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| Section | Weight | Objectives |
|---|---|---|
| AI Life Cycle Risk Management | - AI bias, drift, transparency, and control evaluation - AI model and data risk identification - AI development, deployment, and monitoring risks | |
| AI Risk Program Management | 42% | - Enterprise AI risk program design - AI governance communication and reporting - AI risk assessment and treatment strategies - AI risk monitoring and continuous improvement |
| AI Risk Governance and Framework Integration | 37% | - AI Ownership, Oversight, and Accountability - AI Models, Frameworks, Strategies, and Use Cases - AI Organizational Processes and Alignment |
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NEW QUESTION # 75
A risk practitioner is performing a post-implementation review for an AI system used for credit scoring.
Which of the following is MOST important for the risk practitioner to confirm?
Answer: D
Explanation:
Credit scoring AI systems make high-stakes financial decisions that directly affect individuals' access to credit. Post-implementation review for such systems must confirm that the system performs within ethical, legal, and regulatory boundaries-particularly regarding fairness and explainability.
Why B is Correct: According to ISACA AAIR post-implementation review guidance for high-stakes AI, confirming explainability and fairness is the most critical review element for credit scoring systems. Anti- discrimination laws (Equal Credit Opportunity Act, Fair Housing Act) require that credit decisions be explainable and not discriminatory. Fairness testing detects whether the system produces disparate outcomes across demographic groups, while explainability ensures individual decisions can be justified if challenged.
Why A is Wrong: Access token logging is a security audit trail mechanism. While important for access governance, it does not address the primary regulatory and ethical obligations of a credit scoring system regarding decision quality and fairness.
Why C is Wrong: Stakeholder communication of performance metrics is a governance reporting activity.
Metric communication does not confirm the system is making fair, explainable decisions-it only reports on performance indicators.
Why D is Wrong: User ease of learning and use is a user experience and adoption concern. System usability does not determine whether credit scoring decisions are accurate, fair, or legally compliant-which are the primary post-implementation concerns.
NEW QUESTION # 76
An organization deploys an AI credit scoring model trained on historical financial data that underrepresents certain demographic groups. Which of the following is the risk practitioner's BEST recommendation to mitigate this risk?
Answer: A
Explanation:
Bias in AI models often originates from training data that does not represent the full population the model will serve. Underrepresentation of demographic groups in training data causes the model to perform poorly for those groups, producing discriminatory outcomes in high-stakes decisions like credit scoring.
Why B is Correct: The ISACA AAIR bias and fairness guidance identifies expanding training data coverage as the most effective mitigation for representation bias. Defining specific inclusivity goals ensures the data expansion targets the identified gaps, while broadening data sources introduces representative examples from underrepresented groups. This addresses the root cause-training data deficiency-rather than symptoms.
Why A is Wrong: Model drift reporting detects changes in model behavior over time but does not address existing representational bias embedded in the current model. Monitoring an already-biased model cannot remediate the bias.
Why C is Wrong: Notifying stakeholders of potential inaccuracy is a transparency measure but does not reduce harm to affected individuals. Disclosure of bias without remediation is insufficient under anti- discrimination regulations.
Why D is Wrong: Unsupervised learning can identify hidden patterns but cannot introduce the missing representative data needed to train an unbiased model. Discovering discriminatory patterns in existing data does not resolve the underlying data coverage gap.
NEW QUESTION # 77
An organization depends on multiple external suppliers for AI models and training datasets. Which of the following is MOST important to have in place in order to reduce supply chain risk?
Answer: C
Explanation:
AI supply chain risk arises when external models or datasets are tampered with, have undisclosed characteristics, or cannot be traced to trusted origins. End-to-end provenance and audit trails address these risks by enabling verification of integrity and origin at every stage of the supply chain.
Why A is Correct: According to ISACA AAIR supply chain risk management guidance, verifiable provenance and audit trails are the most important supply chain protection mechanism. Provenance documentation traces the origin, handling, and transformation history of every externally sourced AI artifact- enabling the organization to verify that models and datasets have not been tampered with, that data sources are legitimate, and that the supply chain has not been compromised. Without provenance, organizations cannot distinguish trustworthy from compromised artifacts.
Why B is Wrong: Indemnity clauses assign financial liability after harm occurs. They provide legal recourse but do not prevent supply chain attacks or help the organization verify artifact integrity before deployment.
Why C is Wrong: Training method documentation provides useful technical context but does not verify that the actual artifacts delivered match the documentation. Documentation can be falsified; provenance verification with cryptographic integrity checks cannot.
Why D is Wrong: A vendor risk manager provides governance oversight and relationship management. While important for managing vendor relationships, a single contact point does not substitute for technical provenance verification of every artifact in the supply chain.
NEW QUESTION # 78
An organization embeds AI into existing processes without integrating AI risk practices into enterprise governance. Which of the following should a risk practitioner regard as the GREATEST organizational risk?
Answer: C
Explanation:
When AI is deployed without governance integration, no formal structure exists to assign control ownership, coordinate risk management activities, or align AI decision-making with organizational objectives. This structural void produces divergent, fragmented, and potentially conflicting risk management efforts.
Why C is Correct: According to ISACA AAIR, unclear ownership is the greatest organizational risk from AI operating outside governance structures. Without designated owners, controls may be applied inconsistently across business units, different teams may implement conflicting approaches, and no one is responsible for ensuring AI activities align with enterprise objectives. This governance vacuum creates unmanaged risks and organizational incoherence.
Why A is Wrong: Regulatory compliance documentation gaps are significant but are a downstream symptom of poor governance rather than the root organizational risk. Documentation failures can be remediated more easily than fundamental ownership gaps.
Why B is Wrong: Technical-business alignment is an important concern but represents a strategic planning challenge rather than the greatest organizational risk from absent governance. Alignment can be achieved through business case processes without full governance integration.
Why D is Wrong: Executive approval difficulty is an organizational change management challenge. It reflects organizational politics rather than a structural risk from absent governance. Approval processes function independently of AI governance integration.
NEW QUESTION # 79
Which of the following is the MOST important consideration when selecting controls for AI systems?
Answer: D
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Control selection should start with approved policy, governance, and oversight requirements.
Performance metrics, staff expertise, and integration with existing security processes are important implementation considerations, but controls must first be authorized and aligned with the organization ' s governance framework. This makes option A, Alignment with approved policy and oversight frameworks, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 80
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