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Artificial intelligence (AI) is reshaping how software is built and delivered. Your tech business can use AI to accelerate development, cut costs, and gain a competitive edge. But along with these benefits, AI introduces risks that your company cannot ignore.
When you integrate an AI model, it depends on external datasets, libraries, and frameworks. These components may contain copyrighted material, biased information, or unverified data sources. That raises critical questions: Who owns the rights to the model? Can your startup legally use it in a product? Could vulnerabilities in the software supply chain expose your customers to attacks?
For technology companies, these issues are more than technical challenges. They are legal and compliance risks with real consequences. Regulators in the United States and European Union are already imposing strict requirements on AI adoption. At the same time, customers and investors want proof that your systems are safe and transparent.
This article explains what your tech startup should know about AI in the software supply chain. It explores the opportunities, the risks, and the steps your company should take to protect its intellectual property, reduce liability, and ensure compliance.
AI’s Double Edge in the Software Supply Chain
AI brings both opportunity and risk. On one hand, it helps automate software development, making processes faster and more efficient. Many startups adopt AI to speed product releases and reduce costs. This advantage helps smaller companies compete with larger corporations.
On the other hand, AI also creates new vulnerabilities. Data provenance is a major concern. If an AI system is trained on copyrighted content or biased datasets, the outputs may carry those same problems. That could expose your business to lawsuits or regulatory investigations.
Compliance risks are another issue. Many AI models are distributed with unclear licensing terms. A developer might download a pretrained model without realizing it comes with restrictions. If your tech business uses that model commercially, you could face licensing disputes.
AI also introduces unpredictability. Unlike traditional software, which behaves consistently, AI outputs vary with the same input. This non-determinism makes it difficult to guarantee reliability. For industries like healthcare or finance, that could lead to compliance failures and legal exposure.
Your technology company should see AI as both a driver of innovation and a potential liability. To fully benefit from AI, you need safeguards, technical and legal. Partnering with experts like Stevens Law Group ensures your startup addresses these risks while protecting its future growth.
Open Source AI Models and Their Legal Implications
Open source has long accelerated software supply chains, but it has also created security and licensing challenges. The same is true for open source AI models. They provide access to powerful tools but introduce new layers of risk.
Just like open source code, AI models can contain vulnerabilities. Attackers can tamper with them, insert malicious code, or distribute unsafe versions. Worse, these risks are harder to detect because AI models are not easily inspected like source code.
Licensing is another concern. Many open source AI projects come with usage restrictions. Your startup might unknowingly use a model trained on copyrighted datasets, which could trigger lawsuits. Others may include ethical use clauses that limit how the model can be deployed. Ignoring these terms could leave your business vulnerable to legal action.
Copyright issues extend to training data. Large language models (LLMs) often rely on massive datasets scraped from the internet. These datasets may include books, images, or code covered by intellectual property law. If your technology business builds products on top of such models, you risk infringing on others’ rights.
This is why legal due diligence is critical when working with open source AI models. A law firm like Stevens Law Group can help your company review licensing terms, assess compliance obligations, and protect your innovations. By understanding the legal landscape, your tech business can use open source AI confidently and securely.
Software Supply Chain Visibility Gap That Can Hurt Your Tech Business
One of the biggest problems in the software supply chain is lack of visibility. Many technology companies do not know exactly which AI models, datasets, or frameworks are in use. This blind spot creates major risks.
Without visibility, your team cannot confirm the origin of an AI system. That means you may unknowingly rely on components with security vulnerabilities, hidden backdoors, or unlicensed datasets. If regulators investigate, your tech business may not be able to prove compliance.
Licensing is also tied to visibility. Just like open source software, many AI models come with specific legal obligations. If your developers cannot identify the source of a model, they cannot track whether licensing terms are being met. This oversight can quickly escalate into disputes or penalties.
For startups, visibility is essential not only for compliance but also for intellectual property protection. If you cannot document the origins of the AI tools you use, it becomes harder to defend your own IP rights later.
Building visibility into the software supply chain requires both technical and legal strategies. Tools that track AI provenance can document datasets, dependencies, and frameworks. At the same time, legal counsel can ensure those records align with IP and regulatory requirements. By addressing this visibility gap, your tech startup reduces legal exposure and builds credibility with investors and customers.
Regulatory Pressures on the EU AI Act and U.S. Catch-Up
Governments are acting quickly to regulate the use of AI in the software supply chain. The European Union (EU) is leading with the EU AI Act, which imposes strict requirements on transparency, fairness, and accountability. If your tech startup sells products in Europe, compliance is mandatory.
The EU AI Act requires businesses to prove how their AI systems were trained, tested, and deployed. For high-risk applications like hiring tools or healthcare software, the standards are even stricter. Failing to comply could lead to heavy fines and blocked market access.
In the United States, progress is slower but moving in the same direction. Executive orders and industry-specific guidelines are being introduced, especially for healthcare, finance, and defense technology. For global technology companies, this means preparing for a patchwork of legal frameworks.
Startups cannot ignore these developments. Even if your company is based in the U.S., using AI in products that reach the EU market will trigger EU compliance requirements. Investors also expect startups to anticipate these regulations when assessing long-term risk.
Working with experienced intellectual property lawyers ensures your tech business stays ahead of these pressures. Firms like Stevens Law Group provide legal guidance on aligning with global standards, protecting IP rights, and documenting compliance across borders. For your startup, regulatory readiness is not only a legal obligation but also a strategic advantage.
AI Bill of Materials (AIBOM) and Transparency in Practice
Transparency is critical for building trust in AI systems. That’s why the AI Bill of Materials (AIBOM) is gaining attention in the software supply chain.An AIBOM is a record that documents the datasets, frameworks, and dependencies used in training and deploying an AI model. For your tech business, it acts like an inventory list, showing what went into the system. This helps identify vulnerabilities, track licenses, and prove compliance with regulations.The concept is similar to the Software Bill of Materials (SBOM), which became standard after cybersecurity executive orders. Just as SBOMs provide visibility into software dependencies, AIBOMs aim to provide insight into AI components.
For startups, an AIBOM offers several benefits. It gives your business a way to track whether a dataset includes copyrighted material, monitor licensing restrictions, and confirm that your AI supply chain is secure. It also demonstrates transparency to regulators and customers, which builds trust.
The challenge is that AIBOMs are still emerging. Standards are not fully established, and many tools generate incomplete records. Your tech business may need legal guidance to interpret these documents correctly. Stevens Law Group helps technology companies review AIBOMs, ensuring compliance with U.S. and international regulations.
With an AIBOM, your startup gains greater control over its AI adoption. It reduces hidden risks while strengthening transparency, a combination that gives you a competitive edge in the market.
Software Supply Chain Securing AI Using DevSecOps
AI adoption does not stop at the model. The supporting frameworks, databases, and pipelines are also part of the software supply chain. This is where DevSecOps practices become essential.
DevSecOps integrates security directly into the development pipeline. Instead of treating security as an afterthought, it becomes a built-in part of CI/CD workflows. For your tech business, this means automated checks verify compliance and security at every stage.
For example, automated pipelines can confirm that an AI model meets licensing requirements before deployment. They can scan for vulnerabilities in dependencies and generate compliance records like AIBOMs automatically.
This approach reduces both risk and cost. Manual reviews are often slow and inconsistent. By contrast, DevSecOps automation provides repeatable and auditable processes. Regulators value this kind of proof, and customers see it as a sign of credibility.
Scaling also becomes easier. Large AI models can be resource-intensive. Automated pipelines help optimize builds, reducing expenses and improving performance. This efficiency is critical for startups balancing limited resources with rapid innovation.
Legal expertise complements technical DevSecOps. A firm like Stevens Law Group ensures that the compliance checks in your pipeline align with IP and licensing obligations. By combining automation with legal oversight, your technology company strengthens its position in both innovation and compliance.
The Role of Automation in Reducing Risk and Cost
As AI adoption grows, automation becomes essential. Manual oversight cannot keep up with the scale of today’s software supply chain. For tech businesses, automation reduces risk, cuts costs, and builds trust.
Automation tools track dependencies across AI systems. They can confirm the source of a model, scan for vulnerabilities, and validate licensing. This ensures your startup avoids oversights that could lead to fines or lawsuits.
Automation also addresses cost challenges. Large AI models require significant computing resources. Without optimization, build times increase, draining both time and budget. Automated caching and scaling reduce these expenses while keeping performance high.
Trust is another key benefit. Regulators, customers, and investors want proof that your business uses AI responsibly. Automated reports provide evidence of compliance and governance. This strengthens your reputation and makes your startup more attractive to partners and clients.
Still, automation alone is not enough. It must be combined with legal protections. A model may pass a technical scan but still violate copyright law. By working with intellectual property lawyers, your technology business ensures automation supports compliance without overlooking legal risks.
Building Trustworthy AI for Your Tech Business
Trust is what separates successful AI startups from those that fail. Customers will only adopt your product if they believe it is safe, legal, and reliable. To build this trust, your tech business must combine technical transparency with legal protection.
On the technical side, this means using AIBOMs, adopting DevSecOps practices, and relying on automation tools. On the legal side, it means safeguarding your intellectual property, protecting copyrights, and enforcing trademarks.
Investors also look for trustworthiness. During due diligence, they examine whether your AI supply chain complies with the EU AI Act and other regulations. Businesses that show compliance and IP protection stand out as stronger investments.
Stevens Law Group helps technology companies align innovation with legal safeguards. By ensuring compliance, protecting IP, and guiding licensing decisions, the firm enables your business to adopt AI in the software supply chain securely and strategically.
Conclusion
The rise of artificial intelligence has changed how tech businesses develop and deliver software. But while AI speeds innovation, it also increases risks in the software supply chain. These include licensing disputes, intellectual property theft, compliance failures, and loss of customer trust.
By adopting tools like the AI Bill of Materials (AIBOM), integrating DevSecOps, and using automation, your startup can secure its AI systems. But technical tools alone are not enough. Legal protections are equally critical for defending your IP rights and ensuring long-term growth.
For questions about how AI in the software supply chain may affect your tech business, please contact Stevens Law Group.
