About Aegis

Inspiration

Investigating cybersecurity incidents manually is slow, error-prone, and often misses subtle patterns that can lead to major breaches. I was inspired to create Aegis, an autonomous security agent, to explore how AI could assist in detecting threats humans often overlook, saving time and reducing risk.

What I Learned

Through building Aegis, I learned how to leverage Gemini 3 for autonomous reasoning tasks, how to design a system that runs continuously without supervision, and how to balance technical complexity with a clear, demonstrable workflow. I also improved problem-solving skills in debugging AI pipelines and designing feedback loops for self-correcting logic.

How I Built It

Aegis continuously ingests simulated security event data for testing, applies pattern recognition to detect anomalies, and shows threats in sidebar.

The system uses Typescript, Nano Banana and Gemini 3 for autonomous reasoning. Aegis self-corrects when patterns change, ensuring reliable detection over extended periods.

Built using Google AI Studio

Challenges

Integrating Gemini 3 into a real-time pipeline and managing false positives were the biggest challenges. I iteratively tested different feedback loops and fine-tuned reasoning to maintain accuracy without constant human supervision.

Impact & Future

Aegis can reduce manual investigation time from tens of hours to under one hour, helping teams proactively prevent security breaches. Future improvements include multi-source data integration and enhanced visualization dashboards for even richer insights.

Built With

• gemini-3-api
• github
• google-ai-studio
• json
• markdown
• nano-banana
• typescript