Amazon Bedrock's AI solution can transform the way Ripple manages XRP Ledger

Challenge: XRP Ledger is drowning in log data

The XRP Ledger operates as a decentralized layer-1 network with over 900 nodes distributed across universities and business organizations worldwide. Built on the C++ platform to support high throughput, this system comes with a major issue: each node generates 30-50 GB of system logs, totaling approximately 2–2.5 PB of data each time.

When incidents occur, reviewing these logs often takes several days to a week. Engineers need C++ experts to trace anomalies back to the protocol code, which slows response times and impacts network stability.

Solution: AWS Bedrock transforms raw data into useful signals

Ripple and Amazon Web Services are piloting Amazon Bedrock to accelerate analysis. According to AWS architect Vijay Rajagopal, Bedrock acts as a layer that converts raw log data into searchable and analyzable signals. Engineers can query these models to identify behaviors deviating from XRPL’s standard operations.

Internal AWS assessments suggest that incident review processes could be shortened from several days to just 2–3 minutes.

Architecture: Automated AWS pipeline streamlines the entire process

The proposed process includes the following steps:

1. Collection and segmentation: Node logs are uploaded to Amazon S3 via GitHub tools and AWS Systems Manager. Event triggers activate Lambda functions to determine segmentation boundaries for each log file.

2. Parallel processing: Metadata of segments is pushed into Amazon SQS for concurrent processing. A second Lambda function retrieves relevant byte ranges from S3, extracts log lines and metadata, then forwards them to CloudWatch for indexing.

3. Source code and standard linkage: Alongside log processing, the system monitors key XRPL repositories, schedules updates via EventBridge, and stores source code snapshots and protocol specifications in S3.

This critical step allows linking log signatures with software releases and corresponding specifications. Logs alone may not explain special cases, but when combined with server code and protocol documentation, AI agents can map anomalies to precise code paths.

Why this matters

A real-world example: when the Red Sea submarine cable outage affected node connectivity in the Asia-Pacific region, engineers needed to collect logs from multiple operators and process large files before starting review. With Bedrock, this process can be significantly accelerated.

Broader context

This effort occurs as XRPL is expanding its features. Ripple announced Multi-Purpose Tokens—a token design that can be replaced, aiming for better efficiency and tokenization capabilities. The Rippled 3.0.0 release also includes new modifications and security patches.

Current status

As of now, the project remains in research and testing phases. No public deployment date has been announced. Teams are still verifying the accuracy of the AI models and data governance strategies. Progress also depends on node operators’ willingness to share log information during investigations.

Nevertheless, this approach demonstrates that AI and cloud tools can enhance blockchain monitoring without altering XRPL’s fundamental consensus rules.