Exoft partnered with a cybersecurity company to develop and scale an enterprise network deception platform. Our team handled platform architecture, frontend and backend development, multi-tenant databases, license management, DevOps, and QA. The MVP was delivered in three months, and the platform later reached 400+ customers worldwide.

Client: Cybersecurity Provider

Our client is a cybersecurity company developing enterprise solutions for threat detection and network protection. Its products help organizations identify suspicious activity and respond to potential attacks.

Challenge: Building a Security Platform from Scratch

The client had a product vision and technical requirements but needed an engineering partner to build the platform from scratch. The development involved several challenges:

  • Emerging technology. Network deception was a niche cybersecurity field with few established implementation practices.
  • Complex architecture. The platform needed secure multi-tenant infrastructure, integrations with security tools, and support for SaaS and on-premises deployments.
  • Enterprise usability. Security analysts needed a central interface to manage deception grids and investigate threats.
  • Scalability. The architecture had to support new features, customer growth, and frequent releases.

Cooperation: From MVP to Enterprise Product

The client’s internal experts focused on deception technology, network intelligence, and malware analysis. Exoft handled the architecture, engineering, QA, and maintenance of most customer-facing platform components.

Our team translated requirements into technical solutions, planned future releases, optimized databases, and managed deployments. Regular security audits, penetration testing, and automated testing were part of the development process.

At its largest, the team included 16 engineers and specialists across backend, frontend, QA, architecture, DevOps, and project management.

Project Stages

  • MVP Development: We built the first version for network traffic analysis and threat detection. It collected and correlated network events to identify connections with known malicious servers, botnets, and trojans.
  • Deception Platform Development: After the first investment, the client introduced an automated deception grid to detect attackers moving through corporate networks. We developed its management components, optimized databases, and automated deployment processes.
  • Enterprise Scaling: As adoption increased, we improved platform performance, maintained the codebase, and documented the architecture.

Solution: A Core Network Deception Platform

Deception Grid Management Console

We built a central interface for configuring deception grids, monitoring network activity, investigating threats, and managing deployments across enterprise environments.

Multi-Tenant Database Architecture

We designed a database architecture that separated customer environments and supported reliable security data processing. We used stored procedures, triggers, functions, transactions, and database events to optimize performance.

License Management System

We developed a licensing system to manage product activations, customer entitlements, and license usage. It also provided the client with visibility into deployments across customer environments.

System Services and Deployment Orchestration

We built system services and an orchestration layer for on-premises and SaaS deployments. Virtualization and automated deployment processes made releases easier to manage and reduced maintenance downtime.

Results: 400+ Enterprise Customers

The platform grew from a three-month MVP into an enterprise cybersecurity product adopted worldwide.

  • 3 months to deliver the MVP, on time and within budget.
  • 400+ customers across enterprise and government organizations.
  • 60% of early customers secured before Series A funding.
  • ~1 minute of update downtime, down from several hours.
  • 15+ releases over 10 years.
  • 24/7 support for production environments.
  • Global adoption across all continents.