Satori for Amazon RDS Aurora

Streamline data security and access in your Amazon Aurora and embrace DataSecOps.

DataSecOps for Amazon Aurora

Satori helps customers streamline access to data in Aurora by automating access controls and security. With Satori for Amazon Aurora, data teams can implement a wide variety of access controls and enforce security and compliance policies without writing code or changing existing data flows.

How It Works

Self-service access to data in your Amazon Aurora database

Whether you are working with self-service access, role-based access controls, or attribute-based access controls, Satori thoroughly manages access without any added code and without modifying your current data flows. With Satori, engineers and other data consumers can access the data they need quickly and without complex user and role configurations while simultaneously ensuring that security and compliance policies are enforced. 

Security, compliance, and privacy operations for Amazon Aurora

Satori seamlessly integrates into your data operations and automatically applies security, compliance, and privacy policies in Aurora. Satori identifies and tags PII and other sensitive data, applies relevant policies, and generates compliance reports. 

Granular security without code

Satori enforces granular security controls such as row-level security, column-level security, and masking and anonymization. Controls are managed through APIs or an intuitive user interface and do not require changes to your clients, data, or Aurora configuration.



Self-Service Data Access

Just-in-time, secure, and compliant data access with automated workflows.

Distributed Data Stewardship

Delegate data access management and assign business owners to datasets.

Data Access Audit

Complete data access audit with identity and data context including built-in reports for compliance and security.

Data Inventory

Autonomous data inventory with built-in data classification.

Data Classification

Out-of-the-box, real-time classification and tagging for sensitive data powered by ML for structured and semi-structured data.

Universal Data Masking

Masking, anonymization, and data reduction profiles applied to known and unknown locations of sensitive data.

Data Access Policies

Implement row-level and column-level security based on attributes such as users, groups, data types, schema, and tables.

Users Directory

Organize your Amazon Aurora and BI users by access groups and scale your RBAC implementation without writing code

User Identification

Identify the real BI user connecting to your Amazon Aurora and apply granular data access policies based on the user’s true identity.


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