*This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle*
What I Built
PhishBrief AI is an intelligent, locally-hosted phishing analysis assistant tailored for individuals and small businesses.
Cybercriminals constantly use urgency, spoofing, and malicious links to steal sensitive data, yet many freelancers and small teams simply cannot afford expensive enterprise email security platforms. PhishBrief AI bridges this gap by acting as an accessible first line of defense. It evaluates suspicious emails, highlights red flags, and offers actionable security advice—all without transmitting your data to the cloud.
As a side hustle, this project represents a lightweight, affordable security triage solution designed specifically for IT support teams, students, and small organizations that need understandable threat analysis without the enterprise price tag.
How I Built It
I engineered PhishBrief AI by combining standard web frameworks with powerful local AI inference.
- Application & UI: The core logic is written in Python, utilizing Streamlit to create an interactive, user-friendly interface.
- Local Inference Engine: I used Ollama as the local runtime to ensure all data processing happens directly on the user's machine.
- Open-Weight Models: The primary engine driving the analysis is Granite 3.2 Vision, carefully configured for this specific security workflow. The development environment also supports Gemma 3 1B as an alternative local model.
The architecture centers around a privacy-conscious workflow. When a user pastes a suspicious message, the local model parses the content, identifies phishing indicators, explains the exact risks in plain English, and generates safety recommendations. By using local inference rather than third-party APIs, the application ensures that potentially sensitive email data never leaves the device.
Why Does Open Innovation Matter?
Open-source AI is the foundational pillar of this project. Without open-weight models and runtimes like Ollama, building a truly privacy-first tool would require relying on proprietary, paid APIs—which introduces both data privacy risks and recurring costs.
Open innovation breaks down these barriers. It allows independent developers to experiment freely, iterate on prompting strategies, and build practical applications that respect user privacy. While open models don't automatically guarantee perfect accuracy, they give creators complete control over how the AI is deployed, evaluated, and improved without enterprise-level infrastructure.
My First User
My ideal target audience consists of freelancers and small-business teams who need a straightforward way to evaluate threats without requiring a background in cybersecurity.
I am currently onboarding early testers to run the tool against safe, simulated phishing emails. My primary objective at this stage is to gather feedback on whether the AI's risk breakdowns are intuitive and genuinely helpful for non-technical users. This validation will help refine the tool from a prototype into a viable side-hustle product.
Prize Categories
- Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
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