AI Driven Cybersecurity Platform designed for SMEs

Inspiration

In Kenya, SMEs are the backbone of economic growth and local innovation — yet they are increasingly vulnerable to cyberattacks. Many lack the technical expertise, financial resources, or awareness to protect their digital assets. Seeing how devastating these threats can be to small businesses, we were inspired to build a cybersecurity solution that is smart, accessible, and tailored specifically for SMEs. Our goal is to democratize cybersecurity and empower local businesses to operate securely in the digital world.

What it does

Encryphers is an AI-powered cybersecurity platform designed to detect, analyze, and respond to cyber threats in real-time, with minimal human input. It offers: Intelligent threat detection using machine learning. Automated alerts and incident reports. Behavioral analysis to detect anomalies in user activity.Data encryption & access control for sensitive information. A user-friendly dashboard tailored for SMEs without technical teams.

How I built it

The platform was built using: Python for backend logic and threat detection algorithms. TensorFlow & Scikit-learn for AI/ML threat modeling.Flask for the API layer. React.js for the user interface/dashboard. SQLite/PostgreSQL for secure data handling. Deployment was done using Heroku and Docker for containerization. We focused on creating a modular, scalable architecture that could adapt to different SME needs.

Challenges I ran into

Data scarcity: Finding high-quality, localized cyber threat datasets relevant to Kenyan SMEs. Balancing complexity with simplicity: Creating powerful AI features while ensuring the interface remains user-friendly for non-tech users. Time constraints: Building a robust system while preparing for the hackathon and supporting other commitments. Resource limitations: Hosting and running ML models efficiently with limited computing power.

Accomplishments that I'm proud of

Successfully integrating AI for real-time threat detection in a lightweight environment. Designing a clean, accessible UI that SME owners can easily navigate. Building a working prototype under limited time and resource conditions. Creating a solution that addresses a real, local problem with significant impact potential.

What I learned

How to train and fine-tune ML models for anomaly detection in cybersecurity. The importance of user experience (UX) in cybersecurity tools — simplicity is key. Gained hands-on experience in deploying full-stack applications using modern frameworks and cloud tools. Deepened my understanding of how cyber threats uniquely affect SMEs in developing regions.

What's next for AI Driven Cybersecurity Platform designed for SMEs

Pilot testing with real SMEs in Kenya to gather feedback and improve the solution. Expanding the threat database with localized attack patterns from East Africa. Adding more features like phishing detection, mobile integration, and cybersecurity awareness training modules. Seeking partnerships with local business associations and cybersecurity agencies for rollout. Ultimately, launching it as a subscription-based SaaS product, affordable and scalable for SMEs across Africa.