Case Study

Scaling Cloud Data Protection for Cybersecurity

How Espeo Software strengthened DSX performance and delivered a secure, high-speed Amazon S3 integration

Client overview

The client is a global cybersecurity company from the United States. The organisation applies deep-learning technology to protect data across cloud storage, endpoints and network systems. Its solutions deliver rapid scanning speeds with exceptional accuracy and a minimal false-positive rate. Deep Instinct supports clients across financial services, manufacturing, utilities and the public sector, providing advanced defence against evolving digital threats.

Project details

Technical Debt Challenges

The client needed to modernise its DSX Admin Panel while enabling new cloud capabilities. Legacy architecture and accumulated technical debt slowed delivery, and limited scalability. The client required stable engineering capacity to evolve the product without interrupting operations.

Purpose and Objectives

Espeo Software deployed a dedicated senior engineering team  to strengthen DSX performance, integrate Amazon S3 protection, and improve maintainability. The purpose of the engagement was to ensure consistent delivery, enabled secure, scalable cloud operations.

on-schedule delivery rate
0 %
scanning time per S3 object
< 0 ms
scanning accuracy
> 0 %
Deliver new DSX features without disruption
Integrate Amazon S3 for secure cloud protection
Improve architecture and reduce technical debt
Maintain on-schedule, reliable releases
Ensure full security and compliance standards

How we did it

Discovery & Planning

Work began with close alignment on the DSX roadmap and a full assessment of the platform’s legacy constraints. We set priorities to balance architecture improvements with Amazon S3 integration.

Development

Development focused on extending DSX functionality while strengthening its technical foundation. Secure coding standards guided the Amazon S3 integration for cloud data protection.

Testing

With automated testing and CI/CD pipelines, we ensured consistent quality. Through a  collaboration with quality assurance engineers with  helped confirm performance across all updates.

Deployment

The team released updates using monitoring and rollback options to maintain consistent performance.

Maintenance

Engineers refactored the codebase and strengthened documentation. They focused on steady, incremental improvements that made the platform easier to maintain and ready to scale.

Tech stack & team composition

Angular
Python
PostgreSQL
AWS
GCP
Angular Team Lead
Senior Angular Engineers
Python Tech Lead
Senior Python Engineers
QA Specialists
DevOps Engineer

Results

Faster cloud protection

Amazon S3 scanning time was reduced to under 20 ms.

Improved accuracy

The system reached higher detection accuracy with minimal false positives.

Reduced technical debt

The platform’s architecture was strengthened for future scale.

Favoured by Tech & Business Executives

The standout quality of the team is their thoroughness in addressing and investigating issues.

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