Data poisoningoccurs when attackers manipulate training data to corrupt model behavior. The most direct mitigation is to implementrobust data validation and integrity checks(option C), including anomaly detection on input distributions, provenance controls, verification of data sources, and safeguards for pipelines feeding the training set. AAIA highlightsthreats and vulnerabilities specific to AIand the importance of controls that protect data integrity in AI Operations.
Option A (relying on third-party providers) does not inherently eliminate poisoning risk; providers themselves may be vulnerable. Option B (increasing data size) can dilute but not reliably remove malicious samples.
Option D (simpler algorithms) might help interpretability but does not directly prevent poisoned data from influencing the model. The most effective, aligned control isrigorous data validationto ensure only trustworthy data enters the training process.
References:
ISACA,AAIA Exam Content Outline- Domain 2: Threats and Vulnerabilities Specific to AI (controls for AI- related threats).
ISACA risk guidance referencing data integrity and poisoning risks in AI pipelines.

LIME (Local Interpretable Model-Agnostic Explanations) is a leading tool for explaining individual AI predictions by approximating the behavior of complex models with simple, interpretable ones in localized regions. The AAIA™ Study Guide highlights LIME as highly effective for providing transparency and interpretability to non-technical stakeholders.
"LIME enables auditors to demonstrate how specific input features influenced an AI decision, facilitating trust and stakeholder understanding-especially in regulated or high-impact contexts." Options A, B, and C are technical modeling techniques but do not prioritize stakeholder-friendly explanation.
Therefore, D is best for transparency.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI in Audit Processes," Subsection: "Explainability Tools and Stakeholder Communication"

The AAIA™ Study Guide advises that if an AI model presents a risk that exceeds the organization's predefined risk tolerance-especially in cases of ethical harm or bias-deployment should be delayed until proper safeguards are in place. This approach prevents legal exposure and preserves stakeholder trust.
"When AI risks exceed acceptable thresholds, organizations must suspend implementation until corrective action reduces the risk to within tolerance levels. Proceeding without mitigation violates sound governance principles." While improving data (B) may help, it does not address the immediate governance concern. Risk tolerance (D) should not be adjusted to fit flawed systems. Thus, A is the correct course.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Governance and Risk Management," Subsection: "Risk Evaluation and Implementation Decision-Making"

AI-based coding assistants can inadvertently generate insecure code patterns, reuse vulnerable libraries, or bypass secure coding practices.
AAIA highlights security vulnerabilities as the primary risk because insecure code can lead to:
* Data breaches
* Injection attacks
* Authentication bypasses
* Supply chain compromise
* Privilege escalation
Excessive reliance (A) affects productivity but not security.
Human bias (B) is possible but not as severe as security flaws.
Training complexity (D) is not a risk to AI system integrity.
Therefore, the introduction of security vulnerabilities is the greatest risk.
References:
AAIA Domain 2: AI Risks in Software Development
AAIA Domain 5: Security and Ethical Development Practices

Neural networks are designed to mimic the way the human brain processes information, enabling AI systems to identify complex patterns and make decisions based on data inputs. The AAIA™ Study Guide defines neural networks as central to deep learning architectures that emulate cognitive functions.
"Neural networks simulate the interconnectivity and learning processes of the human brain. This capability enables AI systems to handle unstructured data and perform tasks such as image recognition, natural language processing, and predictive analytics." Options A, B, and D reflect ancillary benefits but not the core design purpose of neural networks. Thus, C is the most accurate.
Reference: ISACA Advanced in AI Audit™ (AAIA™) Study Guide, Section: "AI Fundamentals and Technologies," Subsection: "Neural Networks and Deep Learning"