Published on October 16, 2023, 9:03 pm

TL;DR: Generative AI, such as OpenAI's ChatGPT, offers exciting opportunities for businesses but also comes with risks. Snowflake's Language Model Management (LMM) infrastructure provides a solution to address security concerns and comply with data sovereignty regulations. Organizations should have a clear data strategy in place to leverage generative AI effectively while mitigating potential pitfalls. Balancing innovation with risk management is crucial for leaders navigating the evolving landscape of generative AI adoption.

Artificial intelligence (AI) has made its mark in popular culture through movies like “Bicentennial Man,” “Upgrade,” and “Artificial Intelligence” by Steven Spielberg. Today, AI is experiencing a resurgence in the business world with the introduction of OpenAI’s Chat Generative Pre-trainer Transformer, also known as ChatGPT. This large language model-based chatbot is generating excitement in boardrooms, conferences, and various other settings.

According to a 2023 survey conducted by Oxford Economics and the IBM Institute of Business Value (IBV), 64% of CEOs feel significant pressure from investors, creditors, and lenders to adopt Generative AI (GenAI). Compared to the understanding of AI in 2016, executives now have a clearer view of where to deploy GenAI and which use cases will drive the most value.

Naturally, along with opportunities, there are risks associated with emerging technologies like generative AI. Sanjay Deshmukh, senior regional vice president for ASEAN and India at Snowflake, highlights the importance of understanding AI and data strategies. Educating enterprises about business outcomes is crucial for leveraging GenAI effectively.

When it comes to the benefits and drawbacks of Generative AI, Deshmukh believes there are no negatives to the technology itself. He emphasizes that it is a powerful innovation capable of disrupting multiple industries while offering significant business value. However, models like ChatGPT are trained using external data from the public domain and lack a true understanding of specific businesses or data fraud.

To mitigate potential risks associated with generative AI technology, organizations can utilize Snowflake’s solution for their Language Model Management (LMM) infrastructure. This approach allows data to stay within an organization’s control, reducing concerns about unauthorized access or misuse. Building this infrastructure requires substantial computer and storage resources but offers scalability for organizations.

Addressing data sovereignty issues common in regulated markets in Asia, Snowflake ensures the security of data within its environment, regardless of its physical location. Organizations can encrypt their data and adopt additional security measures like masking or tokenizing to meet regulatory requirements and provide confidence to regulators.

For leaders overseeing GenAI adoption, Deshmukh emphasizes the importance of having a data strategy in place. By consolidating data into a unified platform, companies can leverage it to power AI-driven applications and enhance user productivity. Building a security framework for classifying sensitive data and democratizing access to non-sensitive data enables better decision-making throughout the organization.

In conclusion, Generative AI presents exciting opportunities for businesses but also poses risks that need careful consideration. With the right understanding of data strategies, organizations can fully leverage GenAI while mitigating potential pitfalls. Snowflake’s LMM infrastructure offers a solution to address security concerns and comply with data sovereignty regulations. Balancing innovation with risk management is crucial for leaders as they navigate the evolving landscape of Generative AI adoption.

To learn more about securing Generative AI for enterprises, you can listen to Sanjay Deshmukh’s insights in the FutureCISO PodChats episode available on FutureCIO’s website.


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