Generative Artificial Intelligence (AI) is no longer a distant dream; it’s here, and it’s transforming the way we work. From querying bank bills to composing poems and university essays, generative AI has already embedded itself in various aspects of our lives. But its most profound impact lies within the cloud computing ecosystem. As businesses rush to harness this groundbreaking technology, understanding network connectivity requirements and mitigating risks become paramount.

Data Processing: A Crucial Connectivity Requirement

Large language models (LLMs), the backbone of generative AI, are massive. Training these models demands vast datasets and hyper-fast computing power. The larger the dataset, the more computing resources are needed. However, data accessibility isn’t the only concern. Sovereignty, security, and privacy must also be managed when data transits through public clouds. In 2022, 39% of businesses experienced data breaches in their cloud environments. To address this, explore private connectivity solutions designed for high-performance and AI workloads.

Navigating Regulatory Complexity

The regulatory landscape around AI is intricate and ever-evolving. Consider a multinational bank in New York with 50 mainframes housing critical data. They want to perform AI analysis on this data, but using the public internet for cloud connectivity is restricted due to regulatory constraints. Private connectivity offers a solution. By adhering to privacy-by-design principles, companies can implement techniques like data mapping and loss prevention to safeguard personal data. The General Data Protection Regulation (GDPR) in Europe and GDPR-inspired data privacy laws in the United States underscore the importance of such measures.

Conclusion

Generative AI holds immense potential, but deploying it safely and securely requires meticulous planning. As we embrace this revolution, let’s ensure our cloud networks are ready to support the transformative power of generative AI.