Rahul Athreya

DevOps Engineer, Cybersecurity Technologist & Founder of AutoBug AI — building and securing cloud-native infrastructure with automation, observability, and threat awareness. I’m open to data center operations, DevOps, system administration, cloud operations, or security analysis roles. I bring hands-on experience with Kubernetes, CI/CD, Cloudflare Tunnel, SIEM tools like Splunk, and founder-level experience building AutoBug AI, an AI-powered penetration testing dashboard and cybersecurity workflow platform for authorized testing, real-time activity visibility, action approvals, and findings review.

About Me

Hello! My name is Rahul Athreya. I'm a DevOps and cybersecurity technologist with a bachelor's degree in Information Technology from Arizona State University. I've designed and deployed Kubernetes infrastructure on Raspberry Pi hardware, implemented containerized app hosting, monitoring with Prometheus and Grafana, and secure tunneling via Cloudflare Tunnel. I’ve also integrated Splunk for log monitoring and basic SIEM functionality, using it to detect anomalies and maintain infrastructure visibility. My project work spans DevOps, system administration, and security—what would traditionally take multiple specialized teams. Most recently, I founded AutoBug AI, which pushed me deeper into cloud deployment, product architecture, security workflow design, and technical leadership. My goal is to bring this full-stack experience to a security-conscious infrastructure, operations, or DevOps role.

AI Penetration Testing Dashboard

Founder Work: AutoBug AI

AutoBug AI is an AI-powered penetration testing dashboard and cybersecurity workflow platform for authorized security testing. The product is designed to help users monitor real-time agent activity, approve security-testing actions, and review findings in one place.

What it is

A security operations workspace that combines AI assistance, controlled testing workflows, and a dashboard for reviewing activity and findings.

Why it matters

It focuses on visibility and human control: users can follow what the system is doing, approve actions, and use evidence to support validation and reporting.

What I built

I worked across product architecture, cloud deployment, infrastructure troubleshooting, frontend/backend coordination, sandbox workflow design, and security-focused user experience.

Building AutoBug AI has strengthened my ability to learn quickly, troubleshoot complex systems, lead through ambiguity, and connect technical execution with real operational needs.

Visit AutoBug AI

Certifications

Google Cybersecurity Certificate – June 2025
Google IT Professional Certificate – June 2025
Python Programming Certificate – August 2024
C Programming Certificate – August 2024
AWS Cloud Practitioner Essentials – June 2024
AWS Security Fundamentals – July 2024

Projects

AutoBug AI — AI Penetration Testing Dashboard & Cybersecurity Workflow Platform

Founded and built AutoBug AI, an AI-powered penetration testing dashboard for authorized security workflows. The platform is designed around real-time activity visibility, user-approved actions, findings review, and evidence-centered validation. The project has involved cloud infrastructure, backend/frontend integration, sandbox workflow design, deployment troubleshooting, and customer feedback-driven product iteration.

DevOps & Infrastructure

Built a cloud-native portfolio website using Kubernetes on Raspberry Pi devices. Configured CI/CD pipelines using Woodpecker CI and GitHub, automated deployments with Ansible, secured access with Cloudflare Tunnel, and replaced cloud hosting with self-managed infrastructure.

Cybersecurity & Monitoring

Developed IP scanners, network diagnostics, and traffic-simulation scripts in controlled lab environments. Integrated Splunk to monitor logs from the Raspberry Pi Kubernetes cluster, simulating SIEM functionality. Applied Zero Trust principles using Cloudflare Tunnel for secure ingress.

Skills

Programming: Fortran, Python, Java, JavaScript, C, HTML5, CSS, Swift, Bash

Infrastructure & DevOps: Kubernetes, Docker, CI/CD with Woodpecker, GitHub Actions, AWS, EC2, RDS, S3, Coolify, Prometheus, Grafana, Cloudflare Tunnel

IT/Cyber Tools: Arduino, Raspberry Pi, Kali Linux lab environments, Splunk, Linux terminal, network diagnostics, Cloudflare Zero Trust, RClone

GitHub Projects: Discord bots, IP scanners, IP lookup, controlled lab traffic testing, proxy tools, server management, VMware ESXi, website hosting, AWS & self-hosting

Productivity: Microsoft Excel, Word, PowerPoint, and Outlook

Contact

Connect with me on LinkedIn, GitHub, and Email:

LinkedIn Profile

GitHub Profile

[email protected]

Security Blog

Setting Up Splunk on a Raspberry Pi Kubernetes Cluster

This blog post walks through how I configured Splunk log forwarding from a Raspberry Pi cluster running Kubernetes. I deployed the Splunk Universal Forwarder to each worker node, configured with `inputs.conf` and `outputs.conf` to capture log data from mounted pod volumes such as `/var/log/pods` and container log paths. I used tailed file monitoring for containerized applications, sending data securely to a Splunk Enterprise instance hosted on my Windows machine. Configuration was automated using Ansible to ensure consistent deployment across all nodes. This setup simulates a basic SIEM pipeline, enabling real-time log ingestion, threat detection, and infrastructure health monitoring — providing visibility into my Kubernetes environment without relying on third-party cloud tools.

Published: July 1, 2025

Founder Startup Note

AutoBug AI: AI-Assisted Cybersecurity Workflow Platform

AutoBug AI is a cybersecurity workflow platform I founded to help security teams conduct authorized testing with stronger transparency, human control, and evidence-driven validation. The product is designed around an interactive sandbox experience where users can monitor activity, review findings, and keep testing decisions tied to clear operational context.

My work on AutoBug AI has included cloud deployment, frontend and backend coordination, sandbox workflow design, infrastructure troubleshooting, security operations research, and product iteration based on feedback from cybersecurity and enterprise conversations.

What it focuses on Human-led security testing, workflow visibility, approvals, evidence capture, and practical operational guidance.

What I learned How to turn cybersecurity fundamentals into a real product involving infrastructure, UX, cloud systems, and technical tradeoffs.

Why it matters It shows ownership, fast learning, systems thinking, and the ability to build through ambiguity.

Visit AutoBug AI

Published: May 13, 2026