CyberDefenderAI / AI-Data-Security-Governance

# AI Data Security
A curated, deep-dive guide and list of open-source tools dedicated to **Data Security and Integrity in Artificial Intelligence**. 
In the era of Large Language Models (LLMs) and advanced Machine Learning (ML), the traditional cybersecurity perimeter has shifted. **The data itself is now the primary attack surface.** If malicious actors can poison the training data, manipulate vector embeddings, or bypass data provenance checks, they can fundamentally compromise the AI model's behavior, leading to data exfiltration, biased decision-making, and autonomous system hijacking. This repository explores the critical pillars of securing AI through rigorous data governance.
## 📑 Table of Contents
1. Data Processing: The Foundation of Secure AI
 * Data Cleaning & PII Sanitization
 * Data Verification & Validation
 * Data Lineage
 * Data Integrity
 * Data Provenance
 * Data Augmentation
 * Data Balancing
2. Data Types: Securing the Attack Surface
 * Structured Data
 * Semi-structured Data
 * Unstructured Data
3. Watermarking: Protecting IP and Authenticity
4. Retrieval-Augmented Generation (RAG) Security
 * Vector Stores Security
 * Embeddings & Inversion Attacks
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## 🛡️ Data Processing: The Foundation of Secure AI
Secure AI starts long before the model training phase. The data processing pipeline is the first line of defense against adversarial machine learning, data poisoning (where attackers subtly modify training data to introduce backdoors), and privacy breaches.
### Data Cleaning & PII Sanitization
LLMs are infamous for memorizing their training data. If raw datasets contain Personally Identifiable Information (PII) or Protected Health Information (PHI), the model can be prompted to regurgitate this sensitive data, leading to severe GDPR/HIPAA violations. Data cleaning in a security context means aggressive anonymization, masking, and redaction.
* **Security Risk:** Data leakage, model memorization attacks, prompt extraction.
* **microsoft/presidio:** An open-source framework by Microsoft that provides fast identification and anonymization of PII in text and images using customizable NLP models and rule-based logic.
* **cleanlab/cleanlab:** The standard AI software for finding and fixing label errors in datasets, preventing attackers from injecting poisoned, mislabeled data into the training pipeline.
### Data Verification & Validation
Before data enters a model, it must strictly conform to expected schemas and statistical distributions. Adversaries often attempt to inject out-of-bounds data or malicious payloads (like hidden prompt injections in scraped web text) to corrupt the model's logic.
* **Security Risk:** Data poisoning, buffer overflows in legacy parsers, indirect prompt injections.
* **great-expectations/great_expectations:** A powerful Python-based framework that allows data teams to define strict, testable "expectations" (rules) for data quality and structure, blocking anomalous or malicious batches from entering the t