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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:	CISSP CISM
Passing Score:	450 (out of 800)
Exam Duration:	150 minutes
Exam Format:	Scenario-based, Multiple-choice
Exam Price:	US$459 (members), US$599 (non-members)
Real Exam Qty:	90
Available Languages:	English
Certificate Validity Period:	3 years
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 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 (Q286-Q291):

NEW QUESTION # 286
A large financial services organization wants to use AI to create financial forecasts for clients.
Which of the following implementations is MOST likely to achieve the highest level of security?

A. Large language model (LLM)
B. Private
C. Public
D. Retrieval-augmented generation (RAG)

Answer: B

Explanation:
A private implementation keeps data, models, and processing within the organization's controlled environment, ensuring stronger protection of sensitive financial information, better compliance with regulatory requirements, and reduced exposure to external threats.

NEW QUESTION # 287
Which of the following is the MOST effective defense against cyberattacks that alter input data to avoid detection by the model?

A. Enhancing model robustness through adversarial training
B. Applying differential privacy controls on training datasets
C. Implementing restricted access to the model's internal parameters
D. Conducting periodic monitoring activities on the model's decisions

Answer: A

Explanation:
Evasion attacks manipulate inputs to induce misclassification while leaving the model unchanged.
AAISM prescribes adversarial robustness controls, with adversarial training as a primary measure:
incorporate adversarially perturbed examples into training/validation to harden decision boundaries and improve resilience across threat models (e.g., Lp-bounded perturbations).

NEW QUESTION # 288
When preparing for an AI incident, which of the following should be done FIRST?

A. Create containment and eradication procedures for AI-related incidents
B. Establish a cross-functional incident response team with AI knowledge
C. Establish recovery processes for AI system models and datasets
D. Implement a clear communication channel to report AI incidents

Answer: B

Explanation:
AAISM prescribes Preparation as the foundational phase of AI incident response. The first priority is to form and empower a cross-functional incident response (IR) team with AI/ML expertise (security, data science, product, legal/compliance). Only once the accountable team exists can you define playbooks, communications, containment/eradication steps, recovery processes, and escalation paths. Without a designated team, procedures and channels lack ownership and effectiveness.
References:* AI Security Management (AAISM) Body of Knowledge: Incident Management-Preparation; Roles & Responsibilities; Cross-functional Coordination* AAISM Study Guide: AI IR Operating Model; Stakeholder Mapping; Authority & Escalation* AAISM Mapping to Standards: Security Operations- Preparation Before Procedures (people and roles precede playbooks)

NEW QUESTION # 289
Which of the following is the PRIMARY purpose of a dedicated AI management system policy?

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

Answer: C

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.

NEW QUESTION # 290
Which of the following is the BEST way to ensure an organization remains compliant with industry regulations when decommissioning an AI system used to record patient data?

A. Update governance policies based on lessons learned and ensure a feedback loop exists
B. Perform a post-destruction risk assessment to verify that there is no residual exposure of data
C. Ensure backups are tested and access controls are recorded and audited to ensure compliance
D. Ensure the certificate of destruction is received and archived in line with data retention policies

Answer: D

Explanation:
For regulated data such as patient information, AAISM requires provable data lifecycle closure at decommissioning. The authoritative evidence is a certificate of destruction (covering primary, replicas, backups, and caches) retained per the organization's records retention policy. While testing backups and auditing access (A), updating policies (B), and doing post-destruction risk assessment (C) are valuable practices, documented destruction attestation is the primary compliance proof point that the data was disposed of in accordance with regulatory and contractual obligations.
References: AI Security Management (AAISM) Body of Knowledge - Data Lifecycle Governance; Decommissioning & Secure Disposal; Records Retention and Evidence of Destruction.

NEW QUESTION # 291
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