Bornio Data Privacy Filters®

Filter sensitive data for all your data driven applications in minutes

Borino de-identifies sensitive production data by creating a version of that data that is privacy policy and regulation compliant, user specific and purpose ready in the right form, making it trustworthy and useful for performing high-quality tasks such as application testing, AI model training and validation, BI analytics, and more.. Bornio Data Privacy Filters are deployed into your own cloud environment hence protecting the data throughout the data flow by applying a policy based on de-identified only to sensitive fields.

HOW IT WORKS

Connect with your data cloud, observe, and classify sensitive data

Through automated observability and discovery, Bornio generates form, shape and semantics preserving transformed data derived from production data. The resulting data is as realistic as possible while shielding the privacy of sensitive data at the source.

Get policy recommendations based on user personas

Persona-based policies can be added to comply with regulations and need-to-know basis. These recommended policies can be modified to meet your custom privacy requirements.

Leverage the rich library of data protection methods

Bornio goes beyond simple masking by offering a flexible framework with a large, ever-growing collection of data protection methods to suit the needs of different usage scenarios. Together they help preserve the characteristics of the original data while maintaining in-column, inter-column and inter table consistency and referential integrity where needed. Advanced features such as Data Hardening involving encryption-decryption-de-identification flows and conditional protection are supported.

Create and Embed Data Privacy Filters in Your Data Cloud

Unlike SaaS solutions that move data for processing to a different compute environment, Bornio is installed into your own cloud environment and Bornio Data Privacy Filters are deployed into data pipelines in your cloud and hence operate where the data exists or flows.

A Power Tool for Data and Privacy Engineers

User-Role Specific

Granular policies for persona specific data, in the right format for the role/user. Designed for need-to-know.

Real-World Data

Privacy-preserving, real-world like data for all data-driven applications

Protection Method Library

Extensive library of protection methods for a variety of PHI, PII data and use cases.

Encoded Regulations

Privacy Regulations such as PCI, HIPAA, GDPR and more are encoded into policy models to enable policy recommendations suited for purpose.

Data Consistency

In-column, Inter-column value consistency, Inter-table referential integrity.

Production-derived

High-fidelity, shape preserving data for AI / ML model training, testing and validation and Analytics.

Deployed in Data Clouds

All common File, Database, Data Lakes, Data Warehouses, Lakehouses and Streaming Data (Kafka).

Centralized control

Centralized, consistent control over the specification of enterprise privacy policies. How each data element in a dataset should be protected is encoded in a privacy policy.

Support for SDLC stages

This brings software engineering rigor to the development, testing and push-to-production of Bornio Data Privacy Policies and Filters using a low-code / no-code approach.

Embed Bornio Filters in Modern Data Clouds

Data for Purpose, with Configurable Filters

Generating synthetic data is expensive, complex

> 40% of time model development in data preparation

Data may or may not represent real world scenarios

Compliant data, preserving shape, format, referential integrity

Non-overlapping data sets that facilitate real-world training, testing

Mathematically-proven algorithms maximise privacy and data quality

Improving model accuracy, reduce training and validation times

Insider threats and risk of data breaches limit access to production data for wide range of data consumers.

The legacy methods of data masking compromise both data a quality and privacy.

Diminishing business insights and operational efficiency.

Impacting model training, validation and accuracy

Granular policies per data consumer personas on need-to-know basis.

Rich collection of privacy protection methods – from simple to sophisticated, to meet the most stringent regulatory needs.

Policy makers and data owners can formally review, validate and sign-off

Continuous compliance with monitoring, logging and risk monitoring

Manually labored dummy or fake data does not represent production scenarios accurately

Developers and SREs struggle in automating CICD

Elongating application development life cycle

Impacting model training, validation and accuracy

Production-derived data maintains high-fidelity to production data in terms of form, shape and semantics preservation

Shape data sizes (subset, supersets), entity-relations tracing

In-column, Inter-column and inter-table value consistency and referential integrity preserves data relationships in production data.

Experience Data Privacy Filters® in your own data cloud.