AI is still in its experimentation phase. Most people are focused on what AI can do, while privacy and governance remain an afterthought.
In regulated industries, privacy and governance are not an afterthought. They are the central criteria that determine whether a tool gets approved.
For the last 30 years, software has been deterministic, and so have its controls. You set up guardrails around your data, and those guardrails held.
AI presents a different challenge, because data is the product itself. To get smarter, AI needs more data, more tool connections, more training inputs. The more data you add, the more risk vectors you introduce. AI is a self-feeding monster.
What a lot of people don’t know is that an LLM, or large language model, is effectively a read-only system at inference time. It takes an input and produces an output, and nothing is captured in between. All the data persistence actually happens at the application layer, or what the AI community calls the orchestration layer.
SAFi replaces the orchestration and application layer that every AI provider ships with, and instead uses a direct API connection to the LLM.
All the data SAFi generates, such as conversations, memory, tool logs, and so on, stays in your environment.
Most AI providers, when you use API keys, don’t capture your data during inference. Under the Compliance tab in SAFi, you can find a list of providers that meet this standard.
But if you want absolute privacy and need to guarantee that nothing leaves your network, you can run local models with SAFi. It works just fine.
Most commercial AI chatbots, outside of their enterprise tiers, capture the data you feed them and use it to train their models.
If I were the head of compliance at a regulated company, I would not allow commercial AI tools on my network. And even the ones that label themselves “Enterprise” I wouldn’t fully trust. Those are often just labels slapped on top of commercial chatbots.
The setup I would trust without hesitation: SAFi running with local AI models, behind a VPN, with the internet cable thrown out the window.
But that’s just me 😜
SAFi gives you total control and visibility over what people are feeding into the AI, regardless of where the models are located. You can block specific terms and phrases before they ever reach a model. You can restrict which providers are allowed to receive your content at all, and that enforcement fails closed, so a blocked provider is never silently swapped for another one. None of this is judged by a model. These are fixed rules evaluated in code, which means the same input always produces the same decision, and anyone holding the audit record can recompute it. You set the floor at the organization level, and business units can make it stricter but never weaker.
And on the capability front, SAFi has the same underlying power. The LLMs available through SAFi are the same models behind commercial chatbots like Claude and ChatGPT. The difference is that those chatbots are unrestricted: they can pull data from anywhere, including sources you don’t even know exist. SAFi is contained to only what you allow, so it might appear dumber. In this context, dumber is better. I only want the AI to have access to the data I choose to give it, and to be held accountable for that scope. Nothing more, nothing less.
