Expertise
Technical skills and certifications across DevSecOps and AI Security
Technical Skills
Cloud & Infrastructure
- AWS (EC2, S3, RDS, Lambda, VPC, IAM, CloudWatch, EKS)
- Terraform (Infrastructure as Code)
- Kubernetes & Docker
- Container Orchestration
- Auto-scaling & High Availability Architecture
CI/CD & Automation
- GitLab CI/CD Pipelines
- Automated Deployment & Configuration
- Security Scanning Integration (SAST, DAST)
- Dependency Scanning
- Container Scanning
Networking & Security
- Network Security Architecture (VPC Design, Security Groups, NACLs)
- SIEM Tools & Security Monitoring
- Compliance Frameworks (ISO27001, PCI DSS, GDPR)
- Incident Response
- Vulnerability Assessment
Monitoring & Observability
- Graylog
- Zabbix
- CloudWatch
- Log Analysis & Alerting
- Performance Monitoring
Systems & Programming
- Linux Administration
- Nginx
- APISIX (API Gateway)
- Python
- Bash Scripting
- Machine Learning (Isolation Forest, Anomaly Detection)
Certifications
Current
AWS Certified Solutions Architect – Associate
Amazon Web Services
AWS Certified Cloud Practitioner
Amazon Web Services
CompTIA Security+ CE
CompTIA
Google Cybersecurity Certificate
In Progress
AWS Security Specialty
Target: 2026
CompTIA SecAI+
Target: 2026
Current Focus
My current work sits at the intersection of traditional DevSecOps and emerging AI security challenges. I'm particularly focused on:
- ML Pipeline Security—Securing the full machine learning lifecycle from training data to production inference.
- Anomaly Detection—Applying machine learning to detect security threats that rule-based systems miss.
- LLM Security—Understanding and defending against prompt injection, jailbreaking, and other LLM-specific attacks.