Comprehensive, peer-reviewed security awareness training for deepfakes, prompt injection, and AI security threats.

📚 Read the Course | 🤝 Contribute | 💬 Discuss

• Detection techniques (visual, audio, metadata)
• Prevention strategies (authentication, verification)
• Incident response procedures
• Forensic analysis methods

• Prompt injection attacks and defenses
• LLM security best practices
• Input validation and sanitization
• Output filtering and monitoring

• NIST AI Risk Management Framework
• OWASP Top 10 for LLM Applications
• ISO/IEC 42001:2023 (AI Management)
• C2PA Content Authenticity

• GDPR, CCPA, BIPA regulations
• Criminal statutes (18 USC §2252, CFAA)
• Civil liability and remedies
• International frameworks

20+ Chapters organized into:

1. Basics - Understanding deepfakes and AI threats
2. Detection - Identifying manipulated content
3. Prevention - Protecting systems and users
4. Response - Handling security incidents
5. Advanced - Forensics, legal, standards, threat intelligence
6. Community - Learning paths and resources

Visit durellwilson.github.io/security-awareness-course

# Clone repository
git clone https://github.com/durellwilson/security-awareness-course.git
cd security-awareness-course

# Install mdBook
cargo install mdbook

# Serve locally
cd book && mdbook serve
# Open http://localhost:3000

We welcome contributions from the security community!

• 📊 Research: Submit peer-reviewed findings
• 💻 Code: Add detection/prevention examples
• 📝 Documentation: Improve explanations
• 🔍 Case Studies: Document real incidents
• 🌍 Translation: Internationalize content

See CONTRIBUTING.md for detailed guidelines.

• 🌱 Contributor: 1+ merged PR
• 🌿 Regular: 5+ merged PRs
• 🌳 Core: 20+ merged PRs

All content is backed by peer-reviewed research:

• ✅ Academic journals (IEEE, ACM, California Law Review)
• ✅ Government standards (NIST, CISA, NSA)
• ✅ Industry frameworks (OWASP, MITRE ATT&CK)
• ✅ Reputable vendors (Microsoft, Google, IBM)

15+ Citations with DOI/arXiv verification links.

• Documentation: mdBook
• CI/CD: GitHub Actions (test, build, deploy)
• Hosting: GitHub Pages
• Security: Trivy scanning, markdown linting
• Quality: Automated spell checking, link validation

• 500% increase in deepfake videos (2022-2024)
• 73% of LLM apps vulnerable to prompt injection
• 96% of deepfakes are non-consensual content
• $4.5M average data breach cost

Sources: Sensity AI, Microsoft Security, IBM Security

Introduction → Basics → Detection → Prevention

Beginner + Prompt Injection → Advanced Detection → Incident Response

Intermediate + Forensics → Legal → Standards → Threat Intelligence

Part of the Detroit tech community contributing to:

• Developer education
• Security awareness
• Open source collaboration
• Hacktoberfest initiatives

• GitHub Discussions: Ask questions, share insights
• Issues: Report bugs, request features
• Pull Requests: Contribute improvements

MIT License - see LICENSE for details.

Found a vulnerability? See SECURITY.md for responsible disclosure.

Built with contributions from the open source security community.

Key Research Sources:

• Chesney & Citron (2019) - California Law Review
• Tolosana et al. (2020) - Information Fusion
• Greshake et al. (2023) - arXiv
• NIST AI RMF (2023)
• OWASP LLM Top 10 (2024)

⭐ Star this repo to support security education!

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