Enterprise AI Safety Audit Platform for Large Language Model Deployments
Automated AI vulnerability auditing and compliance platform for enterprise LLMs
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Developed a comprehensive AI safety auditing platform implementing the NIST AI Risk Management Framework (AI RMF) to automate vulnerability detection, compliance reporting, and risk mitigation across enterprise-scale large language models (LLMs). This platform significantly reduces audit times while enhancing AI governance and security posture.
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Project Overview
Created an automated AI safety auditing platform designed to detect risks such as prompt injections, data leakage, and algorithmic biases in large language models using the NIST AI RMF as the compliance backbone. The platform integrates continuous vulnerability scanning and provides comprehensive compliance reporting dashboards.
The Challenge
Organizations lacked scalable and automated solutions for AI safety and compliance, leading to prolonged manual auditing processes and heightened regulatory risks in deploying enterprise LLMs safely.
Our Solution
Built a scalable Kubernetes-based system utilizing Python, TensorFlow, and PyTorch for deep model analysis, combined with React dashboards for real-time audit results and NIST AI RMF compliance validation. The platform automates LLM security testing including reverse engineering and bias detection.
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Key Achievements
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"This platform revolutionized our AI governance, enabling confident deployment of LLMs with comprehensive safety and compliance assurance."
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