How to Ensure Compliance for Amazon RDS

Ensuring compliance in Amazon RDS environments is essential to safeguard sensitive information and meet strict regulatory requirements. Amazon RDS, supporting databases like PostgreSQL, MySQL, SQL Server, and others, offers native features for real-time auditing, dynamic data masking, discovery, and security. Enhancing these capabilities with solutions like DataSunrise further optimizes compliance, efficiency, and risk reduction.

Explore Data Compliance and Regulatory Compliance to learn more about robust frameworks.

Native Compliance Capabilities in Amazon RDS

Real-Time Audit Setup

For Amazon RDS for PostgreSQL, auditing leverages the pgaudit extension, allowing comprehensive activity tracking.

Enable pgaudit by modifying the DB parameter group:

pgaudit.log = 'all'
pgaudit.role = 'rds_pgaudit'
log_statement = 'none'
log_connections = 1
log_disconnections = 1

Then create an audit role and grant it to the necessary users:

CREATE ROLE rds_pgaudit;
GRANT rds_pgaudit TO myuser;

After setting up the audit role, configure your RDS instance to apply these parameters by associating the modified parameter group.

To review audit logs, query the PostgreSQL logs stored in Amazon CloudWatch:

SELECT * FROM pg_catalog.pg_log WHERE log_time > now() - interval '1 day';

This setup provides detailed logging for DML, DDL, and role changes, enabling compliance with GDPR, HIPAA, and PCI DSS.

For deeper insights into building centralized audit collections, AWS provides guidance on building a centralized audit data collection for Amazon RDS for PostgreSQL using Amazon S3 and Amazon Athena.

Dynamic Data Masking

Dynamic masking hides sensitive data dynamically without altering the underlying storage. For PostgreSQL on RDS, dynamic masking is implemented using views:

CREATE VIEW masked_users AS
SELECT id,
       username,
       CONCAT('***', RIGHT(email, 4)) AS masked_email,
       CASE 
           WHEN LENGTH(phone) > 4 THEN CONCAT('****', RIGHT(phone, 4))
           ELSE '****'
       END AS masked_phone
FROM users;

You can control access to the original table by granting SELECT privileges on the masked view only.

This protects PII while maintaining application functionality, as described in the dynamic data masking guide for Amazon RDS PostgreSQL and Aurora.

Data Discovery and Classification

Discovering sensitive data in RDS instances can be achieved through integration with Amazon Macie. Macie identifies and classifies sensitive data types like PII and PHI automatically, as shown in the guide on enabling data classification for Amazon RDS with Macie.

Additionally, broader data discovery across your RDS ecosystem is possible with AWS DataZone.

Example query to manually check for common sensitive data patterns:

SELECT table_schema, table_name, column_name
FROM information_schema.columns
WHERE column_name ILIKE '%ssn%' OR column_name ILIKE '%credit%';

This approach allows prioritization of tables for further review and classification.

Security Best Practices

Amazon RDS recommends encryption, access control, and activity monitoring. Best practices for securing RDS databases are detailed in the AWS encryption best practices for RDS and the security best practices guide for Amazon RDS MySQL and MariaDB instances.

For example, enable encryption at rest by creating an encrypted RDS instance:

aws rds create-db-instance \
    --db-instance-identifier mydb \
    --allocated-storage 20 \
    --db-instance-class db.t3.micro \
    --engine postgres \
    --master-username admin \
    --master-user-password secret99 \
    --storage-encrypted

Always enforce SSL/TLS connections by setting the parameter rds.force_ssl=1 in your parameter group.

Optimizing Compliance with DataSunrise

While AWS native features are powerful, extending compliance automation with DataSunrise Data Audit and DataSunrise Compliance Manager provides enterprise-grade control.

Real-Time Audit Enhancement

DataSunrise supports real-time monitoring across Amazon RDS PostgreSQL and other engines, offering:

• Continuous Audit Logs

• Customizable Audit Trails

• Smart Learning Audit Rules adapting to behavioral patterns

This approach enables instant anomaly detection, minimizing risk exposure.

Dynamic Masking Precision

Dynamic data masking in DataSunrise applies "Surgical Precision Masking" techniques as detailed in the Dynamic Data Masking guide. Masking policies automatically adapt to:

• Roles

• Query contexts

• Specific database columns

This flexible setup enhances protection against insider threats while maintaining user experience.

Advanced Data Discovery

DataSunrise automatically discovers sensitive data using NLP and OCR capabilities, as outlined in the Data Discovery article. This goes beyond basic tagging, identifying hidden sensitive fields across structured and unstructured data.

Strengthened Security Controls

Deploying DataSunrise Security Rules ensures advanced defense against SQL injections, suspicious behaviors, and unauthorized access attempts. The Role-Based Access Control framework ensures that access permissions align strictly with user duties.

Explore more about Data-Inspired Security to understand how threat detection enhances compliance.

Compliance Automation

DataSunrise’s Compliance Autopilot continuously calibrates rules against frameworks like GDPR, HIPAA, and PCI DSS, eliminating manual drift.

It automatically generates Audit-Ready Reports to streamline evidence preparation for regulatory audits.

Conclusion

Applying real-time auditing, dynamic masking, and comprehensive discovery natively in Amazon RDS provides a strong compliance foundation. However, combining these native features with DataSunrise's Autonomous Compliance Orchestration elevates security, reduces compliance gaps, and enhances operational efficiency.

Ensure your Amazon RDS compliance journey is effortless and audit-ready. Learn more and schedule a demo with DataSunrise today.