search

Preventing data exfiltration in machine learning environments with Amazon SageMaker AI

person By Ajish Abraham
source Source: AWS Architecture Blog
calendar_today
schedule 1 min read

In this post, we demonstrate how iBusiness implemented a three-layered security architecture using Amazon SageMaker AI, virtual private cloud (VPC) endpoints, and Amazon WorkSpaces Secure Browser to prevent data exfiltration while maintaining data scientist productivity. You can adapt this approach to build secure machine learning environments that balance strict data protection with team scalability.

Related articles

architect

Cloudflare WAF protects WordPress applications from two high-severity vulnerabilities

Cloudflare has deployed two WAF rules in response to high-severity vulnerabilities disclosed to us by the WordPress security team. The new rules protect all Cloudflare customers using affected WordPress versions, but customers should still update immediately to a patched release

architect

Eclipse Dataspace Components on AWS: Cost optimization strategies

When you deploy Eclipse Dataspace Components (EDC) connectors on AWS, one of the first challenges you face is predicting and controlling the cost of the required infrastructure. Without clear benchmarks, it is difficult to make informed decisions about workload sizing, environment configuration, and long-term investment. Part 1 of this 3-part blog series covered the fundamentals

architect

Eclipse Dataspace Components on AWS: Architecture patterns in production

Running Eclipse Dataspace Components (EDC) connectors in production on AWS requires deliberate architecture decisions around isolation, managed services, and security layering. In Part 1 of this series, we covered the fundamentals of data space architectures and EDC per the International Data Space Association’s (IDSA) standards. If you are new to EDC, we recommend starting there.