MemoryGuard AI is a security-oriented monitoring project designed to collect, process, and analyze memory-related events from distributed environments. The project is being developed as a hands-on study and portfolio initiative focused on Support Engineering, Cybersecurity, backend systems, observability, and event-driven architecture.
The main goal of MemoryGuard AI is to simulate a modern monitoring and security pipeline where a lightweight agent collects system memory metrics, sends them to a messaging platform, and allows a backend service to consume, process, and eventually store or analyze these events.
The current architecture includes a Go-based collector, responsible for gathering memory usage data, and a Java Quarkus backend, designed to consume and process events. Communication between services is handled through Apache Kafka, while PostgreSQL is used as the relational database layer. The environment is containerized with Docker Compose, making it easier to reproduce, test, and evolve the system locally.
MemoryGuard AI was created to explore how cybersecurity, troubleshooting, and backend engineering can work together in a practical project. It is not intended to be a finished commercial product at this stage, but rather a growing technical foundation for learning and demonstrating skills related to:
- Event-driven systems
- System monitoring
- Memory usage collection
- Kafka-based communication
- Java backend development with Quarkus
- Go-based lightweight agents
- Dockerized development environments
- Technical troubleshooting and infrastructure setup
- Security-oriented architecture
The current project flow is:
Go Collector → Kafka Topic → Quarkus API → PostgreSQL
The collector publishes memory usage events to a Kafka topic called memory-events. The backend API is responsible for consuming these events and will later be extended to persist, analyze, and expose the collected data through APIs and dashboards.
- Java 21
- Quarkus
- Go
- Apache Kafka
- Zookeeper
- PostgreSQL
- PgAdmin
- Docker
- Docker Compose
- WSL / Linux development environment
Planned improvements include:
- Persisting memory events in PostgreSQL
- Creating REST endpoints to query collected data
- Adding basic alert rules for abnormal memory usage
- Building a frontend dashboard
- Improving collector capabilities with CPU, process, and disk metrics
- Adding authentication and tenant-aware architecture
- Exploring anomaly detection and AI-assisted analysis
- Improving observability with logs, metrics, and dashboards
This project reflects my interest in combining Support Engineering, Cybersecurity, troubleshooting, and software development. It is part of my learning path toward building stronger skills in secure backend systems, incident-oriented monitoring, cloud-ready architecture, and real-world infrastructure troubleshooting.