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| Section | Objectives |
|---|---|
| AI Risk Management | - Risk identification and assessment for AI systems
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| Regulatory and Compliance Requirements | - Global AI regulatory landscape
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| AI Governance and Strategy | - AI governance frameworks and organizational oversight
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| AI Lifecycle Controls | - Controls across AI development lifecycle
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| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
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NEW QUESTION # 18
Which of the following poses the GREATEST challenge related to the protection of intellectual property generated by AI solutions?
Answer: B
Explanation:
Traditional intellectual property law was designed for human-created works. AI-generated content sits in a legal grey zone because current copyright frameworks in most jurisdictions do not clearly establish who-if anyone-holds copyright in outputs created autonomously by AI systems.
Why C is Correct: According to ISACA AAIR, the lack of regulatory clarity around AI-generated content copyright is the greatest IP challenge because it creates fundamental uncertainty about ownership, transferability, and enforceability of rights in AI outputs. Without clear legal status, organizations cannot confidently assert ownership, license AI-generated materials, or prevent competitors from copying outputs.
This uncertainty pervades commercial agreements, licensing strategies, and competitive protection.
Why A is Wrong: Zero-data retention policies actually protect intellectual property by ensuring vendor systems do not retain proprietary input data. This represents a protective measure, not a challenge.
Why B is Wrong: Training material customization for confidential data handling is a workforce education challenge. While important for data protection, it does not represent the primary IP challenge from AI- generated content.
Why D is Wrong: Low-risk use cases like administrative tasks present minimal IP concerns because the outputs are typically not commercially significant or protectable. The IP challenge is greatest for creative, analytical, and proprietary outputs.
NEW QUESTION # 19
Which of the following is the PRIMARY benefit of implementing a comprehensive data pipeline for AI model training, testing, and validation?
Answer: D
Explanation:
A comprehensive, well-designed data pipeline establishes consistent, documented processes for data collection, preprocessing, transformation, and quality validation across training, testing, and validation stages.
This systematic approach reduces the likelihood of data errors propagating through to the final model.
Why A is Correct: According to ISACA AAIR data pipeline governance guidance, the primary benefit of a comprehensive pipeline is reducing error propagation risk. By applying consistent quality checks, validation gates, and transformation rules throughout the pipeline, errors in raw data are detected and corrected before they influence model training. This prevents data quality failures from compounding into model accuracy and bias problems-producing a higher-quality, more reliable final model.
Why B is Wrong: Governance risk sharing with external providers occurs through contractual arrangements and shared responsibility frameworks, not through data pipeline implementation. Pipeline design is an internal quality management measure.
Why C is Wrong: Automation of early-stage pipeline tasks is an operational efficiency benefit. While valuable, efficiency is a secondary benefit compared to the primary purpose of ensuring data quality and reducing error risk.
Why D is Wrong: Enhanced auditability is an important governance benefit that pipeline documentation provides but is not the primary purpose of pipeline implementation. The primary purpose is quality assurance during model development; auditability is a beneficial side effect.
NEW QUESTION # 20
Which of the following is the GREATEST risk when an organization relies only on adversarial training to protect a private AI model in a testing environment?
Answer: B
Explanation:
Adversarial training improves model robustness against known attack patterns by incorporating adversarial examples into the training process. However, no single security technique provides comprehensive protection-adversarial training addresses only the attack vectors it was designed for, leaving other vulnerabilities unaddressed.
Why B is Correct: The ISACA AAIR security defense-in-depth guidance identifies residual system vulnerabilities as the greatest risk when adversarial training is the sole security measure. Adversarial training protects against specific attack types (evasion, perturbation) but does not address infrastructure vulnerabilities, API security weaknesses, model inversion attacks, membership inference, or other security risks present in a testing environment. A defense-in-depth approach is required for comprehensive protection.
Why A is Wrong: Adversarial training does increase computational requirements and may extend training cycles, but inefficiency is an operational concern rather than a security risk. The security risk of unprotected vulnerabilities significantly outweighs training cycle efficiency.
Why C is Wrong: Overfitting to adversarial training examples is a model quality concern that can be managed through standard regularization techniques. It represents a model performance trade-off, not the greatest security risk from relying solely on adversarial training.
Why D is Wrong: Exposure of proprietary algorithms is an intellectual property risk that is not specifically increased by relying on adversarial training. Algorithm confidentiality is protected through access controls and encryption, which are separate from the adversarial training approach.
NEW QUESTION # 21
A risk practitioner learns that a credit-scoring AI system is exhibiting bias that cannot be eliminated through further training. Which of the following is the risk practitioner's BEST recommendation?
Answer: A
Explanation:
Credit scoring AI systems are subject to anti-discrimination regulations that prohibit using models that produce biased outcomes affecting protected classes. When bias cannot be eliminated through technical means, continuing to operate the system creates ongoing legal violations and harm to affected individuals.
Why B is Correct: According to ISACA AAIR risk treatment guidance and legal compliance obligations, removing a biased credit-scoring system from production is the appropriate response when bias cannot be technically remediated. Continuing to operate a system known to produce discriminatory credit decisions violates anti-discrimination laws (such as the Equal Credit Opportunity Act), exposes the organization to regulatory enforcement, and causes ongoing harm to affected borrowers. Risk avoidance through system withdrawal is the appropriate treatment when the risk cannot be adequately mitigated.
Why A is Wrong: Requesting senior management risk acceptance for confirmed legal violations is inappropriate because organizations cannot accept risks involving known regulatory breaches. Senior management cannot legitimately authorize continued discriminatory lending practices.
Why C is Wrong: Sourcing a replacement system is a necessary future action but takes time to procure, validate, and deploy. In the interim, the biased system should not continue operating. Removing the system from production should precede replacement planning.
Why D is Wrong: Applying compensating controls to generate offsetting biases compounds the discriminatory problem rather than resolving it. Deliberately introducing additional bias-even in the opposite direction-creates an unpredictably biased model that does not produce fair outcomes.
NEW QUESTION # 22
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 # 23
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