Isaca AAISM Exam Dumps

The AAISM guidance defines risk minimization for AI deployment as requiring a formalized AI model life cycle policy and associated procedures. This ensures oversight from design to deployment, covering data handling, bias testing, monitoring, retraining, decommissioning, and acceptable use. Limiting usage to developer-defined scenarios or relying on vendor mechanisms transfers responsibility away from the organization and fails to meet governance expectations. Training and awareness support cultural alignment but cannot substitute for structured lifecycle controls. Therefore, the establishment of a documented lifecycle policy and procedures is the most comprehensive way to minimize operational, compliance, and ethical risks in integrating foundation models.

AAISM Study Guide -- AI Governance and Program Management (Model Lifecycle Governance)

ISACA AI Security Guidance -- Policies and Lifecycle Management

AAISM states that human oversight is the strongest control for hallucination risks, especially in high-impact decisions. Humans validate outputs, correct errors, and override unsafe model responses.

Automated validation (C) helps but cannot fully detect hallucinations. Chunking (B) improves information handling but not hallucination mitigation. Content enrichment (D) does not reduce hallucinations.

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AAISM identifies AI-specific threat modeling as an essential early-stage control in security-by-design, particularly for high-risk systems like credit scoring. It systematically identifies:

* data poisoning

* bias vulnerabilities

* model evasion

* model extraction

* misuse scenarios

Differential privacy (A) is powerful but is a mitigation, not the overarching design control. Segmentation (C) and IP allow lists (D) are supporting controls but not the foundational step in secure design.

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In AI governance frameworks, credit scoring is treated as a high-risk application. For such systems, the highest-priority safeguard is human oversight to ensure fairness, accountability, and prevention of bias in automated decisions.

The AI Security Management (AAISM) domain of AI Governance and Program Management emphasizes that high-impact AI systems require explicit governance structures and human accountability. Human-in-the-loop design ensures that final decisions remain the responsibility of human experts rather than being fully automated. This is particularly critical in financial contexts, where biased outputs can affect individuals' access to credit and create compliance risks.

Official ISACA AI governance guidance specifies:

High-risk AI systems must comply with strict requirements, including human oversight, transparency, and fairness.

The purpose of human oversight is to reduce risks to fundamental rights by ensuring humans can intervene or override an automated decision.

Bias controls are strengthened by requiring human review processes that can analyze outputs and prevent unfair discrimination.

Why other options are not the highest priority:

A . Regular updates improve accuracy but do not guarantee fairness or ethical decision-making. Model drift can introduce new bias if not governed properly.

B . Appeals mechanisms are important for accountability, but they operate after harm has occurred. Governance frameworks emphasize prevention through human oversight in the decision loop.

D . Restricting criteria to ''objective metrics'' is insufficient, as even objective data can contain hidden proxies for protected attributes. Bias mitigation requires monitoring, testing, and human oversight, not only feature restriction.

AAISM Domain Alignment:

Domain 1 -- AI Governance and Program Management: Ensures accountability, ethical oversight, and governance structures.

Domain 2 -- AI Risk Management: Identifies and mitigates risks such as bias, discrimination, and lack of transparency.

Domain 3 -- AI Technologies and Controls: Provides the technical enablers for implementing oversight mechanisms and bias detection tools.

Reference from AAISM and ISACA materials:

AAISM Exam Content Outline -- Domain 1: AI Governance and Program Management (roles, responsibilities, oversight).

ISACA AI Governance Guidance (human oversight as mandatory in high-risk AI applications).

Bias and Fairness Controls in AI (human review and intervention as a primary safeguard).

The AAISM standard explicitly states that traditional penetration tests alone are insufficient for AI systems. Effective AI security testing requires:

* AI-specific threat modeling (e.g., data poisoning, prompt injection, model theft)

* Adversarial attack simulations (white-box, black-box, gradient-based attacks)

* Evaluation of robustness and manipulation resistance

Option B captures these requirements precisely.

Options A, C, and D do not address AI-specific attack vectors.

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