Securing_Generative_AI_in_the_Enteprise.pdf

https://opaque.co/wp-content/uploads/2024/02/Securing_Generative_AI_in_the_Enteprise.pdf

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Confidential computing is an emerging technology that protects data during its use, extending protection beyond data at rest and in transit. It is crucial for organizations handling sensitive data and is being accelerated by the Confidential Computing Consortium. Confidential computing can be implemented in different environments and is vital for data privacy, security, and regulatory compliance. It uses hardware-based trusted execution environments (TEEs) to secure training and operation of models on sensitive data. Opaque's trusted AI solutions enable secure machine learning on encrypted data within TEEs. It also provides a gateway service that acts as a security layer between enterprises and external large language models (LLMs), ensuring data confidentiality.

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As generative AI, particularly LLMs, continues to disrupt industries and alter the way organizations work with data, it’s also introducing data privacy challenges that are crucial to address. Between security breaches and evolving privacy regulations, enterprises must adopt strategies to protect sensitive data if they want to maximize the benefits of LLMs.

Introduction

Artificial intelligence (AI) has come a long way over the past decade, transforming the way we interact with technology. Machine learning (ML) has been radically changing the way data can be processed and analyzed, enabling businesses across various industries to make data-driven decisions. Now, in addition to generating predictions and uncovering meaningful insights from existing data, AI systems can generate new, original content in human-like ways.

The advent of generative AI (GenAI) has captured the attention of businesses and industry leaders, scientific communities and tech enthusiasts, as well as the public in recent years. Large language models (LLMs), most notably OpenAI’s ChatGPT, have emerged as one of the most widely used applications of generative AI. Because they are extensively trained on large datasets, LLMs have broad applicability across industries, from retail and marketing to finance and healthcare.

According to the Cisco 2024 Data Privacy Benchmark Study1:

A significant majority of organizations, nearly four out of five, report getting considerable value from their use of GenAI.

An overwhelming 92% of respondents perceive GenAI as a distinct innovation that necessitates new methods for handling data and associated risks.

Nearly half of the organizations that were surveyed are incorporating non- public company details into their GenAI applications.

More than two-thirds express concern that GenAI might negatively impact their company’s legal standing and the protection of intellectual property.

Securing Generative AI in the Enterprise

Value versus risk

Nearly 80% of organizations report considerable value from GenAI use,

yet there’s a high level of concern