AI Model Security is an enterprise application designed to enforce comprehensive security standards for both internal and external machine learning models deployed in production environments. The application addresses a critical gap in organizational security practices where machine learning models, despite their significant impact on business operations, often lack the rigorous security validation that is standard for other data inputs and systems.

In most enterprise environments, traditional data inputs such as PDF files undergo extensive security scrutiny before processing, yet machine learning models that drive critical business decisions frequently bypass equivalent security measures. This disparity creates substantial operational risk, as compromised or inadequately validated models can impact business logic, data integrity, and decision-making processes. AI Model Security solves this problem by providing comprehensive model validation through automated security assessments, multi-source support for both internally developed and third-party models, and proactive identification and remediation of model-related security vulnerabilities.

By implementing AI Model Security, organizations can establish consistent security standards across all ML model deployments, significantly reducing operational risk from unvalidated or compromised models. It enables secure adoption of third-party ML solutions while maintaining organizational security rules and industry compliance standards. Additionally, it provides comprehensive audit trails and compliance reporting capabilities, ensuring that AI Model Security assessments meet regulatory requirements and internal governance standards.

Models can execute arbitrary code and existing tooling is not checking that for you. Models have been found at the root of cloud take over attacks, and can be used to exfiltrate data, or even to execute ransomware attacks. The sensitivity of the data that models are trained on and exposed to at inference time makes them a prime target for attackers.

AI Model Security enables you to have flexible controls to secure, validate, and manage AI models across different sources through Security Groups, Sources, Rules, and Scans.

AI Model Security delivers a comprehensive framework to establish and enforce security standards for AI models across your organization. Unlike traditional security tools that simply scan for malware, AI Model Security recognizes that AI models require more nuanced security considerations that incorporate license validation, file format verification, and context-specific security checks based on the teams and environments using the models.

The AI Model Security approach moves beyond the simplified first-party versus third-party model distinction to provide granular security controls that scale with enterprise needs. This approach centers around four key components: Security Groups, Sources, Rules, and Scans.

AI Model Security leverages rules to help organizations establish sophisticated, scalable security frameworks tailored to their specific requirements. This flexible approach enables teams to enforce strict blocking mechanisms for high-severity threats while maintaining non-disruptive alerting for compliance monitoring—allowing security teams to effectively manage risk without hindering developer productivity. The result delivers dual benefits: end users gain confident access to vetted models through a seamless experience, while security teams receive comprehensive protection for their AI/ML infrastructure.

To implement AI Model Security effectively, you'll typically need at least two Security Groups: one for external models using HuggingFace as a Source, and another for internal models using Local or Object Storage Sources. This separation allows you to apply appropriate security standards based on the origin and intended use of models across your organization.