Projects
Technical work at the intersection of security and infrastructure
ML-Based Anomaly Detection in DevSecOps Pipelines
Building a system that detects security threats that rule-based approaches miss by applying machine learning to real-time log analysis.
Tech Stack
Key Features
- Real-time anomaly detection with 30-second detection windows
- 10 attack scenarios designed to evaluate ML detection vs rule-based approaches
- Measuring precision, recall, and false positive rates
- Focus on reducing false positives while maintaining high threat detection
Directly applicable to AI safety monitoring—the same techniques that detect anomalous pipeline behavior can detect anomalous model behavior.
Secure Auto-Scaling AWS Infrastructure
Production-ready AWS infrastructure using modular Terraform—VPC, load balancing, auto-scaling, and monitoring, all as code.
Tech Stack
Key Features
- Multi-AZ VPC with public/private subnets
- Application Load Balancer distributing traffic
- Auto Scaling Group with Launch Templates
- CloudWatch alarms + SNS notifications
- Modular code structure (vpc, ec2, alb, asg, monitoring modules)
- Stress testing to validate auto-scaling behavior
Demonstrates security-first infrastructure design with proper network segmentation, least-privilege IAM, and comprehensive monitoring.
More Projects Coming
Currently working on additional projects in AI security and ML pipeline protection. Check back soon or follow my writing for updates.