Skyflow for GenAI

Innovate fast without worrying about sensitive data exposure, compliance, or diverting resources from core product focus.

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Top Challenges in GenAI Privacy

Access to Sensitive Data and IP

AI need limits around access and use of sensitive data throughout the entire lifecycles.

Building and Training Safe AI Agents

Businesses must pair models and applications with privacy guardrails to build safe agents.

Compliance and Governance

Staying compliant and in control of data amid growing, nuanced standards around PII is critical.

How Skyflow Helps

Adopt best-in-class models or build your agents while protecting sensitive data to safely improve internal business productivity, external customer experience, and more.

Easily De-identify and Re-identify

Detect and redact sensitive data and intellectual property automatically during data collection, model training, fine-tuning, RAG, and inference. Easily re-identify the data for use.

Govern Access to Sensitive Data

Protect sensitive data from unauthorized access, breaches, and data leaks with fine-grained access controls around who gets access to which data and for how long.  

Radically Simplify Compliance

Adopt up-and-coming legislations quickly while complying with data residency requirements, such as GDPR, the EU AI Act, DPDP, and others.

Build Privacy-Preserving Agents

Detect, isolate, and protect sensitive information flowing into and out of generative AI agents, allowing them to make decisions and execute actions without compromising privacy.
Try Skyflow

Skyflow in Action

Skyflow protects your most sensitive customer PII. Run secure workflows and execute custom code to extract, protect, and process sensitive data in structured and unstructured formats. Automatically de-identify sensitive data and re-identify it when a specific person needs access.

Secure PII Throughout the LLM Lifecycle

Skyflow helps you secure during the end-to-end LLM lifecycle, including training, fine tuning, RAG, data re-identification, and sharing data with third parties and outside models.

Isolate. Protect. Govern.

Skyflow is a data privacy vault built to radically simplify how companies isolate, protect and govern their most sensitive data. Skyflow customers span verticals like fintech, retail, travel, and healthcare and use the data privacy vault architecture to comply with data residency laws, keep sensitive data out of LLMs, govern access to PII, and more.

  • Data Residency
  • Compliance
  • Data Governance
  • Tokenization and Polymorphic Encryption
  • Data Security
  • Secure Data Sharing
  • API-first
"Companies are eager to adopt ChatGPT and other generative AI platforms but they need to solve for privacy and regulatory compliance. Like we laid out in our seminal paper on the future of privacy engineering, data privacy vault architecture is a right way to go about this."

Joseph Williams

Global Partner in Cybersecurity and Privacy,Infosys
"We were able to successfully deploy Skyflow in less than three weeks with the zero-trust vault architecture, and our total cost of ownership decreased by 67%."

Nitin Shingate

CTO, GoodRx
“We were up and running on Skyflow in just hours, rather than the months it would take to build and implement even a fraction of this data privacy rigor.”

Boe Hartman

CTO, Nomi Health and former CTO, Goldman Sachs
“It would take 3 engineers at least 6-12 months to build the basics of this solution internally, and 2 engineers to maintain it. Beyond hiring and talent costs, we’d also need to bring on consultants to advise on compliance requirements. At the end of the day, building in house would have drastically slowed our time to market. Skyflow made everything easy.”

Johnny Mitrevski

CTO, Scalapay

LLM Best Practices from Experts

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Private LLMs are trending as a solution for AI privacy concerns, yet they may not fully safeguard our data as hoped. Discover their data protection promise and the limits in this article.

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How to Protect, Secure, and Use Unstructured Data

Unstructured data, which makes up approximately 80 to 90% of all data, has remained largely untapped due to lack of proper tooling. With the introduction of data lakes and lakehouses in the past decade, and more recently LLMs, organizations have begun unlocking the potential of this data.

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