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ISACA AAISM Exam Overview:

Certification Vendor:	ISACA
Exam Name:	ISACA Advanced in AI Security Management (AAISM) Exam
Exam Number:	AAISM
Available Languages:	English
Exam Format:	Scenario-based, Multiple-choice
Certificate Validity Period:	3 years
Exam Price:	US$459 (members), US$599 (non-members)
Related Certifications:	CISM CISSP
Passing Score:	450 (out of 800)
Real Exam Qty:	90
Exam Duration:	150 minutes
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 (Q396-Q401):

NEW QUESTION # 396
Which of the following is the MOST effective control to prevent malicious modification of AI chatbot outputs?

A. Implementing a governance AI model trained on datasets of known attack examples
B. Establishing role-based access control (RBAC) for internal access
C. Conducting mandatory end-user AI training on safe and secure prompting practices
D. Creating a list of disallowed words and phrases in prompts

Answer: A

Explanation:
A governance AI model trained on known attack patterns can dynamically detect and block malicious attempts to manipulate chatbot outputs, including prompt injection and adversarial input techniques. This provides adaptive, real-time protection that is more effective than static filtering or user training alone.

NEW QUESTION # 397
Which of the following features of training data can be used to provide data provenance?

A. Data tags
B. Category labels
C. Unstructured data
D. Watermarks

Answer: A

Explanation:
Data tags provide metadata that identifies the origin, ownership, handling requirements, and history of training data. This supports data provenance by enabling traceability and verification of where the data came from and how it has been managed throughout its lifecycle.

NEW QUESTION # 398
Which of the following BEST ensures AI components are validated as part of disaster recovery testing?

A. Simulating denial of service (DoS) attacks against AI APIs to evaluate detection capabilities
B. Disconnecting primary model training clusters to test retraining workflow during extended outages
C. Running simulated data loss scenarios by erasing test records from the AI system's feature store
D. Monitoring model performance metrics during failover and recovery to assess system stability

Answer: D

Explanation:
Business continuity and disaster recovery (BC/DR) exercises for AI must validate that critical AI components (feature stores, model registries, inference services, pipelines) operate within agreed recovery objectives during failover and restoration. Monitoring and evaluating model performance and stability during DR tests provides objective evidence that AI services remain functional, accurate, and reliable under contingency conditions, thereby validating the AI stack end-to-end.

NEW QUESTION # 399
A large financial services organization is integrating a third-party AI solution into its critical fraud detection system. Which of the following is the BEST way for the organization to reduce risk associated with AI vendor and supply chain dependencies?

A. Focusing on performance testing to ensure the solution meets operational requirements
B. Establishing contractual agreements requiring vendors to provide evidence of secure development practices
C. Implementing isolated virtual environments to validate the integration of the fraud detection system with the solution
D. Conducting annual vulnerability assessments of the fraud detection system after integration

Answer: B

Explanation:
AAISM emphasizes supplier assurance through contractual obligations as the foundational control for AI supply chain risk. Contracts should require verifiable evidence of secure development practices (e.g., secure SDLC, model and data provenance documentation, SBOM/MBOM where applicable, vulnerability disclosure, patch SLAs, audit rights, incident notification, and regulatory compliance assertions). This creates enforceable, continuous assurance beyond point-in-time tests.

NEW QUESTION # 400
Which of the following is the BEST control for preventing deepfakes?

A. AI governance policies
B. Regular AI risk assessment
C. System input validation
D. Output provenance verification

Answer: D

Explanation:
Output provenance verification (e.g., robust watermarking, cryptographic signing, content credentials, and chain-of-custody attestations) is the primary preventive and detective control to combat deepfakes at scale. It enables receivers and downstream systems to verify that media originates from trusted sources and has not been tampered with.

NEW QUESTION # 401
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