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| Section | Objectives |
|---|---|
| Ethics, Privacy, and Responsible AI | - Ethical AI principles and compliance
|
| AI Governance and Strategy | - AI governance frameworks and organizational oversight
|
| Regulatory and Compliance Requirements | - Global AI regulatory landscape
|
| AI Lifecycle Controls | - Controls across AI development lifecycle
|
| AI Risk Management | - Risk identification and assessment for AI systems
|
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NEW QUESTION # 70
Which AI security by design option BEST mitigates targeted model poisoning and supply chain tampering?
Answer: A
Explanation:
Model poisoning attacks target the training data or model parameters to degrade performance or introduce malicious behavior. Supply chain tampering introduces compromised components at vendor or integration stages. Security by design principles require embedding defenses against these threats from the earliest design stages.
Why C is Correct: According to ISACA AAIR security by design guidance, adversarial resilience and data integrity controls address both model poisoning and supply chain tampering at their root. Adversarial resilience training prepares the model to resist maliciously crafted inputs. Data integrity controls- cryptographic signing, provenance tracking, integrity verification-detect tampering in training data and model artifacts across the supply chain. Together, these form the most comprehensive defense against both attack categories.
Why A is Wrong: Data refreshes with checksums detect post-hoc data corruption but do not build adversarial resilience into the model itself. Checksums verify file integrity but cannot prevent poisoning attacks that maintain file integrity while altering data content.
Why B is Wrong: Frequent retraining and bias monitoring address performance drift and fairness but do not specifically protect against deliberate tampering. A retrained model may still be trained on poisoned data if integrity controls are absent.
Why D is Wrong: Data tokenization protects sensitive field values from unauthorized access (a privacy control) but does not address model poisoning or supply chain tampering, which can occur without accessing or exposing the sensitive field values themselves.
NEW QUESTION # 71
A risk practitioner is developing risk scenarios related to successful data poisoning attacks on an AI model used across the organization. Which of the following is the BEST approach to help ensure the scenarios are relevant?
Answer: C
Explanation:
Risk scenario development in AI requires that scenarios be grounded in organizational context, business processes, and actual threat landscapes. Risk scenarios must reflect the specific systems, data flows, and stakeholder concerns relevant to the organization.
Why D is Correct: According to the ISACA AAIR Study Guide, engaging key stakeholders is the cornerstone of effective risk scenario development. Stakeholders bring domain knowledge, business context, and awareness of operational dependencies that technical practitioners may lack. This collaborative approach ensures scenarios address real-world consequences, organizational risk appetite, and business-critical functions-making them actionable and relevant.
Why A is Wrong: Adversarial testing in a sandbox validates controls but does not by itself produce contextually relevant risk scenarios. It is a technical activity, not a scenario development process.
Why B is Wrong: Peer benchmarking provides useful threat intelligence but cannot replace stakeholder engagement. Industry peer data may not reflect the organization's specific AI architecture or risk tolerance.
Why C is Wrong: Data flow diagrams are useful supporting artifacts but describe technical pathways rather than capturing the organizational and business context required for relevant risk scenarios.
NEW QUESTION # 72
A conversational AI accepts unrestricted user text and can be coerced into disclosing secrets or taking harmful actions. Which control MOST effectively reduces this risk?
Answer: B
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. A conversational system exposed to unrestricted text is vulnerable to prompt injection and unsafe output. Runtime input validation and output filtering directly constrain malicious instructions and help prevent disclosure of secrets or harmful actions. This makes option D, Implement runtime input validation and output filtering, 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 # 73
Which of the following is MOST critical to mitigate vendor-related risk when managing AI supply chains?
Answer: B
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. AI supply-chain assurance depends on verifiable end-to-end provenance of models and data so the organization can trace origin, modification, and integrity. Training and indemnity clauses help governance but do not prove that supplied artifacts are trustworthy. This makes option C, Verifiable end-to-end provenance of models and data, 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 # 74
Which of the following BEST mitigates privacy exposure when using datasets for AI model training that include personal information?
Answer: D
Explanation:
Within the ISACA Advanced in AI Risk framework, life-cycle controls should protect data quality, model design, testing, validation, monitoring, change management, and secure retirement of AI systems. De- identification directly reduces privacy exposure by removing or transforming identifying information before personal data is used for training. Authentication and human review protect access or oversight but do not remove personal information from the dataset itself. This makes option C, Implementing robust de- identification controls, 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 # 75
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