Reaping the Most Value from Private AI

The exponential growth of Artificial intelligence (AI) is familiarizing business users across multiple industries with large language models (LLMs) and generative AI tools like ChatGPT.  These are typically public AI systems that rely on algorithms trained on a variety of data sets that include external sources gathered from the broader online world. Unfortunately, public AI can be an unintentional two-way street where private organizational data leaks out into the open, which is why many organizations are learning to embrace more isolated private AI deployments.

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Private AI provides unique benefits for firms looking to leverage AI and machine learning (ML) while keeping their data and insights safe from external threats. But success does not come automatically. This article explores the need to properly configure private AI to remain highly interoperable with public AI systems, while also bringing AI/ML modeling in-house and into the hands of business users without overwhelming them with complexity.

Why Private AI is Needed

Generative AI offers organizations greater insights and value than ever.  Hence the breakout success of ChatGPT, the most well-known generative AI application that set records for the fastest-growing user base in history. However, there is a risk that these insights can easily reach the outside world thanks to an AI ecosystem that heavily depends on external data and connections.

Look no further than the choice Samsung made to ban ChatGPT after an employee inadvertently shared sensitive information with the model, which contributed to a data leak. Such incidents underscore the importance of safeguarding confidential data in the age of AI. In response, many organizations are now turning to private AI deployments to have greater control over their data and AI models. Private AI involves ML algorithms that are trained on data unique to a single user or organization, with AI modeling occurring solely on that data.

Importantly, private AI outputs are not shared outside the organization. This helps ensure that other companies, even direct competitors, cannot access valuable models within an AI ecosystem that heavily depends on third-party cloud services and SaaS vendors. But even when we come to appreciate the advantages of private AI, we soon learn that not all private AI is the same and that standing up a secure and efficient private AI environment is not a simple matter of buying a platform and flipping a switch.

Not All Private AI is the Same

There are several important factors that IT teams should consider when implementing private AI within their organizations. To begin with, there is the shared responsibility model to consider: even if a private AI system functions flawlessly from the vendor’s side, any misconfigurations while integrating or managing the system can create new performance or security issues. Operational bottlenecks can also arise from multiple vendors that are misaligned, or from technology partners who lack a clear understanding of the business ecosystem.

Once these pitfalls are addressed, private AI can have a transformative effect on key enterprise operations. Private AI is especially beneficial in specific industries like healthcare and finance, where data sensitivity and regulatory requirements are paramount. For instance, a large bank might establish a private AI platform to securely analyze financial transactions while ensuring that the sensitive customer data used in this process remains protected and confidential.

Specific use cases for private AI include document classification, where trained AI models identify and categorize documents while optimizing their routing; and document extraction, which automates information extraction from documents. Other use cases include secure environments for AI assistant functions like document summarization and auto-generation of text. Some private AI platforms can fully automate email communications to efficiently handle customer inquiries on a large scale. In each of these cases, private AI eliminates manual processes without eliminating protections for sensitive or confidential data and information.

Blending Public and Private AI

For all the security and confidentiality private AI brings to enterprise data and model development, the protected nature of these processes does not mean private AI should be closed off from the rest of the world. There are plenty of cases where it is appropriate and even necessary for private AI to interact with more traditional public AI and data. In such cases, success comes from harmonizing public and private AI systems with essential standards and configuration parameters for secure interoperability.

Consider the example of a case management system for acquisitions. Especially if this is happening within the government, changes to the Federal Acquisition Regulations System may necessitate updates in how materials are procured, directly affecting case management processes. The integration of public AI allows outside regulatory information to inform how a private AI system manages internal processes. Proper orchestration of the two ensures that private data management is informed by broader rules or business context coming from the outside world.

Another example of private and public AI working together would be a chat-based system used by a major bank to interact with customers. Banks must implement Know Your Customer (KYC) systems, which involve handling a substantial amount of private data. To effectively manage this information, they need to summarize, extract and generate insights in a private AI environment, while still coordinating these efforts with public AI data from the SEC, FINRA and, beginning in January 2025, Europe’s new Digital Operational Resilience Act (DORA).

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Ensuring Accessibility

An organization could theoretically create a private AI platform from scratch, but this would require hiring in-house data engineers, software developers and other specialists to develop and maintain the AI models independently. Fortunately, the HR and upskilling challenges can be avoided with a growing selection of platform options that simplify the process through low-code design, removing the necessity for specialized data science.

Equipped with a platform that democratizes AI development through user-friendly dashboards, modeling templates and related features, a team of business analysts can independently handle all the steps involved in private AI modeling. These include data preparation and formatting for quality and accuracy, as well as feature extraction and subsequent model training and tuning to identify and refine valuable patterns or relationships within the data.

With many aspects of AI modeling simplified and automated, organizations can swiftly implement private AI for critical functions and processes, potentially transforming business operations in just a few weeks. The caveat is that this usability layer must be backed up with a solid layer of data governance to define and manage authentication rules, asset dependencies and compliance considerations for the data. This task becomes more manageable when organizations implement a unified data architecture, such as data fabric, which connects and visualizes all data in a comprehensive view across the entire organization.

Conclusion

Private AI offers distinct advantages for organizations seeking to harness the power of AI/ML modeling while ensuring that their data and insights remain secure from external threats. The most effective strategies combine both public and private AI. And by utilizing low-code design, these powerful capabilities can be made readily available to business users, leading to enhanced cost savings, process efficiency and informed decision-making.