Diagnostic support, claims adjudication, and patient-facing agents touch some of the most sensitive data there is. Sakshi tokenizes identifiers before anything is stored, records every decision on a tamper-evident chain, and governs what data an agent may extract, access, and retain, so an automated call is still an accountable one.
What Sakshi governs
Every recommendation witnessed with its model identity, inputs, and reasons, and bounded so high-stakes calls route to a human.
Approvals and denials on the record, with denials routed to human review by policy, and fairness screens on the outcomes.
Extraction and access governed against declared purpose: over-collection and off-purpose reads are flagged, raw identifiers never stored.
Coverage here is built on our DPDP work and grows as India's health-data rules mature. We are honest about what is proven today and what is on the roadmap.
Aligned to
Tokenization at ingest, purpose-limitation and minimization screens, India residency, and a signed audit bundle come from the same engine that maps BFSI to RBI and SEBI. As sector-specific health rules land, they become another clause map, not another product.
Try it with synthetic data, or talk to our team about your workflows.
