SecureMail integrates advanced ML models including NLP techniques and security best practices to detect phishing emails efficiently. It follows a secure software development lifecycle (SDLC), implementing security measures in all phases: development, CI/CD, and production.
Table of contents
- Data Sanitization:
- External inputs (user-supplied data) validated using Pydantic.
- Internal inputs (third-party API responses) reviewed for anomalies.
- Environment Variable Management:
- Sensitive information (e.g., API keys) managed using Python-dotenv.
- Threat Mitigation Strategies:
- Implementing OWASP-recommended secure coding guidelines.
- Pylint: Enforces code quality and security best practices.
- Pytest: Ensures API response accuracy through rigorous testing.
- Snyk: Conducts static security analysis (SAST + SCA) to detect vulnerabilities.
- Docker Bench Security: Assesses Docker container security configurations.
- OWASP ZAP: Conduct Dynamic Applicaction Security Testing (DAST)
- Continuous Security Scanning:
- Snyk for dependency vulnerability detection in production.
- Docker Bench Security for runtime container security evaluations.
- OWASP ZAP for dynamic testing.
- Deployment Platform:
- Hosted on Render (PaaS) using Docker-based deployment for reliability.
Important
Always ensure continuous monitoring to catch any new vulnerabilities as they arise in the production environment.
SecureMail follows a structured data processing pipeline to analyze emails. Below is an overview of the data flow:
- Code Signing with GPG key:
- Uses GnuPG to enhance Code Signing security within Github Commits.
Warning
If you do not properly secure your code signing process, you risk introducing malicious code into your software eg: SolarWinds.
- Integrating Application Security Orchestration and Correlation (ASOC).
- Advanced Threat Modeling:
- Using OWASP Threat Dragon for structured threat analysis.
Distributed under the GPL-3.0 license. See the LICENSE file for more details.