Not All AI Carries the Same Risk. Here’s What “Private AI” Means for Accounting Firms.

The profession is clearly experimenting with AI, but still treating data security as the biggest obstacle to going further.

That worry is well-founded. Accounting Today found that 83% of accountants are concerned about AI exposing client data, and 70% think the technology is evolving too quickly — a higher share than banking, insurance, or any other financial services sector surveyed. This is a profession that handles some of the most sensitive financial data there is, so caution makes sense (and is appreciated).

What doesn’t always get explained is that the risk isn’t the same across every AI tool. There’s a meaningful difference between a public AI model — the kind powering ChatGPT and similar tools — and a private one built to operate inside a secure, controlled environment. Understanding that distinction is what this piece is about.

Most of the tools firms are experimenting with today are built on generative AI. So to understand where the risk comes from, it helps to understand how that actually works.

What is Generative AI?

At a basic level, generative AI is just really good pattern recognition.

It learns patterns from large datasets — text, images, code, documents — and uses them to generate new content that fits the context of what you’ve given it. When you ask for a recipe, it uses patterns it has learned to give you something that matches your prompt rather than inventing something from scratch.

Another example is when a tool reads a jumble of meeting notes and produces something clean and organized — or, in an accounting context, analyzes a prior-year return and generates a custom organizer for that specific client. In each case, it’s taking what it’s learned, recognizing patterns, and generating something new that fits the context.

Now, generative AI isn’t new. Versions of it have been quietly running inside software for years. What changed everything was ChatGPT. In November 2022, it put this kind of technology directly in front of people, and showed, in seconds, what it could actually do.

Obviously, it spread remarkably fast. Within just two months of its release, ChatGPT reached 100 million users, making it the fastest-growing app in history. Today, hundreds of millions of people use ChatGPT each week. And that’s just one of many tools built on this same technology.

Widespread use doesn’t mean it’s dependable, though. Generative AI has a major limitation: it’s only as good as what goes into it. Poor inputs or vague prompts lead to poor outputs — and the tool has no way of knowing the difference. It doesn’t flag uncertainty or catch its own mistakes. It just produces output that sounds confident, whether it’s right or wrong.

That’s not a reason to avoid it. It just means you must be deliberate about where you use it. Generative AI performs most reliably when it’s scoped to a specific, well-defined task with clear inputs. The broader and more open-ended the job, the more room there is for it to go sideways. That distinction matters a lot when you’re evaluating any AI tool for your firm.

Why the skepticism makes sense — and where it gets complicated

Think about the last time a client called because they’d gotten tax advice from ChatGPT. You had no idea what prompt they used or how they worded it. You probably spent ten minutes explaining why the answer was wrong before you could even get to the actual conversation. Moments like that create lasting distrust around the whole category of technology.

But those experiences almost certainly come from general-purpose tools built to handle millions of queries across millions of users. That’s a pretty different thing from a purpose-built, privately hosted AI designed for accounting workflows. They use similar underlying technology, but they’re built differently, deployed differently, and handle data differently.

Accountants are trained for this kind of distinction. The profession runs on questioning things that seem too easy and getting it right rather than getting it done fast. That instinct doesn’t disappear when a new technology shows up (and it shouldn’t).

What makes AI harder to evaluate than previous technology shifts is the backdrop it arrives with. When document portals arrived, practitioners figured out the workflow and moved on. When cloud storage came along, the questions were practical — security certifications, backup policies, access controls — and the answers were findable.

With AI, the anxiety isn’t coming from one specific technical question, but from a broader sense that something is happening inside these tools that’s hard to verify. According to the same AccountingWEB survey, 47% of firms not using external AI tools cite security and privacy concerns as the primary reason — and much of that hesitation comes down to not knowing what vendors are doing with data once it leaves the firm.

We’ve covered what generative AI is and why the skepticism around it is legitimate. Now, let’s look at how differently it can be deployed and what that means for data risk.

How do public AI models handle your data?

The question we hear most about AI tools is: what happens to my data while the tool is doing its job?

It’s a critical question, but what’s interesting is that most firms are already using public AI tools without a clear answer to it. AccountingWEB found that 71% of accounting professionals are using tools like ChatGPT for things like tax research.

When someone pastes client data into a public AI tool, that data leaves the firm’s environment. Where it goes from there depends on the vendor. Some may store it, use it to improve the model, or allow their teams to review inputs, depending on how the service is configured. The terms of service will tell you, but they’re not always written to make that easy to find. That’s part of why the concern persists even among firms already using these tools.

What practitioners are specifically worried about, when you dig into the research, is the scenario where client data ends up as training material — through a staff member who didn’t realize what they were uploading, or a vendor whose data handling turned out to be less airtight than promised. In a profession where a single data incident can shape how clients see you for years (not to mention, cost you thousands of dollars in recovery and fees), that risk has real consequences.

That’s one reason why the CPA.com AI Due Diligence Guide put “where does your data go?” at the top of the list of questions every firm should ask before adopting any AI solution — because once data leaves your environment, your ability to control what happens to it drops significantly.

What does private AI mean?

Private AI isn’t a smarter or more specialized version of AI. It’s just a different way of deploying the technology.

Think about the difference between having a conversation in a crowded coffee shop versus a private office. You might be saying the same things either way, but who can hear it — and what they do with what they overheard — is completely different. Private AI works the same way. The underlying technology is the same, but the environment it operates in and who has access to what flows through it changes entirely.

A private AI model runs in a controlled, isolated environment. Your data never touches shared servers or contributes to model training. Everything stays within a defined, auditable boundary that belongs to the vendor — not a public system accessible to other organizations.

And according to the AccountingWEB research, that’s exactly what accounting pros want: there’s a decisive preference for AI embedded in tools they already use and trust, over external platforms that require data to move somewhere unfamiliar.

That preference makes sense, but trust alone isn’t enough when AI is involved. Even if you already trust the platform, you still need to verify how its AI is deployed. Hosting, access controls, data retention policies, and independent security certification all matter. SOC 2 Type 2 is the standard worth asking about specifically — it’s not a self-assessment. It’s an independent audit of the environment where your data lives, conducted over time.

How SmartRequestAI fits into this

SmartRequestAI is SmartVault’s AI-powered tax client intake tool. It reads a client’s prior-year tax return and generates a personalized intake questionnaire tailored to that client’s specific situation. The tool also auto-routes documents to the correct client folders and generates bookmarked, prep-ready workpapers.

It does that work on private AI models hosted within SmartVault’s own infrastructure. Your data doesn’t move to an external large language model. It’s never used to train or improve the model, and it never becomes visible to anyone outside your environment. SmartVault’s platform is SOC 2 Type 2 compliant, meaning the security controls around that private AI have been independently audited and verified over time.

For firms that are curious about AI but cautious about where to start, SmartRequestAI is a practical entry point. It’s tightly scoped to a specific workflow, built on private AI from the ground up, and operates inside a platform that already meets the security standards your profession requires. You’ll be using it for a well-defined job, in an environment designed to keep client data secure.

Ready to see SmartRequestAI?

If you’re not yet a SmartVault customer, the best place to start is a demo. You’ll see the full platform — including SmartRequestAI — and you can ask us anything about how it’s built and how your data is handled.