Cybersecurity - AI/ML Threat Detection Platform

Overview

A rapidly growing cybersecurity startup needed to build their entire AI/ML threat detection capability from scratch to compete with established security vendors, launching intelligent security products including advanced threat detection, vulnerability assessment, and behavioral analytics.

Key Challenges

• Zero in-house machine learning and data science expertise for security analytics

• Need to quickly build and deploy real-time threat detection ML models

• Requirement for scalable MLOps infrastructure supporting continuous model retraining

• SIEM integration and security orchestration automation requirements

• Tight timeline to launch AI-powered threat intelligence products

• Need for explainable AI to support security analyst decision-making

• Compliance with SOC 2 and ISO 27001 security standards

Our Approach

• 1

  Deployed senior ML engineers and Databricks security specialists with cybersecurity domain expertise

• 2

  Architected complete MLOps infrastructure on Databricks platform with security-first design

• 3

  Built real-time threat detection models using anomaly detection and behavioral analysis

• 4

  Implemented automated model training pipelines with adversarial testing and validation

• 5

  Established comprehensive ML model governance framework with audit trails and explainability

• 6

  Integrated with leading SIEM platforms (Splunk, QRadar) for threat intelligence enrichment

• 7

  Developed vulnerability scoring models using deep learning on CVE databases

• 8

  Created security orchestration automation and response (SOAR) integration framework

Key Outcomes

• Built complete AI/ML security analytics platform on Databricks within 9 months

• Launched 5 AI-powered security products: threat detection, vulnerability assessment, user behavior analytics, phishing detection, and incident prediction

• Saved $2 million annually in breach prevention and security operations costs

• Achieved 99% threat detection accuracy with 0.1% false positive rate

• Established fully automated MLOps pipeline with continuous model monitoring and retraining

• Reduced mean time to detect (MTTD) threats from hours to minutes

• Enabled security analysts to process 10x more security events with AI assistance

• Achieved SOC 2 Type II and ISO 27001 compliance for ML platform