Discover how Skyflow helps build secure AI agents that protect sensitive data and ensure compliance in the modern AI ecosystem.
In this post, we explain how you can use Skyflow with Snowflake or other cloud-based warehouses and analytics data stores to transform sensitive data into non-sensitive data that still keeps the data useful. This removes PII exposure risks while allowing analytical and machine learning operations to work as expected.
Explore advanced de-identification techniques for PII and healthcare data. Learn how Skyflow's encryption and tokenization methods safeguard privacy, reduce breach risks, and maintain workflows, enabling secure data sharing for analytics and AI.
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.
Data anonymization and tokenization are key to protecting sensitive data, but traditional tokenization often falls short, breaking workflows and complicating security. This post tackles the "Austin Problem"—a flaw in conventional tokenization—and shows how a data privacy vault solves it.
As exciting as AWS PartyRock is for building AI apps, it also raises serious data privacy concerns, especially when sensitive information like PII gets involved. In this blog, we’ll explore how you can build privacy-preserving AI applications using a data privacy vault and PartyRock.