Generative AI has brought about data risks in the tech industry, prompting regulators and governments to impose stricter requirements on organizations. To navigate this landscape effectively, companies should update their data management practices and enhance large language models with enterprise/non-public data. This involves improving data protection capabilities, enhancing controls and oversight, and preparing data for AI generation.
Data platforms must implement encryption, anonymization, and tokenization, along with automated classification of data using machine learning. Companies should also have comprehensive audit trails to track data usage and interactions with third parties. Additionally, new disciplines are required to ensure data quality and relevance for training AI models.
IBM offers the Generation AI Data Ingestion Factory, a managed service to address data challenges in AI and maximize the potential of enterprise data. This service includes scalable data ingestion, regulatory compliance, and data privacy management capabilities. It is platform agnostic and customizable, allowing organizations to reduce integration time, comply with data regulations, mitigate risks, and achieve consistent AI results.
To combat data risks effectively, cross-functional expertise is crucial. IBM Consulting team, comprising former regulators, industry leaders, and technology experts, can provide consulting services and solutions tailored to address data risks in AI governance. Contact gsbaird@us.ibm.com for more information on how AI governance can help mitigate data risks.
Article Source
https://www.ibm.com/blog/re-evaluating-data-management-in-the-generative-ai-age/
