Company Dossier · Privacy Infrastructure
Integral Privacy Technologies
The independent privacy layer for AI - turning the world's most sensitive data into datasets you can actually use, and defend.
The independent privacy layer for AI - turning the world's most sensitive data into datasets you can actually use, and defend.
Every AI lab wants real-world data. Every organization sitting on that data - hospitals, pharma companies, insurers - is terrified of the liability that comes with sharing it. Integral Privacy Technologies built a business in the gap between those two facts. The San Francisco company takes sensitive, regulated data, de-identifies it, and hands back a dataset that is both useful for analysis and defensible under privacy law.
The work is unglamorous by design. Integral's messaging reads "Signal preserved. Risk assessed. Built to be examined" - three short claims that map to the three things a compliance officer actually worries about: did you keep the analytical value, did you measure the re-identification risk, and will this survive a regulator pulling the thread. Founded in 2022 by Shubh Sinha and John Kuhn, both alumni of the data-connectivity company LiveRamp, Integral started where the stakes were highest: HIPAA-regulated healthcare data. Healthcare generates roughly 30% of the world's data, and almost none of it moves freely.
Its headline number is speed. A dataset compliance review that traditionally takes 10 to 12 weeks, Integral compresses to hours; a full read on a dataset's quality and compliance obstacles arrives in about a day. In an industry where a legal review can stall a project for a quarter, "time to value" is not a marketing line - it is the product.
In most regulated companies, the data team and the legal team are locked in a slow tug-of-war. Analysts want to move; compliance wants proof. Integral's premise is that this is a false choice - you can de-identify a dataset and keep its signal, if you do the statistics properly and document the work.
Regulated datasets sit unused for weeks while lawyers, statisticians, and engineers negotiate what is safe. Projects miss windows; value evaporates.
Teams either over-scrub data until it is useless, or cut corners and carry hidden re-identification risk into production. Neither holds up in an audit.
Automated de-identification plus embedded experts and audit-ready documentation - fast enough to keep pace with a project, rigorous enough to defend it.
Time to value - the speed with which we empower organizations to leverage privacy-sensitive, regulated data - is our differentiation.
Integral pairs software with people. The software automates de-identification, monitoring, and evaluation; the people - statisticians and privacy engineers - embed in the customer's pipeline where judgment is required.
Statisticians and privacy engineers embedded directly into a customer's data pipeline to design, run, and document de-identification as an ongoing program.
Automated de-identification that preserves analytical utility, delivered with defensibility opinions and audit-ready documentation.
Qualified-expert assessments certifying that datasets meet HIPAA de-identification standards.
Ongoing re-identification risk scoring, entity-preserving linkage, and continuous compliance monitoring across evolving data flows.
Rapid read on a dataset's quality, coverage, and compliance requirements - a multi-week manual process reduced to roughly 24 hours.
Software subscriptions combined with "Forward Deployed" privacy engineering, sold to enterprises, data platforms, pharma, and AI labs.
Integral's customers and partners cluster in regulated, data-heavy sectors: LiveRamp, Databricks, Skyflow, Datavant, PurpleLab, STATinMED, Health Union, Insagic (Publicis), OptimizeRx, and MiBA. With the 2026 Series A, the company is pushing its go-to-market toward AI labs, data and annotation platforms, and enterprises participating in the real-world data economy.
The competitive field is a mix of de-identification and synthetic-data specialists - Datavant, Privacy Analytics, Tonic.ai, Gretel, MOSTLY AI, Truata - and, most often, in-house teams doing expert determination by hand. Integral's wedge against all of them is the same: defensibility plus speed. Synthetic data can lose the signal; DIY compliance is slow and inconsistent. Integral's bet is that "built to be examined" documentation is what wins when a regulator eventually asks.
Keeps real-world signal instead of approximating it, while still meeting de-identification standards.
Hours instead of months, with consistent methodology and expert sign-off rather than ad-hoc judgment.
Software plus embedded humans, so the hard judgment calls are made by statisticians, not a config screen.
Investors are betting that privacy engineering becomes essential AI infrastructure. Total raised: roughly $24.9M.
Shubh Sinha and John Kuhn leave LiveRamp and start Integral in San Francisco to automate healthcare data privacy.
Led by Haystack, to maximize data privacy and quality; the de-identification and evaluation platform launches.
Continuous risk assessment and monitoring expand as the healthcare and pharma customer base grows.
Forward Deployed Privacy Services expand and Integral repositions as the independent privacy layer for the data economy.
We started where the stakes were highest: HIPAA-regulated healthcare data.
Healthcare is ~30% of the world's data - and almost none of it moves freely. That single statistic is the market Integral was built to open.
The founders met at LiveRamp, wrangling identity and privacy for pharma and health, before starting Integral in 2022. LiveRamp later became an investor.
The tagline evolved from "privacy infrastructure for healthcare AI" to "the independent privacy layer for AI" as the company widened its scope.
"Forward Deployed" - Integral borrows the term for elite deployment teams to describe its embedded statisticians, a nod to how manual the hard calls remain.
Founder conversations on breaking healthcare's data bottleneck. (External links.)
It transforms sensitive, regulated real-world data - starting with HIPAA-covered healthcare data - into compliant, AI-ready datasets through automated de-identification, expert determination, continuous risk assessment, and embedded privacy engineering.
Shubh Sinha (CEO) and John Kuhn (CTO) founded Integral in 2022. Both previously worked on identity and privacy products at LiveRamp.
Roughly $24.9M total, including a $6.9M seed round led by Haystack in 2023 and an $18M Series A closed in July 2026.
Enterprises and platforms handling regulated data across healthcare, pharma, insurance, and AI - including LiveRamp, Databricks, Skyflow, Datavant, PurpleLab, STATinMED, Health Union, and OptimizeRx.
Integral combines automated de-identification with embedded human experts and audit-ready, defensible documentation - designed to hold up to regulatory scrutiny while preserving analytical utility, and it delivers results in hours rather than weeks.