implementing a kill switch

Answer: B

Explanation:
For generative AI, the primary enterprise security exposure is data and content exfiltration or policy violations at output, including leakage of sensitive data, toxic content, or regulatory non-compliance. AAISM prescribes policy-aligned output monitoring (e.g., DLP checks, PII/PHI detection, toxicity/safety filters, watermark
/attribution checks) integrated into inference gateways to enforce organizational policies and evidence compliance. Exceeding benchmarks (A) is not a control; training-data validation (C) may be infeasible with third-party LLMs; and kill switches (D) are essential contingency controls but do not continuously strengthen everyday security posture.
References: AI Security Management™ (AAISM) Body of Knowledge - GenAI Governance and Guardrails; Output Filtering and DLP Controls; Policy Enforcement at Inference. AAISM Study Guide - Monitoring & Auditing of GenAI; Gateway Patterns for Safe Use; Control Effectiveness Measures.

NEW QUESTION # 227
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