As artificial intelligence (AI) changes industries at a dizzyingly rapid pace, industries and governments alike are just beginning to grapple with the implications of the groundbreaking technology. One major issue has come to the foreground: data privacy concerns. Between possible data breaches and companies using your data to train their own models (and perhaps helping your competition in the process), enterprises have concerns. In fact, data privacy has become such a concern that governments in the European Union have issued warnings for companies to tackle data privacy issues before releasing generative AI applications.
As AI plays a central role in process automation and digital transformation efforts, understanding the core principles of private AI will be central to safeguarding your data and your customers’ data. Let’s examine what private AI means and what benefits it delivers.
[ Learn how to operationalize AI without compromising data privacy: Implementing Private AI: A Practical Guide. ]
Private AI refers to methods of building and deploying AI technologies that respect the privacy and control of users’ and organizations’ data. In many ways, it’s a philosophy, but it’s by no means fully shared across AI providers. For example, many vendors use customers’ data to train their own AI models. This follows a “your data is our currency” approach to technology that many organizations have followed. Unfortunately, this approach can cause privacy leaks and, more importantly, help your competition. True private AI will not share your data or use it to fine-tune models in any way.
To truly follow the private AI philosophy, AI must follow at least three core principles.
[ What differentiates private AI from public AI? Read the post, Private AI vs. Public AI: 4 Key Facts. ]
For starters, private AI trains on your data—and your data alone. Public AI models usually train on vast sets of data. Large language models (LLMs) like ChatGPT, for example, train their data set on a wide range of data from the internet (although, it still took many hours for human employees to ensure the chatbot returned strong answers). This illustrates the challenge of using too many data points—you still have to sift through a lot of noise to get to the proper signal. But large public cloud providers also train on vast data sets—including those of their own customers.
In contrast, private AI models are built only on your data. Results are tailored to your organization, making them far more accurate. For example, let’s say you want to build an AI model that classifies incoming documents. With low-code design, you could quickly train a model using real documents you received as a representative data set. From there, the AI model can classify incoming documents automatically. This model remains private to you and will generate more accurate results and classifications.
Second, data should always remain under your control. The organization providing AI services should not use your data to train their own models.
At first glance, the practice of training on your data may seem innocuous. It’s only one company that sees it, right? Well, there’s more to the story. For starters, you never truly know how they will use your data. Second, regulations often require that data remain private and that users have the ability to have their data deleted. For example, the EU’s GDPR regulations require the “right to be forgotten” as part of their provisions. If a third party has your customers’ data and they request removal, you may be unable to fulfill the request. You should have full control over your own data.
It’s not just your data but also the models you use that should remain private. AI models can be a competitive differentiator, and private AI models will take into account the nuances of your organization. When companies use your data to train their own models, they’re helping your competition by sharing the insights they glean with their full user base. This means your models can end up helping your competition.
It’s critical to note that data privacy also requires a strong cybersecurity foundation. If you build private data models with an in-house data science team, you’ll need security experts on the team. But if you choose a larger vendor for your AI, including those that offer the ability to easily create private AI models, make sure they have strong defense-in-depth security practices, including active security monitoring to protect infrastructure and systems and strong end-to-end encryption to make data impossible to unlock even if stolen.
AI is evolving rapidly, and organizations may adopt solutions hastily to keep up. But it’s important not to throw out basic rules of engagement in the race to adopt the latest productivity technology.
Data privacy is a strong consideration in all technology decisions—we can’t lose sight of that fact in the rush to gain productivity benefits from AI. Organizations still need to follow compliance regulations—such as the “right to be forgotten” as part of the EU’s GDPR provisions or privacy rules around healthcare with the US HIPAA laws. These could make the idea of using public AI providers—particularly large public cloud providers—a non-starter. And it speaks to the importance of strong governance around AI as the field progresses.
[ Listen to Appian CEO Matt Calkins discuss the advantages of private AI vs public AI. Watch the Appian World 2023 keynote video. ]
But it’s not just regulations that make private AI critical. It just makes practical sense. Training on your own data is almost always a better option—you’ll have a far more targeted AI model if it was built with the building blocks of your own data. This will lead to fewer errors and greater productivity among your workforce.
Regulators across countries have increasingly wrestled with the implications of widespread AI adoption. While much attention has been paid to reducing inherent biases or tamping down potential national security concerns, privacy has also been at the center of many of these discussions. And with good reason—public AI models could be non-starters for companies in highly regulated industries, dampening adoption rates and preventing these organizations from realizing the productivity-expanding potential of artificial intelligence.
That’s why it’s critical to future-proof your AI efforts. And the future belongs to private AI. While private AI is a philosophy, it’s also a pragmatic practice. The three central tenets we explored—training on your own data, retaining control over your own data, and ensuring your AI models are never shared—are critical for safeguarding data in the fast-moving AI world. In the rush to adopt AI, do not throw caution to the wind—make sure to keep privacy top-of-mind when integrating AI into your processes and workflows.
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