CyberDefenderAI / Cybersecurity-in-the-field-of-AI

# Cybersecurity in the field of AI
As Artificial Intelligence transitions from a theoretical advantage to a foundational component of modern IT infrastructure, the cybersecurity paradigm is fundamentally shifting. AI is both the **shield** (automating defenses, summarizing threats, analyzing code) and the **target** (vulnerable to prompt injection, data poisoning, and model inversion). 
This repository is a consolidated directory divided into five standalone, deep-dive modules. Each module comprehensively covers a specific domain of AI implementation, vulnerabilities, threat modeling, and automated defense mechanisms.
## 🗺️ How to Use This Guide
This repository is designed for Security Architects, SOC Analysts, ML Engineers, and DevSecOps professionals. Choose a module below based on your current operational focus.
## 📂 1. AI Security Tools & Applied Scenarios
**Repository:** AI-Security-Tools-Applied-Scenarios
**Overview:** 
A highly actionable, curated list of open-source tools and practical use cases leveraging AI, ML, and LLMs to solve complex information security challenges. This module bridges the gap between theoretical AI capabilities and daily security operations.
**Key Highlights:**
* **Workflow Integrations:** AI-powered IDE plugins for Shift-Left security, Browser extensions for OSINT, and CLI tools for automated bash scripting.
* **Autonomous Security Agents:** Overviews of Red Team orchestrators (like PentestGPT) and defensive MCP (Model Context Protocol) servers bridging LLMs to local SIEMs.
* **Applied Use Cases:** Deep dives into automated vulnerability analysis, anomaly detection (UEBA/NTA), fraud detection, and threat intel localization.
* **Target Audience:** Red Teamers, SOC Analysts, DevSecOps Engineers.
## 📂 2. AI Data Security & Governance
**Repository:** AI-Data-Security-Governance
**Overview:** 
In the era of Generative AI, the data *is* the primary attack surface. This module focuses on securing the ML data pipeline from ingestion to vector storage, ensuring that the foundation of the AI model remains uncompromised and privacy-compliant.
**Key Highlights:**
* **The Data Lifecycle:** Techniques for data sanitization (PII removal), cryptographic data provenance, lineage tracking, and data poisoning prevention.
* **RAG Security:** The unique vulnerabilities of Retrieval-Augmented Generation, focusing on Vector Database isolation and Row-Level Security (RLS).
* **Advanced Threats:** Understanding Embedding Inversion Attacks (reverse-engineering text from vectors) and implementing AI watermarking.
* **Target Audience:** Data Scientists, Privacy Officers, MLSecOps Engineers.
## 📂 3. Threat Modeling for AI Systems
**Repository:** Threat-Modeling-for-AI-Systems
**Overview:** 
Traditional threat modeling frameworks (like STRIDE) fail to capture the probabilistic nature of neural networks. This module explores specialized frameworks and repositories required to systematically identify and assess risks in AI systems before they re