From Data Silos to Smart Answers: Rapyder Powers Seclore’s AI-Ready Data Lake on AWS

About Customer:

Seclore is a global leader in data-centric security, helping organizations protect, track and control sensitive information wherever it travels. Its platform enables secure collaboration, continuous monitoring and simplified compliance without disrupting business operations. Seclore serves enterprises and government agencies across financial services, engineering, education and other sectors.

Industry:

Industries, IT- ITeS

Offering:

Gen AI/AIML

Business Challenges:

1. Seclore’s structured and unstructured data was spread across HRMS, Active Directory, Microsoft 365 and regulatory sources. With no unified view, teams struggled to see the complete picture or trust that they were working from complete, current data.
2. A multi-tenant platform demanded strict data isolation between tenants, which ad-hoc integrations could not guarantee.
3. Seclore’s obligations span GDPR, CCPA, TiSAX, RBI and IRDAI. Periodic reporting only revealed gaps at audit time, whereas the business needed continuous visibility into its compliance posture.
4. Answering risk and compliance questions meant manual analysis across sources rather than simply querying one trusted source. This slowed decisions and pulled skilled teams into data gathering.
5. Seclore needed SQL-style queries over structured data as well as semantic search over documents, but no single approach covered both, limiting the questions teams could ask of their data.
6. Business users needed dashboard connectivity and fast query execution, but not at the expense of security. Performance and access could not come from loosening guardrails.
7. The architecture had to be cloud-native and swappable so that it could be deployed across different environments and evolve with the business without rework.

Solution Implemented:

1. Rapyder built a centralised AWS data lake that integrates data from HRMS, Active Directory and Microsoft 365. Amazon API Gateway and AWS DMS enable ingestion and continuous synchronisation, while Amazon S3 securely stores unstructured files and logs at scale.
2. Rapyder deployed the entire solution inside an Amazon VPC for network isolation and enforced tenant separation with role-based access control.
3. Rapyder set up continuous monitoring of ingestion, query and access activity using Amazon CloudWatch and AWS CloudTrail. Query results are passed through Amazon Bedrock models to produce compliance-aligned insights in natural language.
4. Rapyder built Retrieval-Augmented Generation (RAG) workflows in which user queries are enriched with relevant context from the data lake before Amazon Bedrock generates a response, so questions can be asked in plain language and answered directly.
5. Rapyder generated embeddings for unstructured content with Amazon Bedrock and indexed them in Amazon OpenSearch to enable semantic search. Incoming queries are routed through Amazon API Gateway to workloads on Amazon EKS, which decide dynamically whether to retrieve from OpenSearch or from the synchronised structured sources.
6. Rapyder ran query workloads on Amazon EKS behind Amazon API Gateway for scalable, high-performance execution, while keeping every request inside the VPC and role-based access boundaries, backed by AI guardrails.
7. Rapyder designed a modular, cloud-native architecture on Amazon EKS and managed AWS services. Prompts are centralised in Amazon S3 so they can be updated without redeploying the application.

Services Used:

• Amazon API Gateway
• Amazon EKS
• Amazon DynamoDB
• Amazon Bedrock
• Amazon OpenSearch
• AWS DMS
• Amazon S3
• Amazon CloudWatch
• AWS CloudTrail
• Amazon EventBridge

Business Benefits:

1. Unified data access & operational efficiency – 60–70% reduction in manual data consolidation effort (analyst hours/week); data-to-insight time cut from days to hours
2. Enhanced security & data isolation – Zero cross-tenant data incidents post-deployment; 100% tenant isolation coverage (VPC + RBAC audit pass rate)
3. Stronger compliance & governance – ~50% reduction in audit-prep effort (weeks → days); continuous compliance monitoring vs. point-in-time checks
4. Faster, smarter decisions – Query-to-answer time reduced from hours to seconds/minutes (NLP chat vs. manual report requests)
5. One intelligent query experience – Single interface replacing 3–4 disparate tools/dashboards; ~40% drop in “where do I find this data” support tickets
6. Improved user experience – 70–80% reduction in ad-hoc data-request turnaround (self-serve dashboards/chat vs. request queue)
7. Scalability with reliability – Zero-downtime prompt/config updates (no redeployment); linear tenant onboarding without performance degradation

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