AI Search Without Data Movement? If Only! 

I recently received an email from a well-respected AI company telling me that their system could help me find information in my personal data. Great! All I had to do was upload my data to their very secure website. Not so great.  

I have information scattered across local storage, email, Slack, and multiple cloud providers. Pulling all that information together and uploading is more work than I want to put in just for better local search. Corporate data is even worse: not only do companies have all the same data problems as I do, plus loads more databases and data intensive applications, their problems are magnified across their entire workforce. Large banks, for example, might have petabytes of data in company databases plus at least as much scattered around in Office documents, PDFs, Slack, WebEx, Teams, Email, and so on. And they also must address regulatory requirements around personal information (PII, PHI, PPI) and data movement.  

Moving all that data to some vendor’s cloud is both logistically and technically nuts.  

At about this point in my musings, what should pop into my mailbox? This article: AI, huge hacks leave consumers facing a perfect storm of privacy perils.  

o make a long story very short, not only are data breaches becoming ever more common, AI enables hackers to quickly sort through all that information to find good targets. The idea of putting personal data up on the web is only becoming less and less attractive (if that’s possible at this point). For companies, the logistics, expense, and risk of uploading internal data to external servers so that AI can search it are only getting worse by the minute. There are just too many weak points where security can be compromised—not something you want to stay up at night worrying about. 

Zero ETL Keeps Data Safe 

At the same time, the amount of information is increasing exponentially, and internal data isn’t SEO friendly: it’s unstructured, disorganized, and scattered. Keyword searches return too much irrelevant information to sort through on a regular basis. The data scavenger hunt can easily eat up 7-9 hours per person per week. We need AI to manage that information. 

Using AI to find relevant information should take a Zero ETL approach. It’s best when AI works with the data where the data is located: databases (of course), but also in all those other pesky locations that I mentioned earlier (Slack, Email, etc). No heroic (and probably doomed) efforts to gather all the data from every location and centralize it. No copying into a vector database or uploading to someone else’s server. No bringing the data to the AI.  

This is how SWIRL approaches AI search: the data stays in place and SWIRL brings the AI to the data. The data never leaves your control.  

SWIRL also leverages existing security protocols so you’re not adding another layer of vulnerability to your environment.  

SWIRL runs in your private cloud or completely on-premises. SWIRL runs on air-gapped systems, making it perfect for secure data installations.  

SWIRL gives you control over which AI you use, so you’re not locked into one model but rather can choose any AI approved for your environment. 

SWIRL’s powerful combination of advanced search, RAG, and AI lets you end the data scavenger hunt. Safely, securely, and quickly. So, you can get the information you need when you need it and sleep well at night knowing that your data isn’t sitting on someone else’s server waiting to be hacked. 

To find out more about SWIRL’s Zero ETL approach to AI search, contact us.