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Partner integration

Protegrity & Snowflake

Native protection for analytics and AI. Protegrity applies field-level controls directly in Snowflake so teams can query, share, and build models on governed data—without sacrificing speed or compliance.

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  • Protegrity Native

    The integration is native to Protegrity and offers a more seamless experience when applying this platform.

  • Integration type

    • Analytics

  • Partner

    Yes

  • Supported platforms

  • Use cases

    • Agentic Pipeline Protection & Runtime Enforcement
    • Cloud Migration & SaaS Integration
    • Internal Data Democratization & External Data Sharing With Partners/Vendors
    • Privacy-enhanced Training Data for AI/ML Models
    • Prompt Input Filtering & Output Curation for GenAI Systems
    • Regulatory Compliance & Data Sovereignty

Overview

The Protegrity and Snowflake integration empowers data-driven enterprises to unlock secure, scalable analytics across the Data Cloud by combining Protegrity’s granular data protection with Snowflake’s seamless data sharing and multi-cloud flexibility. Dynamic protection is applied directly within Snowflake’s native architecture, ensuring sensitive information remains governed while enabling secure collaboration across business units, partners, and geographies.

This joint solution removes barriers to cloud adoption, accelerates time-to-insight, and simplifies compliance for regulated industries such as financial services, healthcare, and the public sector—delivering a future-ready foundation where data is persistently protected, accessible, and actionable at scale.

Key Integration Feature

The Protegrity + Snowflake integration delivers native, fine-grained data protection within the Snowflake Data Cloud, enabling secure analytics and regulatory compliance without compromising performance or accessibility. 

Features & Capabilities

Explore how Protegrity keeps sensitive data usable—and safe—across your Snowflake workloads.

  • 01

    Granular Data Protection: Policy-based security at the column and field level 

    Why It Matters

    Ensure sensitive data such as PII, PHI, and financial information is protected while maintaining usability for analytics and operations.

    How it Works

    A global bank uses Protegrity within Snowflake to tokenize account numbers, allowing analysts to run aggregate queries without ever exposing raw data.

  • 02

    Seamless Integration with Snowflake Data Cloud: Security without disruption 

    Why It Matters

    Protegrity integrates directly into Snowflake’s architecture, enabling encryption, masking, and tokenization with minimal performance overhead.

    How it Works

    Customers report maintaining sub-second query performance even when working with protected data sets.

  • 03

    Secure Data Sharing Across Enterprise: Enable collaboration without risk

    Why It Matters

    Share data safely with internal teams, subsidiaries, or external partners by applying consistent protection policies across all Snowflake workloads.

    How it Works

    A healthcare provider securely shares de-identified patient data with research partners to accelerate clinical trials while staying HIPAA-compliant.

  • 04

    AI & Analytics Enablement: Unlock insights without exposing sensitive data 

    Why It Matters

    Protegrity-protected data can be safely used for advanced analytics, reporting, and AI/ML model training without regulatory exposure.

    How it Works

    Retail organizations analyze tokenized customer purchase histories in Snowflake to drive personalization initiatives without risking PCI violations.

  • 05

    Compliance Simplification: Streamlined adherence to global regulations

    Why It Matters

    Align with GDPR, HIPAA, PCI-DSS, and other data privacy mandates through consistent application of data protection policies inside Snowflake.

    How it Works

    Enterprises reduce audit preparation time by 40% through automated reporting of Protegrity’s data protection policies applied in Snowflake.

Architecture &
Sample Data Flow

At the core of Protegrity’s Snowflake integration is an architecture designed to deliver persistent, field-level data protection within Snowflake’s cloud-native environment. Sensitive data is safeguarded the moment it enters the Snowflake Data Cloud—whether ingested from on-premises systems, cloud applications, or third-party sources—and remains protected throughout its entire lifecycle. 

Protegrity integrates seamlessly with Snowflake’s elastic, multi-cluster compute model, enabling encryption, tokenization, and dynamic data masking to be applied natively within data pipelines and workloads. This ensures that governed data can be securely queried, transformed, and shared—without compromising performance or business agility.

The data journey
Visualizing the data journey
The data journey explained
  • 01
    Data ingestion

    Protegrity-enabled connectors capture structured and semi-structured data from operational databases, cloud applications, and data lakes. As data enters the Snowflake Data Cloud, sensitive fields are automatically identified and prepared for protection.

  • 02
    Data Protection & Transformation

    Protegrity applies encryption, tokenization, or dynamic masking to sensitive fields during ingestion or transformation processes. Policies are centrally defined and enforced via Protegrity APIs, ensuring consistent governance regardless of source or workload.

  • 03
    Data Delivery

    Authorized Snowflake users can query, join, and analyze governed datasets without needing access to sensitive raw values. Protected data remains fully usable for BI dashboards, reporting, and advanced analytics.

  • 04
    AI/ML Enablement

    Protegrity allows de-identified or tokenized datasets to be securely exported for AI/ML pipelines. This empowers organizations to innovate with predictive models while maintaining compliance with GDPR, HIPAA, PCI-DSS, and other regulations.

  • 05
    Data Sharing & Collaboration

    Snowflake’s secure data sharing features are enhanced by Protegrity’s persistent protection. Enterprises can confidently share governed datasets across business units or external partners, knowing that sensitive fields remain protected.

  • 06
    Monitoring & Auditing

    All protection activities are logged for visibility and compliance reporting. Automated alerts and audit trails help enterprises demonstrate adherence to data privacy mandates and internal security policies.

DEPLOYMENT

Deployment of Protegrity Data Protection with Snowflake involves embedding Protegrity’s protection services into the Snowflake Data Cloud environment to ensure secure handling of sensitive data across its lifecycle. The process begins with provisioning Protegrity components—such as policy servers, tokenization engines, or API gateways—either in the customer’s cloud environment or as part of a hybrid model, depending on performance and compliance requirements.

Data protection policies are centrally defined and managed within the Protegrity platform, giving security teams the ability to enforce encryption, tokenization, masking, and granular access controls directly in Snowflake. As data is ingested into Snowflake from external sources (such as SaaS platforms, operational databases, or data lakes), Protegrity connectors and APIs intercept sensitive fields and apply persistent protection. These policies ensure that customer, financial, or healthcare data is secured at rest, in motion, and during query execution.
To support elasticity and performance at scale, the deployment leverages public cloud functions (such as AWS Lambda, Azure Functions, or Google Cloud Functions). These serverless components execute Protegrity protection services in parallel, automatically scaling to handle bursts of ingestion or analytics workloads without manual intervention. This parallelized architecture enables organizations to process and protect millions of records efficiently while minimizing latency.
The deployment model is designed to align with Snowflake’s multi-cluster shared-data architecture, ensuring that Protegrity’s components scale dynamically to support high query volumes and large-scale analytics. Integration with Snowflake’s role-based access controls (RBAC) ensures that protection policies map seamlessly to enterprise security models. In addition, auditing and logging from Protegrity provide visibility for compliance teams, while the underlying cloud provider’s monitoring services (e.g., CloudWatch, Stackdriver, Azure Monitor) add operational assurance.
This architecture allows organizations to confidently use Snowflake as a central analytics and data-sharing platform while ensuring that PII, PHI, or regulated financial data remains protected. By combining Snowflake’s agility with Protegrity’s persistent data protection and the scalability of public cloud functions, enterprises can meet regulatory requirements, reduce compliance costs, and accelerate innovation without compromising security.

RESOURCES

Quick reads and deep dives to help your team plan, deploy, and scale Protegrity with Snowflake—securely and without slowing analytics or AI.

Protegrity Reference Architecture for AWS

Diagram and guidance for deploying Protegrity with Snowflake on AWS—components, data flow, key management, and scaling patterns for secure, high-performance analytics.

READ MORE

Solution Brief

A concise overview of the joint Protegrity + Snowflake solution—how native, field-level protection enables secure analytics and AI, key benefits, and core use cases.

READ MORE

Case Study

See how a regulated enterprise used Protegrity in Snowflake to protect PII, simplify audits, and keep BI and data-science workloads fast and compliant

READ MORE

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