GM Nostr. I need some AI knowledge from you if you could please read my use case, and see if it makes sense.
I am trying to build agents for a legacy business that is highly adverse to using AI due to privacy concerns etc. However, I believe I can integrate AI without exposing any critical information outside of their network. This is how I propose I pull that off. (I've also tested this with Openclaw and have a production ready version that appears to work fine).
Essentially I need to take sales information (with sensitive data) and inventory information (with sensitive data) out of a database. I have no barriers to do that, but I don't want that information on a cloud server outside the network.
So here is my solution. I have SQL reports run monthly, pulling this data from the database, saving it where my Openclaw agent running Ollama can get to it. Then that Ollama agent (hosted on a local private server) masks the sensitive data using SHA-256, and saves the "key" file that maps original data to masked data locally.
Then another agent running Claude Haiku, gets the masked data, does some analysis on it that I need done automatically. Returns that analysis back to the Ollama agent, which then decrypts the output using the original mapping file.
That way no sensitive data is getting onto Claude servers. Even though I am using API keys, I would prefer to have zero data retention, no training, no exposure whatsoever hence the Ollama hashing step.
Is this sensical? Is it over engineered? Is it doing what I am describing?
There is zero tolerance for data exposure, and this was the solution that I thought could address that. I would run all of the analysis locally on Ollama but I have several workflows that need to run using the output data and need OpenClaw to manage that for me. In addition, I need some compute power hence why I am electing Claude for the "hard work". I don't know that Ollama could do everything I need it to without Openclaw, and so far I haven't got those two to play well together.
Let me know.
I am trying to build agents for a legacy business that is highly adverse to using AI due to privacy concerns etc. However, I believe I can integrate AI without exposing any critical information outside of their network. This is how I propose I pull that off. (I've also tested this with Openclaw and have a production ready version that appears to work fine).
Essentially I need to take sales information (with sensitive data) and inventory information (with sensitive data) out of a database. I have no barriers to do that, but I don't want that information on a cloud server outside the network.
So here is my solution. I have SQL reports run monthly, pulling this data from the database, saving it where my Openclaw agent running Ollama can get to it. Then that Ollama agent (hosted on a local private server) masks the sensitive data using SHA-256, and saves the "key" file that maps original data to masked data locally.
Then another agent running Claude Haiku, gets the masked data, does some analysis on it that I need done automatically. Returns that analysis back to the Ollama agent, which then decrypts the output using the original mapping file.
That way no sensitive data is getting onto Claude servers. Even though I am using API keys, I would prefer to have zero data retention, no training, no exposure whatsoever hence the Ollama hashing step.
Is this sensical? Is it over engineered? Is it doing what I am describing?
There is zero tolerance for data exposure, and this was the solution that I thought could address that. I would run all of the analysis locally on Ollama but I have several workflows that need to run using the output data and need OpenClaw to manage that for me. In addition, I need some compute power hence why I am electing Claude for the "hard work". I don't know that Ollama could do everything I need it to without Openclaw, and so far I haven't got those two to play well together.
Let me know.
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