Artificial intelligence (AI) has gained significant traction in recent years, with large language models (LLMs) demonstrating their potential to transform various enterprise processes. However, as concerns over data safety and AI models grow among consumers and regulators, the adoption of AI on a wider scale calls for robust AI governance practices that prioritize data governance throughout the data lifecycle. Understanding the importance of data governance in AI is essential for instilling confidence in consumers, enterprises, and regulators.

The Risks of Training LLM Models on Sensitive Data

Large language models like ChatGPT and Google Bard can be trained on proprietary data to meet specific enterprise needs. Companies may deploy private models to assist sales teams, customer service, HR, marketing, and even healthcare providers. However, training LLMs on sensitive proprietary data poses several risks:

1. Privacy and re-identification risk:

The use of private or sensitive data in AI model training can potentially lead to the identification of specific individuals, threatening data privacy.

2. In-model learning data:

LLMs continue learning and adapting from the context of conversations, which increases the complexity of governing model input data. Additional precautions are needed to prevent sensitive information shared during conversations from being used in other contexts.

3. Security and access risk:

Controlling access to data is critical to ensuring the security of AI models. However, current AI deployment security measures are still evolving, and the sensitivity of the model’s output cannot be fully controlled based on the user’s role.

4. Intellectual Property risk:

Training models on intellectual property, such as songs or copyrighted work, may raise issues of infringement and require careful monitoring to avoid legal complications.

5. Consent and DSAR risk:

Data privacy regulations emphasize obtaining consent from customers for the use of their data and the ability to request data deletion. However, training AI models on sensitive customer data creates a potential exposure source if customers revoke their data usage consent.

Data Governance for LLMs

Data governance plays a crucial role in the architecture of LLMs. IBM’s data governance solutions, powered by IBM Knowledge Catalog, offer various capabilities to facilitate data discovery, automated data quality, and data protection. The implementation of Privacy Enhancing Techniques to remove sensitive data before feeding it to AI is also essential for ensuring privacy and auditability.

Building a Governed Foundation for Generative AI with IBM Watsonx and Data Fabric

IBM’s Watsonx provides an enterprise-ready studio for AI builders to leverage generative AI capabilities powered by foundation models. IBM Watsonx includes Watsonx.data, a data store built on an open lakehouse architecture, supported by querying, governance, and open data formats for accessing and sharing data across hybrid cloud environments. IBM’s data fabric solutions offer data integration, data governance, and other capabilities to build a robust data infrastructure for successful AI implementations.

Get Started with Data Governance for Enterprise AI

As AI models, particularly LLMs, continue to reshape industries, managing and governing AI models alone is not enough. Effective data governance before inputting data into AI models is crucial. To learn more about how IBM data fabric can support your AI journey, book a consultation or start a free trial with IBM Watsonx.ai.

FAQ

Why is data governance important for enterprise AI?

Data governance is essential for enterprise AI because it helps ensure the safety, privacy, and legal compliance of data used to train AI models. Effective data governance mitigates risks such as privacy breaches, intellectual property infringement, and non-compliance with data privacy regulations.

What are the risks of training LLM models on sensitive data?

The risks of training LLM models on sensitive data include privacy and re-identification risks, in-model learning data concerns, security and access risks, intellectual property risks, and consent and data subject access request (DSAR) risks.

How can data governance be implemented for LLMs?

Data governance for LLMs involves implementing robust practices for data discovery, data quality, and data protection. This includes identifying and removing sensitive components from the data, maintaining referential integrity, and keeping an audit trail of data usage to ensure compliance and auditability.

What is IBM Watsonx?

IBM Watsonx is an enterprise-ready studio that combines traditional machine learning (ML) with generative AI capabilities. It includes Watsonx.data, a data store built on an open lakehouse architecture, enabling AI builders to access and share data across hybrid cloud environments.