Skyflow
API-delivered data privacy vault that isolates and tokenizes sensitive PII, PHI and PCI data instead of encrypting it in place.
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Skyflow provides a ‘data privacy vault’ delivered as an API: rather than encrypting sensitive data where it sits inside an application or database, Skyflow isolates PII, PHI and PCI data in a dedicated, policy-governed vault and returns tokens in its place, so the sensitive values never have to flow unprotected through application code, analytics pipelines or, increasingly, large language models. The company launched a GPT Privacy Vault product specifically to keep sensitive data out of LLM prompts and outputs.
Founded in 2019 by Anshu Sharma and Prakash Khot and headquartered in Palo Alto, Skyflow has raised roughly $100-122 million in funding, including a $30 million Series B extension led by Khosla Ventures in 2024. Its differentiator is architectural: instead of adapting existing encryption or masking techniques, Skyflow removes sensitive values from data flows entirely, which the company says helped it grow LLM-related usage from 0% to roughly 30% of revenue as customers sought to adopt generative AI without exposing regulated data.
Skyflow’s customer base spans fintech, retail, travel and healthcare, with disclosed clients including GoodRx, Lenovo and Hippocratic AI.
Innovation Matrix Assessment
Moved quickly to launch a GPT/LLM-specific privacy vault product as generative AI data-exposure risk emerged as a mainstream enterprise concern.
Removing sensitive data from application and AI pipelines entirely, rather than merely encrypting it in place, reduces the practical blast radius of a breach or model-data-leakage incident.
Real but modest funding evidence — a $30M Series B extension in 2024, no larger recent round found — and reported usage/growth figures (billion records, 2B API calls per quarter, 110% revenue growth) are company-published rather than independently verified.
Tokenizing and isolating sensitive data via an API-delivered vault is a structurally different approach from traditional field-level encryption or DLP, though it remains a specialized niche rather than a broad category redefinition.
Named customers (GoodRx, Lenovo, Hippocratic AI) and an investor-published leadership profile provide some independent corroboration, but no third-party security audit or breach-prevention case study was found.
Keeping regulated data out of AI pipelines is likely to become more, not less, important as enterprise LLM adoption accelerates.
Why CISOs Should Care
Lets security and privacy teams adopt AI and analytics tools without having to trust every downstream system with raw PII, PHI or PCI data.
What Makes It Different
Sensitive data is isolated in a dedicated vault and represented everywhere else by policy-governed tokens, rather than encrypted and decrypted as it moves through systems.
The Matrix Verdict
62/100 — INCREMENTAL INNOVATOR
An Incremental Innovator: Skyflow's data-vault architecture is a genuinely different and well-timed approach for the LLM era, but its funding scale and independent efficacy evidence remain modest for a small, still-early company.
Editorial Note: Claims vs. Verified Findings
Usage-scale figures (billion records, 2 billion quarterly API calls, 110% revenue growth) and the 0%-to-30% LLM revenue mix are drawn from an investor-published profile and were not independently audited.
Sources
- Insight Partners (Skyflow leadership profile) — https://www.insightpartners.com/ideas/skyflow-leadership/
- BusinessWire ($30M Series B extension) — https://www.businesswire.com/news/home/20240328765469/en/Skyflow-Raises-$30-Million-led-by-Khosla-Ventures-for-Data-Privacy-in-AI-and-Global-Data-Residency
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