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The Role of AI and ML in Software Development

Careless implementation of open source code libraries can leave you exposed to a host of security and risks.
Sonatype can help.

The popularity of AI is exploding. In our 9th Annual State of the Software Supply Chain report, we discovered a 135% increase in the adoption of Artificial Intelligence and Machine Learning and (AI/ML) components within corporate environments over the last year.

This widespread acceptance is matched only by its expanding utility, and its ability to speed up software development is having a transformative impact. Sonatype has pioneered the use of AI/ML to speed up vulnerability detection, reduce remediation time, and predict new types of attacks. We can help you approach AI implementation with confidence.

Discover how Sonatype uses AI across its portfolio:

Malicious Component Detection

Sonatype Safety Rating

License Classification

AI Component Detection

Reduce open source risk across your SDLC

Sonatype Lifecycle uses AI to continuously analyze open source components throughout the software development life cycle (SDLC). By detecting vulnerabilities, enforcing policy controls, providing remediation guidance, and ensuring compliance, we can help reduce open source risk and speed up your development.


Automatically intercept and quarantine malicious OSS

AI-powered behavioral analysis predicts suspicious components days before any public advisory, protecting you from zero-day attacks. Sonatype Repository Firewall is the only solution that protects your repository by preventing known and unknown open source risks from entering your software supply chain.


Our commitment to AI implementation

DevOps professionals are at the forefront of this shift and Sonatype has a responsibility to our customers to use AI responsibly. This means our utilization of AI needs to be fair, transparent, and secure.




Doubling down on AI and ML: Enterprise adoption trends

Over the past year, corporate adoption of tools like ChatGPT has more than doubled, reflecting a significant shift in how companies approach data science and machine learning. One of the most significant implications of AI in software development is its potential to generate code, becoming a necessary tool for increasing productivity. But developers also recognize the potential for AI to complicate threat detection, particularly where OSS is concerned. AI OSS is not well regulated, so security monitoring and remediation advice for the open-source libraries used in your code is paramount. Having a strategy to manage SBOMs will be crucial in managing AI-related components.

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 transparency in the reasoning process will lead to uncertain results

14%  say it will lead to technical debt


Large Language Models (LLM) 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. However, notable drawbacks include cost, data privacy and security, and vendor lock-in. Balancing these pros and cons is essential when evaluating the integration of LLMs-as-a-service into an enterprise's workflow.

Licensing Risk

Open-source LLMs present significant opportunities for natural language interaction but recognize the potential licensing risks associated with these models. In many cases, developers may fine-tune these models to suit specific applications, but the licensing terms of the foundational model must be carefully considered. LLM models have become proficient at generating human-like text, in part by scraping publicly available data off of the Internet. But without express permission from copyright holders, this capability raises serious copyright infringement concerns.

And for now, technology is outpacing legislation. The inevitable legal challenges are likely to help democratize the AI 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 on how we integrate it into our portfolio, allowing you to identify, classify, and block threats to software supply chains.

Explore the Sonatype platform

Sonatype Nexus Repository

Build fast with centralized components.
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Sonatype Repository Firewall

Block malicious open source at the door.

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Sonatype Lifecycle

Reduce risk across software development.
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