
Protect AI
The Platform for AI Security

Description
Protect AI provides a broad and comprehensive platform designed to secure artificial intelligence systems end-to-end. It equips Application Security and Machine Learning teams with the necessary tools for visibility, remediation, and governance to manage AI security risks effectively. The platform helps organizations defend against unique AI security threats, regardless of whether they are fine-tuning existing Generative AI models, building custom ones, or deploying LLM applications.
By enabling capabilities to see, know, and manage security vulnerabilities, the Protect AI platform empowers organizations to adopt a security-first approach to AI. It integrates features like zero-trust model security, LLM runtime protection, and automated red teaming to ensure AI exploration and innovation can proceed with confidence. This AI Security Posture Management (AI-SPM) approach addresses the entire AI lifecycle, from development to deployment.
Key Features
- AI Security Posture Management (AISPM): Provides end-to-end visibility, remediation, and governance for AI systems.
- Zero Trust for AI Models (Guardian): Scans, enforces, and manages model security to block unsafe models and secure the ML supply chain.
- LLM Runtime Security (Layer): Offers insights, detection, and response capabilities to protect LLMs during operation against data access issues, attacks, and breaches.
- Automated GenAI Red Teaming (Recon): Identifies potential vulnerabilities in LLMs using attack libraries and LLM agents with no-code integration.
- Model-Agnostic Scanning: Supports scanning various model types and formats for security threats.
- End-to-End AI Security: Addresses security risks throughout the AI development and deployment lifecycle.
- Open Source Security Tools: Offers community-supported tools like LLM Guard, ModelScan, and NB Defense.
Use Cases
- Securing the AI/ML development lifecycle
- Protecting deployed AI and LLM applications
- Managing enterprise AI security posture
- Preventing ML supply chain attacks
- Identifying vulnerabilities in Generative AI systems
- Implementing zero trust principles for AI models
- Ensuring compliance and governance for AI usage
- Detecting and responding to runtime threats against LLMs
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