Threat Detection and Analysis Solution Uses Data Science, Big Data and Self-Learning

Client Background

Our client develops, manufactures, and sells networking hardware for the communication and information technology industry worldwide. The company also offers a security portfolio spanning cloud, email, endpoint, web, and network security, along with advanced malware protection and next-generation intrusion prevention systems.

The client had recently acquired a security startup whose technology identifies attacks before they can expose or corrupt sensitive data. The solution analyzes web traffic and endpoint and network data, then applies machine learning to detect malicious activity.

Business Challenge

The client wanted to scan and detect threats across its ecosystem using network snapshots and data science. But the team lacked in-house expertise to do this and needed partners who could work with the technology's beta versions and still-evolving functionality.

Project Description

The client brought in SoftServe to help build its threat detection and analysis solution, grounded in data science, big data, and self-learning models. The core capability: tracking and detecting anomalies from a snapshot of network traffic.

SoftServe's team handled configuration, infrastructure, and monitoring. The team also led two major migrations: from on-premise infrastructure to AWS, and from a monolithic architecture to microservices.

Working in Scrum, the team consisted of three DevOps engineers, one team lead, and one project manager. They stayed in sync with daily standups, weekly planning sessions, weekly demos, and ongoing communication with the client.

The technical product owner defined requirements, and the SoftServe team translated them into technical tasks across four migration phases:

• Planning
• Proof of concepts (PoCs)
• Production in a cloud
• Traffic switch

Technology Stack

Value Delivered

The client's ambition, detecting threats before they cause damage, required more than a beta product. It required a team that could build the infrastructure around it. SoftServe delivered both: a cloud-native foundation and a migration path from legacy systems to microservices, done in step with the client's own engineers.