Sentinel-AI

** Sentinel-AI** is a modular open-source toolkit for detecting, preventing, and understanding misuse in generative AI systems.
It is designed for developers, researchers, and digital rights advocates focused on AI safety, robustness, and transparency.

🔬 Modules include data poisoning detection, bias analysis, and prompt injection defense.

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Features

• ** Poison Detection** – Identify outliers in structured datasets (CSV)
• ** Bias Analysis** – Analyze and quantify representation and skew in data (coming soon)
• Prompt Security – Detect prompt injection and insecure model outputs (in development)
• CLI & Python API for flexible integration
• Privacy-first, no data ever leaves your machine

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Project Structure

sentinel-ai/ ├── sentinel_ai/ │ ├── poison/ # Outlier detection │ ├── bias/ # Bias quantification (planned) │ ├── prompt/ # Prompt injection detection (in progress) │ └── utils/ # Shared utilities ├── examples/ # Usage examples ├── tests/ # Unit tests └── README.md

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Getting Started

Installation

pip install sentinel-ai
Requires: Python 3.8+

 Basic Usage
 Data Poisoning Detection (CSV)
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from sentinel_ai import poison

# Run detection on a dataset
results = poison.detect_outliers("data.csv")

# Print flagged rows
print(results[results["flagged"] == True])
 Works with CSV files containing numeric data. Header row is optional.

 Use Cases
AI Red-Teaming & Adversarial Testing

Dataset Auditing & Verification

Prompt Filter Development

Fairness & Bias Research

Digital Rights Toolkits

🛡 Philosophy
Sentinel-AI is built around the idea of responsible AI empowerment – providing tools to detect misuse, not restrict use.
We believe developers should be equipped to protect models, users, and systems from unintended consequences.

 Roadmap
 poison: CSV-based outlier detection

 bias: Fairness metrics, representation analysis

 prompt: Injection pattern detection, response sanitization

 GUI frontend (Qt/Web)

 Community-sourced pattern libraries

 License
This project is licensed under the MIT License.
© 2025 lennarddaw

 Contributing
Pull requests, issue reports, and ideas are welcome!
Check out the CONTRIBUTING.md (optional) to get started.

 Links
 PyPI Package (if published)

 Example Scripts

 Docs (coming soon)