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Data privacy and security in  GenAI-enabled services

The rapid adoption of GenAI (generative artificial intelligence) for enterprise business scenarios has ushered in a new era of unprecedented creativity, utility and productivity.

More specifically, the proliferation of large language models (LLMs) like Copilot (ChatGPT), Lambda, and Falcon 40B, has led to organizations seeking to quickly train and deploy GenAI-powered applications  and services for their businesses across a variety of industries, reshaping digital transformation efforts as we know them. However, to make the most meaningful use of GenAI, organizations must continuously (and securely) integrate large data sets into their machine learning (ML) models; after all, your outputs are only as good as the data used for training your services.

While GenAI demonstrates promises, security and privacy risks associated with ingesting and exposing sensitive data such as personal identifiable information (PII), protected health information (PHI) data has now become clear.

The real-world risks of not securing PII before it is ingested, or not having a well-tested model, can include: unintentional data loss, sensitive IP exposure, and even potential infringement on regional data privacy regulations.