SteveSharma-official/defending-tomorrow

# 🛡️ 捍卫明天：智能威胁时代的 AI 安全 **作者：** Steve Sharma | **状态：** 积极开发中 | **版本：** 1.0 ## 📚 关于本书 *捍卫明天* 是一本权威的 AI 安全实践指南——由一位首席架构师专为高级安全专业人员、ML 工程师和 SOC 架构师撰写。 **这是一本为保护 AI 系统安全以及将 AI 武器化用于防御而编写的实战手册。** **关键指标：** - 📖 涵盖 4 大部分的 15 个章节 - 💻 60 多个带注释的代码清单 - ✅ 15 多份可打印的清单 - 📊 35 多张架构图 - 🌏 结合澳大利亚背景并具备全球适用性 - **覆盖 5 个司法管辖区** – 美国、英国、欧盟、加拿大、澳大利亚 ## 📁 仓库结构 defending-tomorrow/ │ ├── code/ # 特定章节的代码清单 │ ├── ch03-adversarial-ml/ # 第 3 章：FGSM，成员推理攻击 (membership inference) │ ├── ch04-secure-dev/ # 第 4 章：差分隐私 (Differential privacy)，安全序列化 (safe serialization) │ ├── ch05-mlops-security/ # 第 5 章：Terraform，K8s 策略 │ ├── ch06-llm-security/ # 第 6 章：Prompt 注入，RAG 安全 │ ├── ch07-advanced-defenses/ # 第 7 章：Neural cleanse，水印技术 │ ├── ch08-threat-detection/ # 第 8 章：自编码器 (Autoencoders)，LSTM 模型 │ ├── ch09-automated-response/ # 第 9 章：告警聚类 │ ├── ch10-soar-platforms/ # 第 10 章：自适应剧本 (Adaptive playbooks) │ ├── ch11-red-teaming/ # 第 11 章：攻击路径图 │ └── ch12-zero-trust/ # 第 12 章：Service mesh，ABAC │ ├── checklists/ # 可打印的评估工具 │ ├── pdf/ # PDF 版本（可打印，随时用于审计） │ └── markdown/ # Markdown 源文件（可编辑） │ ├── diagrams/ # 架构图 │ └── source-files/ # Draw.io，Excalidraw 源文件 │ ├── docs/ # 文档 │ ├── appendices/ # 附录 A-E（术语表、工具、标准） │ └── examples/ # 使用示例 │ └── notebooks/ # Jupyter notebooks │ ├── scripts/ # 实用脚本（自动化、验证） │ ├── requirements.txt # Python 依赖项 ├── CONTRIBUTING.md # 贡献指南 ├── LICENSE # MIT License └── README.md # 本文件 ## 🚀 快速开始 ``` ## 📂 文件夹用途快速参考 | Folder | What It Contains | Who Should Use It | |--------|------------------|-------------------| | `/code` | Executable Python code by chapter | Developers, ML engineers, security researchers | | `/checklists` | Printable PDF and editable Markdown checklists | Auditors, compliance teams, security managers | | `/diagrams` | Architecture diagrams (PNG/SVG + source files) | Architects, presenters, documentation teams | | `/docs` | Appendices A-E (glossary, tools, standards, case studies) | Everyone - reference material | | `/examples` | Interactive Jupyter notebooks | Hands-on learners, trainers | | `/scripts` | Utility scripts for automation | DevOps, security automation engineers | ### 文件夹详细内容 **📁 /checklists** - 15 checklists from Chapters 1-15 - Format: Markdown (editable) + PDF (audit-ready) - Topics: Security posture, Zero Trust AI, Governance maturity **📁 /code** - Code listings referenced as `Code X.Y` throughout the book - Organized by chapter (`ch3_adversarial/`, `ch6_llm_security/`, etc.) - Dependencies listed in `/requirements.txt` **📁 /diagrams** - 35 diagrams available in Draw.io (`.drawio`) and exported PDF formats - Use case: Security architecture reviews, board presentations, regulatory submissions **📁 /docs** - **⚠️ Password-protected content** — Contact the author for access - Contains: Endorsement previews, draft chapters, high-resolution diagrams **📁 /examples** - Standalone demos: Adversarial training, RAG security, SOAR engine - Includes Jupyter notebooks and deployable applications **📁 /scripts** - Automation tools for repository maintenance - `generate_toc.py`, `validate_checklists.py`, etc. ```bash # Clone the repository git clone https://github.com/SteveSharma-official/defending-tomorrow.git cd defending-tomorrow # Install dependencies（如果 requirements.txt 存在） pip install -r requirements.txt 📚 Chapter Reference Chapter Folder Topics Ch 3 code/ch03-adversarial-ml/ FGSM, PGD, model extraction, membership inference Ch 4 code/ch04-secure-dev/ Differential privacy (Opacus), safe serialization (safetensors) Ch 5 code/ch05-mlops-security/ Terraform, Kubernetes Pod Security Policies Ch 6 code/ch06-llm-security/ Prompt injection classifiers, RAG security Ch 7 code/ch07-advanced-defenses/ Neural Cleanse, backdoor detection, watermarking Ch 8 code/ch08-threat-detection/ Autoencoders, LSTM-based UEBA Ch 9 code/ch09-automated-response/ Alert clustering, dynamic containment Ch 10 code/ch10-soar-platforms/ Adaptive playbook engines Ch 11 code/ch11-red-teaming/ Attack path graphs (NetworkX) Ch 12 code/ch12-zero-trust/ Istio service mesh, ABAC policies ✅ Available Checklists ID Name Use Case 1.1 Initial AI Security Posture Assessment Quick start assessment 2.1 Threat Landscape Assessment Identify AI-specific threats 3.1 Adversarial Vulnerability Assessment Test model robustness 4.1 Pre-Training Security Checklist Before model training 4.2 Model Release Security Gate Production deployment 7.1 Advanced AI Model Security (20-point) Comprehensive verification 12.1 Zero Trust AI Implementation (50-point) Enterprise rollout 13.1 AI Security Governance Health Check Compliance readiness 15.1 Future-Proofing AI Security Strategic planning 📥 Download all checklists: checklists/pdf/ 📚 Appendices Appendix A: AI Security Assessment Frameworks (8 checklists) Appendix B: 50+ Open-Source Tools Catalog Appendix C: 200+ Term Glossary (AI + Cybersecurity) Appendix D: Standards Mapping (NIST, MITRE ATLAS, ASD Essential Eight, APRA CPS 234, ISO 42001) Appendix E: 5 Case Studies (Gov, Bank, OT/ICS, Defence, Healthcare) 🤝 Contributing We welcome contributions! Please see CONTRIBUTING.md for guidelines. Technical Reviewers Needed: Looking for CISOs, ML Engineers, and Security Architects. 📄 License Code samples: MIT License (free use with attribution) Diagrams & checklists: All rights reserved (book content) 🐛 Reporting Issues Open an issue for: Code corrections or bugs Clarification requests Security concerns (private disclosure available) 📬 Connect ⭐ Star this repository to stay updated with new chapters and code releases! **Book Pre-Release Updates** Want to know when the book is available? 📧 Email: steve@cybersecuritylink.com.au (put "Defending Tomorrow" in the subject) ## 作者 **Steve Sharma** — Principal Cybersecurity Architect, IRAP Assessor ## License MIT License ```

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