100% Pass Quiz 2026 ISACA AAISM: Professional ISACA Advanced in AI Security Management (AAISM) Exam Valid Exam Pattern

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ISACA AAISM Exam Overview:

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

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ISACA AAISM Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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 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.

ISACA Advanced in AI Security Management (AAISM) Exam Sample Questions (Q428-Q433):

NEW QUESTION # 428
When integrating AI for innovation, which of the following can BEST help an organization manage security risk?

A. Adopting a phased approach
B. Evaluating compliance requirements
C. Re-evaluating the risk appetite
D. Seeking third-party advice

Answer: A

Explanation:
AAISM emphasizes that when introducing innovative AI systems, organizations reduce security and compliance risk by following a phased adoption approach. This allows incremental deployment, controlled testing, and gradual scaling while monitoring risks in real time. Re- evaluating risk appetite and evaluating compliance are important governance steps but do not directly mitigate risks during implementation. Seeking third-party advice can add expertise but does not provide the structured control that phased integration offers. The most effective risk management approach for AI innovation is to adopt a phased rollout strategy.

NEW QUESTION # 429
Implementing which of the following would MOST effectively address bias in generative AI models?

A. Fairness constraints
B. Adversarial training
C. Data minimization
D. Data augmentation

Answer: A

Explanation:
AAISM identifies fairness constraints (e.g., constrained optimization, debiasing objectives, conditional generation controls, and post-processing calibrations) as the most direct, measurable method to mitigate disparate outcomes in generative systems. While data augmentation can help with coverage, and adversarial training improves robustness, fairness constraints explicitly target distributional fairness and outcome equity in generated content, aligning with governance and compliance goals.
References: AI Security Management™ (AAISM) Body of Knowledge - Fairness & Bias Management in Generative AI; Metrics, Constraints, and Remediation. AAISM Study Guide - Fairness Objectives, Post-hoc Debiasing, and Evaluation Protocols.

NEW QUESTION # 430
What BEST ensures a proper business continuity plan (BCP) for an AI solution?

A. Implementing access controls
B. Testing AI infrastructure failover mechanisms
C. Enhancing monitoring for model failure
D. Increasing backup restoration detail

Answer: B

Explanation:
AAISM defines an AI BCP as requiring validated failover and recovery testing of AI components, including:
* model hosting environments
* model-serving APIs
* feature stores
* inference pipelines
Only Option B tests actual continuity of AI functionality.
Monitoring (A) detects issues but does not ensure continuity. Access controls (C) relate to security, not continuity. Backup detail (D) is insufficient without recovery testing.
References: AAISM Study Guide - AI Resilience and Disaster Recovery Validation.

NEW QUESTION # 431
Which of the following security framework elements BEST helps to safeguard the integrity of outputs generated by AI algorithms?

A. Risk exposure due to bias in AI outputs is kept within an acceptable range
B. Management is prepared to disclose AI system architecture to stakeholders
C. Ethical standards are incorporated into security awareness programs
D. Responsibility is defined for legal actions related to AI regulatory requirements

Answer: A

Explanation:
According to AAISM technical controls, the element of security frameworks that directly safeguards output integrity is ensuring that bias-related risks are maintained within acceptable ranges. This ensures that AI results remain consistent, fair, and reliable, preserving trust in outputs. Ethical awareness, architectural disclosure, and legal responsibilities are governance practices but do not directly secure output integrity. Output integrity is primarily protected through bias management and ongoing evaluation of fairness and accuracy.

NEW QUESTION # 432
Which of the following technologies can be used to manage deepfake risk?

A. Adaptive authentication
B. Multi-factor authentication (MFA)
C. Systematic data tagging
D. Blockchain

Answer: D

Explanation:
The AAISM study material highlights blockchain as a control mechanism for managing deepfake risk because it provides immutable verification of digital media provenance. By anchoring original data signatures on a blockchain, organizations can verify authenticity and detect tampered or synthetic content. Data tagging helps organize but does not guarantee authenticity. MFA and adaptive authentication strengthen identity security but do not address content manipulation risks.
Blockchain's immutability and traceability make it the recognized technology for mitigating deepfake challenges.

NEW QUESTION # 433
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