Open-Source Model and AI Governance

A New Era of Transparency or a Regulatory Challenge?

By Patrick Upmann, Leading Expert in AI Governance and AI Ethics

The rapid development of artificial intelligence (AI) has reached a new dimension in 2025. While major tech companies like OpenAI, Google DeepMind, and Meta continue to develop proprietary AI models with immense computing power, the open-source AI movement is experiencing an unprecedented rise. Models like DeepSeek, Mistral, and LLaMA 3 have demonstrated that high-performance AI does not have to remain exclusively in the hands of a few corporations but can also be developed and used by a broad community. This has profound implications for AI governance—how AI technologies are regulated, monitored, and embedded within ethical guidelines.

A key moment in the ongoing debate was the EU’s decision not to impose the same regulatory requirements on open-source AI as on proprietary models under the AI Act. This was seen as a major victory for open-source advocates, who argue that strict regulations could stifle innovation among independent developers and research institutions. At the same time, security experts warn that open access to powerful AI models creates new risks—from deepfake manipulations to misuse by cybercriminals.

In the US and China, contrasting developments have emerged. While US authorities are increasingly discussing ways to curb open-source AI through specific security measures and access restrictions, China is focusing more on regulatory control and mandatory licensing for AI models—regardless of whether they are proprietary or open-source.

All these developments highlight that open-source AI has become a global political and economic issue. The debate is no longer just about innovation freedom versus control but also about economic competitiveness, national security, and the protection of fundamental democratic values. It is therefore essential to develop new AI governance approaches that both promote the benefits of open-source AI and minimize its risks.

Why Open-Source AI is Relevant for AI Governance

Traditionally, AI models have been developed behind closed doors by large tech companies, raising concerns about transparency, bias, and market dominance. Open-source models provide an alternative, allowing independent researchers, regulators, and businesses to analyze the source code and training data to identify biases and risks. However, the significance of open-source AI for AI governance goes far beyond that.

Advantages of Open-Source AI for AI Governance

1. Transparency and Traceability Publicly accessible AI models enable in-depth scrutiny by experts from academia, industry, and regulatory bodies. This reduces the risk of opaque decision-making and builds trust in the technology.
2. Enhanced Security Through Community Review Open-source models are continuously reviewed and improved by a broad community. Errors and security vulnerabilities can be detected and fixed faster than in proprietary solutions.
3. Democratization of AI Development Access to open-source AI makes it easier for startups, research institutions, and smaller companies to develop their own AI applications without relying on expensive proprietary solutions. This fosters innovation and competition.
4. Flexibility and Adaptability Open-source models can be tailored to specific requirements. Companies and authorities can adapt models to regional, cultural, or legal frameworks, improving global AI adoption and acceptance.
5. Prevention of AI Monopolies Open-source models reduce dependency on a few major AI providers. This strengthens fair competition and promotes a more diverse AI landscape.
6. Increased Efficiency for Businesses Companies can use open-source AI to automate existing business processes and improve data-driven decision-making without paying high licensing fees for proprietary AI solutions.
7. Support for Regulatory Compliance Open-source models offer a clear advantage in meeting regulatory requirements. Companies using such models can demonstrate that their AI decisions are transparent and verifiable.

Challenges of Open-Source AI in AI Governance

Despite its numerous advantages, open-source AI poses significant challenges that must be addressed through effective AI governance.

1. Security Risks and Misuse

Since open-source AI is accessible to everyone, there are few restrictions on who can use these technologies and for what purposes. This opens the door for misuse in areas such as:

• Deepfakes and Disinformation: By 2024, deepfake videos were already widely used for political manipulation, smear campaigns, and fraud. A Europol study predicts that by 2026, over 90% of online content will be manipulated.
• Automated Cyberattacks: Open-source AI enables cybercriminals to conduct phishing attacks, identity theft, and targeted hacking attempts with unprecedented efficiency.
• Biometric Manipulations: Criminal groups use AI to bypass facial recognition systems or create fake digital identities.

2. Regulatory Control and Liability

A central issue is the unclear responsibility for open-source models:

• Who is liable when an open-source AI model makes incorrect decisions or causes harm? The developer, the user, or the platform hosting the model?
• The EU AI Act requires transparency and audit mechanisms for high-risk AI, but open-source models are often developed in a decentralized manner and are difficult to track.
• Legal gray areas emerge when open-source models are used for safety-critical applications such as autonomous vehicles or medical diagnoses.

3. Unclear Compliance Requirements

Open-source AI models are often not subject to strict quality checks or security certifications. This presents major challenges for businesses and developers:

• GDPR and Data Protection: Many open-source models use data from unknown sources. Without full traceability, GDPR violations can occur.
• Lack of Standardization: While proprietary models often comply with regulatory requirements, open-source alternatives lack uniform testing procedures.
• Responsibility for Training and Usage: Companies integrating open-source AI must ensure compliance with legal frameworks—often without clear guidelines.

4. Lack of Governance Mechanisms and Ethical Concerns

Unlike proprietary AI, which is developed by companies with clear rules, open-source AI often lacks overarching governance structures:

• Ethical Guidelines: While companies like OpenAI have clear usage policies, open-source AI lacks binding ethical frameworks.
• Abuse Prevention: Without security mechanisms, open-source AI can be exploited for extremist propaganda, surveillance, or algorithmic discrimination.
• Manipulation Risks: Open-source models can be easily modified and used for purposes not intended by the original developers.

Conclusion and Call to Action

Open-source AI is one of the most exciting developments in today’s AI landscape. While it enables innovation, transparency, and the democratization of AI access, it also brings significant challenges regarding security, ethical responsibility, and regulation.

Governments, businesses, and civil society must actively address these challenges and develop solutions that both preserve the advantages of open-source AI and minimize its risks. Finding a balance between regulation and innovation is crucial to ensuring the sustainable and ethical use of this technology.

Take Action Now!

As a leading expert in AI Governance and AI Ethics, I help companies and organizations use open-source AI securely, in compliance with regulations, and in a strategically meaningful way.

Let’s work together to develop solutions for a sustainable and ethical AI strategy. ?? Contact me directly via LinkedIn or my website: AIGN.Global