An Intellyx Brain Candy Brief
Private AI uses language, image and sound recognition to monitor communications and perform an array of privacy treatments on data entering and leaving an enterprise application estate, including AI inference and training models.
When you think of privacy assurance, you often think of redaction, wherein sensitive PII like ID numbers and addresses, and PHI such as prior illnesses or medications are obfuscated from human view, but that’s only a small part of privacy assurance, especially when high-data-volume LLMs, RAGs and machine learning scenarios are brought into the picture.
The Private AI solution scours structured (digitized or text) and unstructured data (voice calls, pictures of documents) from prompts and responses between people and operational systems, with policy-based controls for exactly how and when sensitive data is used.
No need to write regexes, just hook up the sources, select data treatment mechanisms, and fine tune the data flow. A cool ‘playground’ interface shows even non-data-scientists the resulting impacts of selected data treatments and replacements.
For instance, you may want to allow an AI to conduct sentiment analysis on a raw customer phone call, but anonymize any private information in the results that a chatbot or customer service rep doesn’t need to access. Or, you might reconstitute key datapoints like a customer ID or order number that were hidden from a third-party system or model training work if that makes sense for the workflow at hand.
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