AI & Automation
When your team uses ChatGPT or similar public AI models to handle client briefs, draft contracts, or analyse sensitive financial data, you're making a calculated trade-off. The convenience is real. The cost savings are tangible. But the data risk is often invisible—until it becomes a liability. This is the critical distinction between private AI vs ChatGPT data risk business operations, and it's something most London SMBs, professional services firms, and financial advisers are glossing over. The truth is simpler than you might think: every prompt you send to a public AI model may be feeding your competitors' learning datasets and compromising your clients' confidentiality.
ChatGPT operates on a free-tier or paid model that serves millions of users worldwide. OpenAI's terms of service explicitly state that content you submit may be used to improve their models—unless you've opted into ChatGPT Enterprise, which costs significantly more and carries its own limitations. For a legal firm drafting a contract involving a merger, or a financial adviser analysing client portfolios, this is a critical vulnerability.
When a solicitor pastes a non-disclosure agreement template into ChatGPT to refine the language, they're transmitting proprietary client structures and commercially sensitive information across OpenAI's infrastructure. The data may be encrypted in transit, but it's stored, processed, and potentially indexed by systems designed to improve the AI's general knowledge. For regulated sectors—legal, financial services, healthcare—this creates compliance exposure:
The problem deepens when you consider that ChatGPT doesn't retain a complete audit trail of what your organisation has submitted. You can't prove what went in, when, or how it was processed—critical requirements for demonstrating compliance during an FCA examination or SRA audit.
Private AI models—whether built on open-source frameworks like Llama 2, or hosted within your own infrastructure—fundamentally change the data equation. Instead of submitting information to a third party, your organisation trains or operates the model within your own environment, or via a vendor with a strict contractual obligation not to share your data.
However, private AI isn't automatically risk-free. Many organisations implementing private models make critical mistakes:
The right private AI implementation requires the same rigour as any other business-critical system: encryption, access controls, audit logging, and regular penetration testing. This is where many SMBs and smaller professional practices stumble. They assume that moving away from ChatGPT is enough. It isn't.
Many organisations try to have it both ways: using ChatGPT for low-risk tasks (brainstorming, general research) and keeping sensitive work internal. Theoretically sound. Practically problematic.
The friction between systems creates decision paralysis. When a team member needs a quick answer, they're more likely to reach for the familiar ChatGPT tab than navigate the slower private system. A solicitor drafting a risk assessment might start with ChatGPT for speed, then move sensitive details to the private version—but by then, fragments of the case have already been transmitted. Financial advisers often describe a similar pattern: initial client notes go into ChatGPT, then the "real analysis" moves to private tools, leaving an audit trail that regulators can easily reconstruct.
There's also the issue of vendor lock-in and cost creep. ChatGPT's pricing is seductive because the cost appears to be per-user per-month. But once you add a private AI system for sensitive work, you're paying for both—and the total cost of ownership for private AI can be 3–5 times higher, especially when you factor in infrastructure, maintenance, and the expertise needed to manage it securely.
Rather than treating private AI and ChatGPT as binary choices, successful organisations implement a tiered approach:
The organisations managing this best aren't the ones with the most sophisticated technology. They're the ones with the clearest policies, the best training, and the strongest cultural emphasis on confidentiality. Technology is a foundation, but human discipline is the difference between a genuinely secure AI deployment and a false sense of security. Whether you're exploring private AI options, auditing your current ChatGPT usage, or trying to understand what compliance really means in this new landscape, the answer starts with honest assessment of your actual risks.
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