Ryan O’Leary, Esq., Research Director | IDC | Report | Generative AI
This IDC report examines the challenges and opportunities of harnessing data for GenAI and LLMs. As generative AI continues to develop, organizations will need to find ways to safely collect and process data. However, the complexities and hurdles of dynamic consent management can hinder data utilization. The report highlights performant privacy technologies that mitigate data security and privacy concerns, enabling safe data processing without the associated risks.
Key Highlights:
Security and privacy are the two biggest roadblocks to GenAI for enterprises. Risk associated with leveraging sensitive data is untenable. GenAI’s impact will be curtailed if there is no data available to train the models.
Using Anonos Variant Twins, a data controller can create privacy-secured data sets from which it is impossible to re-identify individuals. Variant Twins essentially remove risk from data and allow it to be used safely.
61% of organizations are at least experimenting with generative AI, according to IDC's Future Enterprise Resiliency and Spending Survey, Wave 2.
Performant privacy-based controls reconcile tensions between ensuring high data utility and effectively protecting data when in use between and outside of perimeters. Performant privacy can protect against prompt leakage of proprietary context or data and can safely train domain specific LLMs.
