Harness the power of open source AI
Accelerate innovation with AI and ML — without introducing risk into your SDLC. Sonatype’s industry-first end-to-end AI Software Composition Analysis (SCA) gives you visibility and control over AI/ML usage, ensuring speed, security, and compliance without compromise.
Explore the industry's 1st end-to-end AI Software Composition Analysis
Corporate adoption of open source AI models has surged, reflecting a significant shift in how companies leverage AI in their data and DevOps pipelines. See how the Sonatype platform can help you harness the power of AI safely.
Securely integrate, manage and govern open source AI models with Sonatype
Build fast using open source AI without worrying about bringing risk into your data pipelines and SDLC. The Sonatype platform enables developers to safely integrate open source AI models and libraries into your applications ensuring your builds are stable and secure.


Centralized access to AI models
Access the latest Hugging Face models faster and easily share them across your organization.
Centralize your development
DevOps with ease
Expand your ecosystem

Seamless AI Model Governance
Get instant visibility, policy, and control over your AI/ML usage in your applications and data pipelines with AI SCA.
Unlock AI Reports
Mitigate AI Risk
Set and Enforce Policies



Open Source AI/ML Compliance
Streamline reporting requirements to easily comply with regulations.
Increase AI Transparency
Meet Regulatory Requirements
Share Comprehensive SBOMs

Proactive Defense Against Malicious Open Source AI
Block malicious AI models and libraries from entering your repository.
Intercept Malicious AI Models
Choose Safe AI Models
Control Your AI Risk

Forrester evaluated 10 SCA providers and recognized Sonatype with the highest possible scores in AI component analysis
Approach AI model management and security with confidence
Our industry-first AI SCA solution allows you to adopt AI and ML with the same level of safety and productivity as traditional open source. Let us help you alleviate concerns around using AI and ML securely.
Top DevOps Concerns About Generative AI
19% say it will pose security and resilience risks
19% say it will require special code governance
14% say inherent data bias will impact reliability
Top SecOps Concerns About Generative AI
18% say it will pose security and resilience risks
15% say lack of reasoning transparency will lead to uncertain results
14% say it will lead to technical debt
AI Resources

Gartner® Report: Emerging Tech Impact Radar: Artificial Intelligence

The risks & rewards of generative AI in software development

The Effects of AI on Developers
Frequently Asked Questions
How does Sonatype help with AI model governance and management?
Sonatype helps organizations understand their AI usage and makes it easier for developers and data scientists to use AI in their applications. With industry-first AI software composition analysis (SCA) and end-to-end AI model management, organizations can gain greater visibility, policy enforcement, and storage of Hugging Face models.
What should I consider before deploying AI models in my applications?
AI/ML models offer several distinct advantages, including accelerated development, simplified integration of advanced language capabilities, and performance benefits thanks to the bulk of the processing being handled server-side. If managed improperly, the drawbacks are severe including data privacy and security challenges, the possibility of malicious attacks, and litigious action for any license breaches.
Does Sonatype support Hugging Face models?
Sonatype offers full support for Hugging Face, the largest hub of ready-to-use datasets for machine learning and open AI models with fast, easy-to-use, and efficient data manipulation tools. Our support of Hugging Face models enables the same standard of risk mitigation controls as we provide to open source software components or packages.
What licensing risk comes with AI and LLMs?
While open source AI presents significant opportunities for natural language interaction, it poses potential licensing risks. In many cases, developers may fine-tune open AI models to suit specific applications, but the licensing terms of the foundational model must be carefully considered.
And for now, technology is outpacing legislation. The inevitable legal challenges are likely to help democratize the AI/ML landscape as companies will have to become more transparent about the training datasets, model architectures, and the checks and balances in place designed to safeguard intellectual property.
AI is a powerful tool for software development, and our customers count on our products to help them make critical decisions. This is why we are continually working on ways to integrate it into our portfolio, allowing you to identify, classify, and block threats to software supply chains.
Does Sonatype use AI and ML in its development?
Sonatype has pioneered the use of artificial intelligence and machine learning to accurately speed up vulnerability detection, reduce remediation time, and predict new types of attacks. We use AI/ML to transform software supply chain management in the following ways:
- Release Integrity, a first-of-its-kind AI-powered early warning system uses over 60 different signals to automatically identify and block malicious activity and software supply chain attacks.
- Sonatype Safety Rating, an aggregate rating score, generated by our ML/AI analysis, which evaluates a range of risk vectors including the likelihood of an open source project containing security vulnerabilities.
- License Classification, a ML/AI and human curation driven system to detect and classify open source software licenses into threat groups, such as banned, copyleft, and liberal.
What is Sonatype’s approach to AI and ML?
Effective use of AI and ML starts with ensuring the outputs are providing the most precise and reliable data. Sonatype has a duty to use AI responsibly, which means it must be:
- Fair | AI systems are designed to treat all individuals and groups fairly without bias. Fairness is the primary requirement in high-risk decision-making applications.
- Transparent | Transparency means that the reason behind decision-making in AI systems are clear and understandable. Transparent AI systems are explainable.
- Secure | AI systems must respect privacy by providing individuals with agency over their data and the decisions made with it. AI systems must also respect the integrity of the data they use.