What Is AI Supply Chain Risk Management?
AI Supply Chain Risk Management involves securing the path AI models take from development to deployment, ensuring that all elements of the AI lifecycle—whether open-source models or proprietary code—comply with security, licensing, and governance standards.
Our AI Supply Chain Risk Management solutions focus on:
Real-time enforcement
Prevents the download of risky AI models at the moment of access, blocking issues like malicious payloads, non-compliant licenses, or models from prohibited sources.
Early-stage protection
Guards against shadow AI use, where models are downloaded outside of official pipelines.
Comprehensive policy management
Ensures that AI models meet internal and external compliance standards before they are used.
Our Approach
Pre-Download Enforcement Engine
We provide an enforcement layer that blocks risky models before they are ever downloaded or executed. This technology is integrated into Cisco Secure Access and Cisco Secure Endpoint, preventing risky models from entering the system at the earliest stage.
Policy-Driven Risk Management
Our solution allows enterprises to create custom policies around AI model sourcing, licensing, and security. It identifies and blocks models with problematic licensing (e.g., GPL, AGPL), malicious payloads, or models sourced from prohibited regions, ensuring complete risk management.
Seamless Integration
Our tools integrate seamlessly into Cisco's security products, providing a unified defense strategy that works across developer environments, endpoints, and cloud infrastructures.
Why AI Supply Chain Risk Management Matters
As the use of open-source AI models and third-party libraries increases, security teams face mounting pressure to prevent compliance, security, and legal issues. Traditional scanning tools—often employed too late in the development process—fail to address these risks early enough.
Our AI Supply Chain Risk Management tools enable organizations to:
Block risky models before they reach development environments, minimizing potential security or compliance breaches.
Ensure AI models align with company policies around licensing, security, and origin.
Avoid costly rework or legal challenges arising from unvetted open-source models.