Reducing Manual Effort and Improving Risk Detection with AI

Overview

A large enterprise required enhanced visibility across multiple cybersecurity data sources to strengthen vulnerability risk management. ImagineX partnered with the client’s cybersecurity team to design and implement an ML-powered entity resolution platform that automated data linking, reduced duplication, and scaled efficiently to millions of records.

Problem

• Fragmented IT asset data across Qualys, ServiceNow, Archer, and other sources.

• Redundant and inconsistent records lowered data quality.

• Manual deduplication slowed vulnerability risk management workflows.

• Existing systems could not scale to process millions of records efficiently.

Solution

ImagineX designed and implemented an ML-based entity resolution solution using deep learning and natural language models. Integrated with Databricks and Azure Fabric, the system automated asset data linking, reduced duplication, improved data quality, and supported scalable, efficient vulnerability risk management.

Outcome

• Reduced manual effort through AI/ML-powered data linking and deduplication.

• Improved accuracy of vulnerability data, achieving 99.99% on Qualys datasets.

• Enabled processing of millions of records daily in minutes.

• Simplified architecture lowered system complexity and maintenance costs.

Software Engineering

• Deep Learning

• Natural Language Processing

• Technology & System Architecture

• ML Model Customization

• Big Data Engineering

Technology