Title

AWS re:Invent 2022 - [NEW LAUNCH!] Automate data discovery with Amazon Macie (SEC215)

Summary

Introduction: Himanshu Verma, leader of a team of worldwide security specialists at Amazon, introduces the session on Automate Data Discovery with Amazon Macie, joined by Anuj Gupta, a principal solutions architect, and Carlos Carlos, Director of Data Security at OPPORTUNE.
Data Protection Needs: The session discusses the challenges organizations face in identifying sensitive data to comply with various data protection regulations like GDPR and CCPA.
Amazon Macie Overview: Amazon Macie, relaunched in 2020, uses machine learning and pattern matching to discover sensitive data at scale, integrated with Amazon S3, and provides an interactive dashboard for data security posture.
Automated Sensitive Data Discovery: A new feature launched at re:Invent 2022, enabling automatic discovery of sensitive data across all S3 buckets in an AWS account, reducing the need for manual scans and significantly lowering costs.
Customer Story: Carlos Carlos shares OPPORTUNE's experience with Macie, emphasizing the importance of finding sensitive data, reducing risk, and improving data security posture.
Key Takeaways: The session concludes with recommendations to opt into the new feature, leverage managed data identifiers, define custom identifiers, and take action on the continuous visibility provided by Macie.

Cost Optimization: The new feature of automated sensitive data discovery is designed to be cost-effective, reducing the need for full bucket scans and offering a 30-day free trial.
Customization: Organizations can tailor Macie's capabilities by using managed data identifiers for common data types or creating custom identifiers for organization-specific sensitive data.
Integration and Automation: Macie's findings can be integrated with AWS Security Hub for a centralized view of security posture and can trigger automated workflows using Amazon EventBridge.
Continuous Monitoring: The service emphasizes the need for ongoing monitoring and remediation, as sensitive data can continuously enter S3 buckets.
Real-World Application: OPPORTUNE's use case demonstrates the practical benefits of Macie in a regulated financial environment, highlighting improved accuracy, reduced time for data discovery, and prioritized risk remediation.
Future Expansion: There is an interest in expanding Macie's capabilities to other data sources beyond S3, indicating a potential direction for AWS to enhance data discovery and governance across a wider range of services.