Introduction

In an era where auditability, transparency, and compliance are critical, Ramco is modernizing its systems with a scalable and intelligent audit trail architecture. At the heart of this transformation is Elasticsearch, a powerful engine that enables high-performance logging, deep searchability, and advanced analytics.

This article outlines how Elasticsearch is used in Ramco’s audit trail subsystem—highlighting its role in data ingestion, storage, querying, visualization, and our roadmap for security and scalability.

What is an Audit Trail?

An audit trail is a chronological record of events, transactions, or changes that have occurred within a system. It typically includes details such as: User ID, Timestamp, Action performed, Entity affected Audit trails are essential for:

• Security investigations
• Regulatory compliance (e.g., GDPR, SOX)
• Operational debugging
• Monitoring user behaviour

Overview of Ramco Audit Trail

Ramco Audit Trail uses CDC, Kafka, Debezium connectors and Elastic to provide a generic audit trail subsystem which can capture changes in application data based on modelling.

While each of these technologies provide a better scalable means to capture and store audit data, in this article we will look at why we chose Elastic as the data storage in this scheme.

Why Elasticsearch for Ramco Audit Trail ?

Traditional applications use SQL or any database as a storage. But for Ramco Audit Trail, Elastic was chosen as the data store for the following reasons

1. High-Performance Search and Querying

Elasticsearch is built on top of Apache Lucene, offering near real-time search, full-text search capabilities and support for complex Boolean and time-based queries. This makes it ideal for retrieving audit logs quickly, even across millions of records.

2. Scalability

Elastic scales horizontally, supporting clusters with hundreds of nodes and petabytes of data. Audit systems that generate large volumes of logs can rely on Elastic to maintain performance over time.

3. Structured and Unstructured Data Handling

Elasticsearch handles JSON documents natively, allowing it to store structured and semi-structured data without rigid schema definitions.

4. Integration with Kibana

Kibana provides real-time visualization and dashboards, enabling filtering by users or actions or key data. This can be used to view audit data directly although Ramco Audit Trail provides a generalised screen with ability to show context sensitive audit data.

Ramco Audit Trail Data Flow

Elasticsearch: Indexes and stores the audit logs.

How the features of Elastic are used in Ramco Audit Trail

Aggregation based Indexing – Storing data by audit aggregation improves manageability and performance. Appending documents to an aggregation index helps in faster updates. (An audit aggregation is a modelled collection of tables and columns that must be tracked together for most efficient and useful audit data)

Security and Compliance - Use role-based access control (RBAC) in Elastic to restrict data access.

Anomaly Detection & Self-Audit Agents: Leverage Elastic ML to detect unusual patterns such as off-hour logins or access spikes. Extend this with Generative AI-powered self-audit agents that continuously monitor, analyse, and summarize audit data. These agents provide contextual insights, flag potential risks, and support real-time alerts enabling proactive, autonomous audit and compliance checks.

Challenges and Considerations in using Elastic as a data source

While Elastic is powerful, it has the following challenges:

• Storage costs can be overwhelming. Audit data can be voluminous.
• Elastic Queries should be well tuned for faster data fetch
• Data privacy should be ensured by masking sensitive data is masked.

Conclusion

By leveraging the Elastic Stack, Ramco delivers a modern, scalable audit trail solution tailored for enterprise-grade compliance, operational insights, and security. Our ongoing investment in observability and automation ensures this platform evolves with growing data and compliance needs.