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About
Skyflow AI is a data privacy and security platform purpose-built for the AI era. It provides a centralized vault that governs the flow of sensitive data—including personally identifiable information (PII), payment card data (PCI), and protected health information (PHI)—across an organization's entire technology stack, including AI models, MCP servers, and agentic AI workflows. At its core, Skyflow uses polymorphic encryption and zero-trust architecture to keep data encrypted at rest, in transit, and in memory. Organizations can tokenize and de-identify sensitive data before it reaches large language models (LLMs) or AI training pipelines, dramatically reducing compliance risk during fine-tuning and inference. Key capabilities include privacy-safe analytics that allow data science and marketing teams to work with sensitive data without exposing raw values, a payment vault for offloading PCI compliance, and automated data residency controls that satisfy regulations such as GDPR, PDPA, DPDP, LGPD, CCPA, and CPRA. Skyflow integrates natively with major cloud and data platforms including AWS, Google Cloud, Snowflake, Databricks, ServiceNow, and HubSpot. It is designed for enterprises that need to deploy agentic AI at scale without sacrificing security or compliance. Implementation is measured in hours rather than months, and the platform is maintained by Skyflow so internal engineering teams can focus on product rather than compliance infrastructure.
Key Features
- Runtime AI & MCP Security: Automatically identifies and redacts PII and sensitive data before it reaches LLMs, AI training pipelines, or MCP servers—protecting data at inference time without blocking AI adoption.
- Zero-Trust Polymorphic Encryption: Keeps sensitive data encrypted at rest, in transit, and in memory using polymorphic encryption, enabling privacy-safe analytics and sharing without exposing raw values.
- Global Data Residency & Compliance: Rapidly meets data residency and regulatory requirements for HIPAA, PCI DSS, GDPR, CCPA, CPRA, DPDP, LGPD, and PDPA through a single unified vault.
- Payment Data Vault: Removes PCI-scoped data from your environment entirely, consolidating disparate payment security solutions into one vault to reduce fraud and streamline compliance.
- Agentic AI Governance: Governs the flow of sensitive data across autonomous AI agents and multi-step agentic workflows, applying granular access controls and global policies at scale.
Use Cases
- Securing agentic AI and autonomous AI agent workflows by preventing sensitive PII from flowing into LLM prompts or multi-step agent pipelines at runtime.
- Healthcare organizations protecting PHI and achieving HIPAA compliance without rebuilding their data infrastructure from scratch.
- Fintech and e-commerce companies offloading PCI DSS compliance by vaulting payment card data outside their own environments.
- Global enterprises managing data residency obligations across multiple jurisdictions (EU, India, US, Brazil, Singapore) from a single control plane.
- Data science and marketing teams running privacy-safe analytics on sensitive customer data using polymorphic encryption without exposing raw PII.
Pros
- Fast time-to-value: Customers report going live in hours rather than the 6–12 months required to build equivalent in-house data privacy infrastructure, freeing engineering teams for core product work.
- Broad compliance coverage: A single platform satisfies a wide range of global regulations—HIPAA, PCI, GDPR, CCPA, CPRA, DPDP—removing the need for multiple point solutions.
- Deep ecosystem integrations: Native integrations with AWS, Google Cloud, Snowflake, Databricks, ServiceNow, and HubSpot make it easy to embed privacy controls into existing data stacks.
- AI-era readiness: Built specifically to address the unique risks of agentic AI and LLMs, providing runtime controls that traditional data security tools lack.
Cons
- Enterprise pricing with no self-serve tier: Skyflow is positioned as an enterprise solution with demo-based access, making it inaccessible for smaller teams or developers who need a quick, affordable way to get started.
- Complexity for smaller use cases: The breadth of features and architecture may be overkill for organizations with simple data protection needs or limited regulatory obligations.
- Vendor dependency: Centralizing sensitive data governance in a third-party vault creates a critical dependency; any Skyflow outage or service change could affect production data flows.
Frequently Asked Questions
What types of sensitive data does Skyflow protect?
Skyflow protects personally identifiable information (PII), payment card data (PCI), and protected health information (PHI), as well as intellectual property and other regulated data types across your entire tech stack.
How does Skyflow keep sensitive data out of AI models?
Skyflow identifies and redacts sensitive information at runtime before it reaches LLMs during data collection, training, fine-tuning, and inference. It can tokenize or anonymize data so AI models receive de-identified inputs while downstream systems still function correctly.
Which compliance regulations does Skyflow support?
Skyflow supports HIPAA, PCI DSS, GDPR, CCPA, CPRA, India's DPDP, Brazil's LGPD, Singapore's PDPA, and China's PIPL, among others—allowing organizations to meet multi-jurisdictional data residency and privacy requirements from a single platform.
How long does it take to implement Skyflow?
Many customers go live within hours. Nomi Health, for example, reported running on Skyflow in just hours—compared to the months it would have taken to build equivalent privacy controls in-house.
What platforms and cloud providers does Skyflow integrate with?
Skyflow offers native integrations with AWS, Google Cloud, Snowflake, Databricks, ServiceNow, and HubSpot, and provides APIs that can be embedded into virtually any technology stack.
