Which of the following recommendations would BEST help a service provider mitigate the risk of lawsuits arising from generative AI’s access to and…

The most significant legal risk associated with generative AI using internet data is intellectual property (IP) and copyright infringement. Activating filtering logic to exclude data with IP flags is a direct, preventative technical control. This measure proactively reduces the likelihood of the AI model ingesting, training on, or reproducing protected content without authorization. By addressing the risk at the data ingestion stage, the service provider directly mitigates the root cause of potential infringement lawsuits, making it the most effective recommendation among the choices.

B. Disclosing policies is a transparency measure. While important for compliance, it does not prevent the underlying act of IP infringement from occurring.

C. Appointing a data steward is a governance control. It is an indirect measure; the specific technical controls the steward implements, such as filtering, provide the direct mitigation.

D. Reviewing logs is a detective control performed after data has already been collected. It does not prevent the initial collection of infringing data.

1. ISACA

Artificial Intelligence Audit and Security Management (AAISM) Study Guide

1st Edition. Domain 2: AI Data and Model Management. This domain emphasizes the criticality of data sourcing

provenance

and quality for AI systems. It outlines that organizations must implement controls to ensure training data complies with legal and regulatory requirements

including copyright and IP laws. Filtering data sources is a primary technical control to achieve this.

2. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0). Section 4.3

"Govern" Function. The framework highlights the need for policies and controls to manage risks associated with AI training data. It states

"Data-related controls can include checking for and filtering out copyrighted material

personally identifiable information (PII)

or biased content from training datasets to mitigate legal and ethical risks." This directly supports filtering as a key risk mitigation strategy.

3. Liang

W.

et al. (2023). Holistic Evaluation of Language Models. Stanford University

Center for Research on Foundation Models (CRFM). This and similar research from Stanford's HAI (Human-Centered AI) institute frequently discuss the challenges of "data contamination

" which includes copyrighted and other problematic data from web scrapes. The research underscores the importance of data curation and filtering techniques to improve model safety

legality

and alignment. (See discussions on data curation in sections related to model training and evaluation).