Enterprise AI Data Security: What You Need to Know About ChatGPT, Microsoft Copilot, and Other Public AI Tools

Public AI tools such as ChatGPT, Microsoft Copilot, and Google Gemini can drastically increase the speed at which enterprises get things done, but they come at a cost. While these new tools are easy to pick up and use (often with immediate productivity benefits), you must consider AI data security before adopting them into your business — or face potentially catastrophic consequences.

This article explains the risks of using public AI tools without proper data security oversight and AI data security best practices. It also discusses self-hosted AI as a potential solution, as well as our predictions for how these tools will continue to evolve.

Let’s start with the clear benefits: AI tools allow you to solve complex problems orders of magnitude faster than before. This directly translates to more cost-effectiveness and simpler business operations as you outsource more and more time-consuming and complex tasks to AI models.

Public SaaS AI platforms like ChatGPT allow you to access these benefits without any specialized knowledge or technical management on your side — you just sign up, log in, and set your new AI assistant to work.

But no coding, no configuration, and no infrastructure doesn't mean no responsibility. These AI platforms come with data security concerns you need to consider before you put them to work. Remember: you are in charge of the data you pass on to third parties, and you are legally responsible if it is misused once it has left your control.

In addition to any SaaS subscription fees, access to public AI tools comes at the cost of privacy and data security. By passing data directly to public AI tools, you are exposing it to a third party and giving up control over how it is stored or used. If private customer data is involved, this can have legal ramifications: privacy regulations such as GDPR, CCPA, and HIPAA routinely punish businesses that disclose user data without the required security and control measures and agreements.

You also risk divulging company secrets to these large language model (LLM) systems, which the providers could then use to train future systems for public use. This might not put you on the hook for any legal consequences, but it could seriously threaten any competitive moat that your company has. Imagine if your competitor could just ask ChatGPT to give them summaries of your customer calls!

Private information or proprietary information can sneak into data you send to public AI services when you’re training or fine-tuning their models for your use case. It can then be exposed and harm your business. Other ways this can happen are through a misconfigured retrieval-augmented generation (RAG) pipeline, prompts from employees with good intentions who are more focused on their immediate tasks than the long-term impacts of AI data security, or outright malicious actors trying to execute cyberattacks.

Fortunately, there are technologies and practices you can put in place within your organization to reduce the risks posed by public AI tools. These controls help ensure that you are not exposed to any legal, financial, or operational problems in the event that protected data is improperly used.

It’s a good practice for companies to explain the basics of how these public AI tools work to employees. Communicate which information might compromise security if it gets passed to an LLM, and make sure that vetting data before sending it to third parties is part of your business processes. In and of itself, this is not a robust protection, as any individual employee could violate the privacy policy at any time. Still, it’s good to have as many employees as possible working with your privacy policy rather than against it.

One case where you do want employees working against your privacy policy is during regular security audits. You can take the red teaming concept from the security world and apply it to your own AI data security. Have a portion of your team intentionally try to break your company’s security protocols and send private data (mock data, of course) to a public AI platform. By doing this, you get to identify the holes in your system.

Doing these security audits on a regular basis can reveal where your current systems are lacking so that you know exactly where to focus your efforts to make them more robust.

Your security chain is only as strong as its weakest link. It doesn’t matter how secure your systems and processes are if even one of your vendors is vulnerable. That’s why it’s important to choose AI platforms that are transparent about their security practices and policies.

You must assess the security and privacy policies that public AI vendors are using or plan to use and ensure they meet your own legal and reputational requirements. You should also ensure that the correct agreements are in place for the jurisdictions your users are located in. (For example, GDPR requires a data processing agreement for any third-party information sharing.) 

Many successful enterprise applications of LLMs are driven by RAG. RAG is a process that improves responses from AI tools by sending up-to-date, specific data along with prompts so that LLMs always have the specific, contextual data they need to respond accurately. Because this process controls what information gets passed to an LLM and what doesn’t, it provides an opportunity to implement security measures at the retrieval stage of RAG by using conventional role-based access controls.

Practically, this can be as simple as leveraging existing role-based access permissions: employees can only submit the data they have access to. While this doesn't protect against employees who require broad access to data from accidentally submitting something they shouldn't, it does reduce the AI data security risk that accidental disclosure poses overall. In more complex and automated use cases, service accounts can be restricted to certain datasets for the same effect.

Prompt injection is also a risk — malicious users might try to add their own instructions to a prompt in order to confuse the LLM and get it to do something that you don’t want, like reveal sensitive data. One of the best ways to guard against this is with prompt templates. Instead of passing the user query directly to the LLM, you can wrap their request in a template like this:

Public LLMs like those used by ChatGPT, Microsoft Copilot, and Google Gemini are often so effective because they are powered by vast infrastructure and huge datasets. To use them, though, you need to send your private data off-prem, give up control, and trust that your vendor will handle that data responsibly. This presents a data security risk by its nature.

For most tasks, the only way to truly make sure you have the necessary AI data security is to use a self-hosted LLM. While normally less powerful than the frontier models, self-hosted LLMs can still be effective for focused use cases — and they completely sidestep the privacy problems inherent with public AI tools. They’re also improving rapidly. Recently, researchers found that the LLM DeepSeek was as effective as one of OpenAI’s latest models — and it’s completely open source and free to self-host.

Of course, self-hosting comes with downsides. It can be costly to set up and maintain infrastructure if you need to process a lot of data. Self-hosting also introduces more complexity that your business has to manage in-house, rather than just outsourcing to a public LLM provider — and you may need to rely on less powerful models.

But you also get certain benefits that you can't get with large public LLMs. First, you get complete control of your data lineage to make sure no systems have access to any data they shouldn't. You can even completely airgap your system if that’s appropriate. You get lower latency for all requests to the LLM and lower overall network traffic, since it lives physically close to your application. You can also have far more flexible AI applications, since the LLM can run offline.

If you have the technical know-how in-house, you can implement a hybrid system that offers the best of both worlds. With a hybrid system, a private LLM redacts sensitive data before sending prompts to more powerful external LLMs. While this is more complicated to set up, it can help you realize the benefits of both public and private AI toolchains.

There's a lot of speculation over what's coming in AI — from new technologies to new applications and use cases. In 2025, we expect several key technologies and practices to emerge that will affect AI data security for enterprise.

Some companies are currently investing in building lighter small language models (SLMs). While these models might not be as powerful as their LLM counterparts that focus on incredibly broad use cases, they may become powerful enough to use reliably for narrow tasks — and involve significantly less overhead and infrastructure. These SLMs are also far easier and less resource-intensive to host and run than LLMs.

One major pain point impeding the mass adoption of AI tools within enterprises is the difficulty of setting up a secure AI stack. Therefore, we expect to see more enterprise-managed, enterprise-hosted complete AI stacks like Weaviate and Qdrant. These companies integrate an assistant, model, and vector database out of the box. Pinecone provides a free tier for their AI stack that lets you easily spin up AI-powered apps without having to invest too heavily upfront. This significantly cuts down the barrier to entry, so expect to see more platforms offering this in the future. Tools like Appsmith are natural partners to these products, enabling users to integrate AI components with other databases and tech tools into a bespoke workflow or custom frontend.

As these platforms become more prominent, they will allow you to easily spin up complicated technical back ends for your AI applications. Just make sure you understand exactly what their AI data security policies are, so you aren’t just transferring the data security problem from public LLMs to these enterprise-managed AI stacks.

It is a standard requirement for companies to disclose their privacy policies and how they use customer data (especially with privacy regulations like GDPR and CCPA). In your privacy statement, you have to list the third parties you share data with, as well as how and why that data will be used. If a third party notifies you of a breach on their side, you also have to pass the message on to your own users.

Businesses that use public and private AI tools are already including AI-specific sections in their privacy policies to ensure that users are fully aware of how their data is used, who it may be shared with, and how that data handling is fully compliant with required regulations.

Appsmith is no exception: we maintain an AI section within our privacy policy and encourage other businesses to do the same, especially since many customers are asking for this directly. As customers and possibly government regulations begin requiring more of these disclosures, it will become even more important to understand exactly how your vendors use the data provided to them within AI applications.

Even though it can be worth it for long-term security, it can still be a lot of work to set up your own self-hosted LLM solution for AI applications. That's why it's important to look for different tools that can do the heavy lifting for you.

A big component of your AI tech stack is the platform you use to build and host your applications. A robust app platform can implement a lot of the infrastructure, like authentication and encryption, so that you don’t have to. As AI becomes more prominent in applications, we expect that a lot of the back-end requirements to support it will become standard as well.

That includes any requirements to self-host your LLMs and the apps that rely on them. That’s why we're keeping AI in mind as we continue to build Appsmith, and why we have new AI features in our roadmap. We provide a free, open-source cloud-hosted version as well as the ability to self-host. We have also recently introduced managed hosting, which removes the pressure of infrastructure deployment and maintenance while ensuring appropriate privacy and security for your AI apps.

Reach out to us today to see how we can help you implement AI data security across your enterprise. And, if you’re experimenting with AI solutions in support, sales, or customer success, request early access to our upcoming AI assistant tool to explore a whole new way of using AI in your enterprise.