Use Case

Data protection on ETL pipelines

Exo protects data moving through ETL pipelines from the source to data warehouses like Snowflake, Redshift, and others. You write the prompts and workflows that drive ETL agents; Adaptive provides the harness, tools, MCP registry, networking, and guardrails.

harness·h-918

Adaptive

$adaptive harness h-918

↳ session opened

harness $etl run --job=customers-to-warehouse

✓ mask: email, phone, ssn

✓ egress: warehouse-prod only

✓ scope: read-only on source

2.1M rows → masked → loaded

teardown: credentials revoked

harness $

pii: masked

egress: scoped

creds: ephemeral

The problem

Data is extremely fluid in organizations. Most organizations use ETL data pipelines to move data from structured data sources such as Postgres and MySQL to data warehouses like Snowflake, Databricks, or even S3 buckets. Protecting data becomes challenging once it reaches its destination, as it is then accessible to all different folks, such as data science and analytics teams.

$4.45 million

The global average cost of a data breach in 2023, marking a 15% increase from the previous year.

78%

Share of organizations storing sensitive data in SaaS applications, heightening data leakage risks.

$401

The average cost per lost or stolen record in 2022, up 2.4% from 2021

Ensuring the masked sensitive data movement through the ETL pipeline is a major challenge for organizations. Without these protection policies, the propagation of sensitive data to various locations increases the risk of leakage. The high number of ETL pipelines in an organization makes it difficult to maintain visibility on the application of protection policies.

The solution

Set Data Protection Policies centrally and ensure only masked data moves to the destination data source.

Adaptive applies masking or tokenization policies to sensitive data, ensuring that only protected data reaches the destination data source. This approach minimizes the exposure of sensitive data and helps organizations remain compliant with privacy regulations by default. By centrally defining protection policies as configurations rather than embedding them in code, organizations gain higher visibility, enabling them to scale these policies across multiple ETL pipelines without errors.

Benefits

How Adaptive helps

Masking and Tokenization Policies

Utilises native database views to apply protection policies such as masking or tokenization.

Protect sensitive data with pre-defined masks without changing the ETL pipeline's workflows or access.

Observability for ETL Pipeline

Comprehensive data observability for ETL pipelines, ensuring real-time monitoring and detailed insights into data flows.

Identify and resolve issues, maintaining the reliability and integrity of data processes.

Drop-in Replacement

Seamlessly integrates with your existing ETL pipeline workflow, requiring no changes to workflow and serving as a drop-in replacement.

Just update the connection string to implement any level of data protection policy