Which of the following testing techniques would BEST validate whether an organization's data governance program effectively ensures data quality and…

Assessing data lineage is the most direct and effective technique to validate a data governance program's control over data quality and integrity for AI. Data lineage provides a verifiable audit trail of data from its origin through all transformations and processing steps until it is used for model training and deployment. By examining this trail, an auditor can confirm the provenance of the data, identify any unauthorized or improper modifications, and ensure that the data adheres to established quality standards. This directly tests the operational effectiveness of the governance framework in maintaining trustworthy data for AI systems.

A: A business impact analysis (BIA) evaluates the potential consequences of an AI model's failure on business operations, rather than validating the data governance controls that ensure data quality.

B: Reviewing documentation confirms the existence of policies and procedures but does not validate their effective implementation or the actual state of data quality and integrity in practice.

C: A penetration test is a security assessment focused on identifying and exploiting vulnerabilities in the AI system, not on evaluating the internal data governance processes for quality and integrity.

1. ISACA

Auditing Artificial Intelligence

2023: In the section on "Data Governance

" the guide states

"Data lineage and provenance should be established to trace data back to its source

providing transparency and accountability for the data used in AI systems." (p. 21). This highlights lineage as a core mechanism for validating data governance.

2. National Institute of Standards and Technology (NIST)

AI Risk Management Framework (AI RMF 1.0)

January 2023: The framework's "Govern" function emphasizes the need for data governance. Under section 4.3.1 (Data)

it notes

"Data sourcing

quality

and any pre-processing or labeling steps should be documented to enable traceability and reproducibility

" which is the essence of assessing data lineage.

3. Ko

H.

Lee

S.

& Lee

K. (2022). A Study on the Data Governance Framework for Artificial Intelligence. Journal of Theoretical and Applied Information Technology

100(15)

5099-5111: This academic paper discusses that "Data lineage is essential for tracking the origins and transformations of data

which is a cornerstone for ensuring data quality and integrity within an AI data governance framework." (Section 3.2).