Sure! The translation in American English would be: “Secure and Distributed Ledger in Scalable Deployments with Multiple Accounts Using Amazon Bedrock and LangChain.”

The growing concern over data privacy has led software companies to adopt stricter solutions in information management. In this context, the implementation of multi-account architectures on platforms like Amazon Web Services (AWS) has established itself as the gold standard for ensuring the security and confidentiality of customer information. This model allows each customer to have their own account, facilitating data segregation and preventing leaks between different users.

However, the transition to a multi-account environment is not without complications, especially with the arrival of generative artificial intelligence technologies, such as those offered by Amazon Bedrock. Managing access control and operational visibility becomes particularly challenging when handling numerous accounts, which may further complicate the observability needed to optimize AI performance.

A proposed solution involves creating a dedicated operations account that centralizes the management of these processes, allowing customer data to flow through managed services and be stored only in their individual accounts. This approach not only ensures that data boundaries remain clear but also reinforces privacy.

Logging data, a crucial element for ensuring regulatory compliance, presents its own challenges. Although Amazon CloudWatch provides a log of invocations, there may be risks associated with exposing customer data in the operations account. Thus, there is a need to maintain these logs in the customer accounts.

The strategy, therefore, suggests securely managing logs distributed across multiple accounts, transferring invocation logs to customer accounts. This allows companies to centralize AI operations without compromising data privacy. Using the AWS Security Token Service for customer accounts to assume specific roles in the operations account is key to securely managing this data.

The success of this strategy also depends on the shared responsibility model offered by AWS. While AWS handles infrastructure and services, customers are responsible for protecting their own data through appropriate controls and logging strategies. By implementing these best practices, organizations not only comply with privacy standards but can also scale their AI operations efficiently and securely.