Skyflow
Skyflow is a Palo Alto, California-based privacy infrastructure company founded in 2019 by Anshu Sharma, Co-founder & CEO, and Roshmik Saha, Co-founder & engineering leader. The company provides a Data Privacy Vault, a platform designed to isolate and govern sensitive information while allowing businesses to continue using that data across applications, analytics systems, and AI workflows. Skyflow is also emerging as one of the companies helping define the Data Privacy Vault category itself.
Skyflow operates at the intersection of enterprise software, cybersecurity, data governance, privacy infrastructure, and AI. Its core premise is straightforward: sensitive customer data should not be scattered across databases, applications, logs, and third-party tools. Instead, it should reside inside a dedicated vault governed by encryption, tokenization, policy controls, and zero-trust principles. The timing matters. Enterprises are racing to deploy AI systems while navigating stricter privacy expectations, expanding regulations such as GDPR and HIPAA, and rising breach costs. Skyflow is positioning itself as a trust layer for this new environment.
The broader implication reaches far beyond a single company. Skyflow reflects a larger shift toward privacy becoming a foundational component of software architecture rather than a compliance exercise added after products reach scale.
About Skyflow
Trust has quietly become one of the most expensive assets in technology. For years, software teams treated sensitive data like inventory. The more systems that had access to it, the more useful it became. Customer records moved between applications. Payment data flowed through integrations. Identity information found its way into analytics tools, support platforms, and operational databases. Then reality arrived with regulators, security incidents, compliance requirements, and AI systems hungry for data.
Skyflow was founded to challenge that model. The company built a Data Privacy Vault that stores regulated data such as PII, PCI, and PHI separately from operational systems. Applications interact with protected tokens and policy-controlled access layers instead of directly handling raw information. That distinction becomes increasingly important as organizations deploy more software, more integrations, and more AI-driven workflows. Every copy of sensitive data creates another potential exposure point. Skyflow's thesis is simple: reduce the number of places sensitive data exists in the first place.
Why Skyflow Matters Right Now
Technology has always rewarded speed. AI is forcing companies to think about trust at the same scale. Enterprise leaders want AI systems to automate workflows, analyze customer interactions, improve operations, and create new revenue opportunities. Those systems perform best when they have access to rich datasets. Unfortunately, the richest datasets often contain the most sensitive information. That tension is becoming one of the defining infrastructure challenges of the AI era.
Skyflow increasingly positions itself as a security platform for the AI data stack, helping organizations control what sensitive information can be accessed, processed, stored, or retained by AI systems and AI agents. As enterprises move from AI experimentation to production deployment, governance requirements are becoming just as important as model performance. Data access, auditability, privacy controls, and compliance frameworks increasingly determine whether AI initiatives scale beyond pilot programs. Executives are discovering that AI strategy and data governance strategy are no longer separate conversations. They are rapidly becoming the same conversation.
The Problem Skyflow Is Solving
Traditional security models focus on protecting systems that contain sensitive information. Skyflow focuses on reducing how many systems contain sensitive information at all. Its platform combines encryption, tokenization, masking, access controls, and governance policies into a centralized privacy layer accessible through APIs. Applications continue functioning while minimizing direct exposure to regulated data.
The practical outcome is significant. Engineering teams spend less time building privacy controls from scratch. Security teams gain centralized visibility. Compliance teams reduce operational complexity. Organizations can continue using data across analytics, operations, customer experiences, and AI initiatives without broadly exposing sensitive information. For organizations operating in financial services, healthcare, insurance, and other highly regulated sectors, that proposition resonates immediately.
Market Context
The market forces supporting Skyflow appear unusually durable. Privacy regulations continue expanding globally. Data residency requirements are becoming more complex. Consumers increasingly care about how organizations handle personal information. At the same time, AI systems are creating greater demand for data access rather than reducing it. Those trends are colliding. Historically, privacy was often treated as a cost center. Today, privacy is becoming a prerequisite for growth. Organizations cannot fully embrace AI, automation, and digital transformation without solving governance, trust, and compliance challenges first.
The broader market includes data security, tokenization, identity, and governance vendors. Skyflow's architecture takes a different approach by isolating sensitive data before it spreads throughout enterprise systems. That architectural shift reflects a larger movement across enterprise software: minimizing exposure instead of continuously adding layers of protection around exposed assets. When infrastructure categories emerge, they often begin as niche problems before becoming standard architecture. Data Privacy Vaults may be following that path.
Leadership and Team
Skyflow's leadership combines experience across enterprise software, cloud infrastructure, and distributed systems. Anshu Sharma previously held leadership roles at Salesforce and has spent years operating across startup and enterprise ecosystems. Roshmik Saha brings deep engineering expertise and experience building large-scale technology platforms. The founding vision was shaped by a belief that privacy should be easier to implement than it traditionally has been.
That philosophy explains much of Skyflow's strategy. Rather than asking every company to become an expert in privacy engineering, Skyflow aims to provide privacy capabilities as infrastructure. Infrastructure companies often succeed because they remove complexity. Privacy has become one of the most complex challenges in modern software.
Why Hiring Momentum Matters
Skyflow is actively hiring across engineering, product, and go-to-market functions. Viewed through a market-intelligence lens, that signals more than recruitment activity. Infrastructure companies typically expand hiring when demand grows faster than organizational capacity. Privacy infrastructure now sits at the center of multiple technology trends simultaneously: enterprise AI adoption, data governance modernization, regulatory complexity, cybersecurity investment, and digital transformation initiatives.
Hiring activity suggests Skyflow sees long-term demand rather than a temporary surge. Sophisticated operators often watch infrastructure hiring patterns closely because talent allocation frequently reveals future market priorities before product launches or strategic announcements do.
What This Signals for the Industry
The software industry spent the past decade optimizing for scale. The next decade may be defined by optimizing for trust. That does not mean innovation slows down. It means the architecture underneath innovation evolves. Skyflow represents a broader movement toward minimizing data exposure rather than continuously expanding security controls around exposed data. The distinction may sound technical, but it reflects a meaningful shift in how organizations think about risk, compliance, and AI deployment.
Investors including Insight Partners, Canvas Ventures, Foundation Capital, Mouro Capital, and MS&AD Ventures have backed that thesis through Skyflow's growth journey. Publicly reported funding includes a $17.5M Series A and a $45M Series B, reflecting investor conviction that privacy infrastructure will become increasingly important as enterprises deepen AI adoption.
The winners in the AI era may not be the organizations that collect the most data. They may be the organizations that can safely use data while preserving trust.
Frequently Asked Questions
What is Skyflow?
Skyflow is a Palo Alto, California-based privacy infrastructure company that provides a Data Privacy Vault for protecting and governing sensitive data across applications, analytics platforms, and AI systems.
Who founded Skyflow?
Skyflow was founded in 2019 by Anshu Sharma and Roshmik Saha.
What is a Data Privacy Vault?
A Data Privacy Vault is a dedicated platform that stores sensitive information separately from operational systems while controlling access through tokenization, encryption, masking, and policy enforcement.
How does Skyflow support AI applications?
Skyflow helps organizations govern how sensitive data is accessed, processed, stored, and retained by AI systems and AI agents.
What industries use Skyflow?
Skyflow is relevant to financial services, healthcare, insurance, enterprise software, and AI-native organizations that manage regulated or sensitive information.
Why are investors backing Skyflow?
Investors are betting on the growing importance of privacy infrastructure, AI governance, compliance requirements, and enterprise data security as AI adoption accelerates.
How is Skyflow different from traditional security platforms?
Skyflow focuses on isolating sensitive data inside a centralized privacy vault rather than securing copies of that data spread across numerous systems and applications.
Why does privacy infrastructure matter for AI?
AI systems require access to large volumes of data. Privacy infrastructure helps organizations use sensitive information while maintaining security, compliance, governance, and customer trust.
