Horizon Europe – Using AI in Grant Proposal Writing Responsibly

Artificial Intelligence (AI) is transforming the way research and innovation proposals are developed. While generative AI (GenAI) tools offer powerful support for drafting content quickly and efficiently, they also present serious risks in terms of eligibility, privacy, intellectual property, and copyright compliance. Understanding these risks and how to manage them is essential for applicants, consortium partners, and consultants working on Horizon Europe and other EU projects.

Quick Overview of Key Points:

• ✅ Use AI to assist, not replace, your proposal writing.
• ⚠ Follow programme-specific AI disclosure rules to prevent ineligibility.
• 🔒 Protect sensitive data by disabling model training and controlling access.
• 💡 Never input patentable information into online AI tools.
• 📜 Review copyright and ownership terms for generated content.
• 🤝 Align consortium partners and consultants on AI usage policies.

This expanded guide explains these risks in depth, integrates recent EU legal developments, and provides actionable steps to ensure compliant and responsible AI usage.

The Rapid Evolution of AI Tools

Generative AI is advancing at an extraordinary pace. New models—often released by providers in the US and China—are integrated into tools marketed specifically for grant writing. While these tools promise to save time, they raise several critical questions:

• Which AI model is the tool built on?
• What are the terms of service and data policies?
• How is data stored, processed, or deleted?
• Who may have access to the data you provide?
• Are model training and data sharing features clearly explained and controllable?

These questions are not academic. It is increasingly common for consortium members or consultants to use AI tools without informing others, introducing risks to proposal compliance, data security, and intellectual property.

Eligibility Risks: AI and Funding Compliance

For example, in 2024, a Horizon Europe consortium reportedly had a proposal flagged during evaluation because sections generated with AI lacked proper source citations and the required AI disclosure statement was missing. While the proposal was eventually corrected after review, it delayed the process and created unnecessary risk. Such cases underline why clear attribution, transparent tool usage, and compliance with programme guidelines are essential to avoid rejection.

Funding programmes are beginning to define clear rules for AI usage. For example, the Horizon Europe CSA application form (page 32) explicitly requires that applicants:

• Take full responsibility for all AI-generated content.
• Disclose the AI tools used during proposal preparation.
• Provide a list of sources for AI-generated or rewritten content.
• Be vigilant about potential plagiarism risks related to AI tools.

Failure to comply with these rules can lead to ineligibility. Different funding schemes (including Eureka) may introduce similar requirements. Always review the programme guidelines before integrating AI into the proposal-writing process.

Privacy Risks: Treat AI Tools Like Public Platforms

Consider the following scenario: a consortium member enters a draft budget and partner contact details into a cloud-based AI tool with data sharing enabled. Weeks later, this data is used to improve the AI model, inadvertently exposing confidential information to the provider’s engineers. This illustrates why AI tools must be treated as public platforms unless proven otherwise.

Generative AI tools should be approached as if your inputs are public unless proven otherwise. Many providers, such as Mistral’s “Le Chat,” include terms that allow user input and output to be used for model training. This creates specific risks when handling:

• Personal data (e.g. names and profiles of consortium members).
• Financial information and project budgets.
• Background intellectual property or unpublished research.

U.S. Data Access Laws and Extraterritorial Reach

Beyond the CLOUD Act, proposal writers should be aware of FISA Section 702, recently renewed in April 2024, which grants U.S. intelligence agencies broad access to data held by American companies—even if that data is stored on EU territory. As Microsoft France acknowledged, no EU-based server operated by a U.S. company is fully safe from U.S. data requests under these laws. This reality complicates GDPR compliance, since European organizations cannot control or even detect such access. The most reliable safeguard is using tools hosted and operated entirely within the EU, or deploying self-hosted AI under your own control to ensure true data sovereignty.

Best Practices to Mitigate Privacy Risks

• Disable data sharing for model training wherever possible.
• Ensure all partners and consultants agree to use the same privacy settings.
• Avoid entering any sensitive or confidential information into cloud-based AI tools.

Patent Risks: Protecting Intellectual Property

Patent law introduces another layer of caution. Even with data sharing disabled, many AI providers monitor user activity for “abuse detection.” This monitoring could be interpreted as public disclosure, potentially invalidating future patent claims.

EU AI Act: Transparency and Compliance for Proposal Writing

The EU’s Artificial Intelligence Act is the first comprehensive legal framework directed at AI systems, and key transparency provisions began applying from February 2025 with broader rollout through 2026 and beyond. Under the Act, providers (and indirectly users) must ensure that AI-generated content is clearly labeled. While Horizon Europe proposal writing is not classified as “high-risk”, evaluators now expect transparent documentation of any AI assistance. Teams who document their AI use, including human review and tool details, will be in a stronger position as compliance expectations tighten.

How to Reduce Patent Risks

• Do not input information related to patentable inventions into online AI tools.
• Consider using locally hosted AI solutions under a confidentiality agreement with the provider.
• Ideally, run AI models entirely in-house to maintain control over proprietary data.

Copyright and Ownership Challenges

Generative AI intersects with copyright law in two major ways:

• Training data legality: The EU DSM Directive introduced legal exceptions for text and data mining, but rights holders can opt out. This creates uncertainty around the training data used by commercial AI providers.
• Output ownership: Copyright ownership of AI-generated content varies by jurisdiction and depends on the level of human input. Some tools grant full ownership to the user, while others restrict usage or reserve rights for the provider.
• Commercial exploitation: Verify that outputs can be used commercially and are not reused to train the model.

Intellectual Property: Human Authorship as a Defensive Strategy

A recent development in EU law underscores the importance of human creative input to secure copyright protection. A Czech court ruled in 2023 that purely AI-generated images (via DALL‑E) cannot qualify as copyrighted works, because they lack a human author’s creative contribution . To avoid ownership disputes, teams should use AI as a drafting tool—not the sole author—by substantially editing outputs to ensure human authorship. One case involved a research institute that documented edits to AI‑generated diagrams; this helped establish clear ownership later in commercialization discussions.

When in doubt, examine the provider’s terms of service and keep a record of your AI-related processes for compliance audits. For example, a research institute that assumed ownership of AI-generated diagrams later found that its AI vendor claimed shared rights, leading to a costly dispute. Clear contracts can prevent such conflicts.

Key Questions to Ask

When evaluating AI for grant writing, group your questions by theme for clarity:

Data Privacy and Security

• Where is the AI model hosted (local or cloud)?
• Can data sharing for model training be fully disabled?
• How is your data stored, protected, and eventually deleted?

Legal Compliance

• Does the funding programme require AI usage disclosure?
• Are the AI models independently reviewed for reliability and compliance?
• Does the provider comply with EU privacy and copyright laws?

Ownership and Intellectual Property

• Do you or the provider own the generated outputs?
• Are you permitted to use outputs commercially without restriction?
• Does the provider reuse outputs to train its models?

Consultants and Consortium Partners

• Do consultants disclose if they use AI tools?
• Have all partners agreed on shared AI usage policies?
• Is there a mutual agreement to prevent unauthorized use of AI?

Horizon Europe: The New Reality of AI Disclosure

Horizon Europe’s updated 2025 proposal forms now require applicants to declare AI tool usage, provide sources for AI-generated content, and verify plagiarism. These clear instructions indicate that failure to comply may render a proposal ineligible. Some consortia have adopted internal AI usage logs, documenting each instance AI contributed to a draft—strengthening both transparency and evaluation confidence.

Compliance Checklist

Before using AI in grant proposal writing, confirm that:

• All tools comply with EU privacy, copyright, and data security requirements: Check whether providers offer compliance certifications (e.g. ISO 27001).
• AI usage is disclosed in line with programme rules: Keep a log of when and how AI tools were used.
• Data-sharing settings are disabled for model training: Verify and document these settings for every tool used.
• No patentable information is entered into external tools: Restrict such details to internal, offline environments.
• Outputs are reviewed for accuracy, plagiarism, and source attribution: Use plagiarism checkers and maintain a reference list.
• Consortium partners and consultants sign clear AI usage agreements: Include AI-related clauses in collaboration contracts.

Practical Recommendations for Proposal Teams

To ensure compliance and clarity, teams should establish an AI governance checklist:

• Prefer EU‑hosted or self‑hosted AI tools certified under GDPR and standards like ISO 27001 or the EU Cloud Code of Conduct.

• Anonymize or remove sensitive data before entering prompts.

• Maintain an AI usage log, listing tool names, AI contributions, and human revision steps.

• Include AI clauses in consortium contracts, specifying data handling, ownership rights, and confidentiality.

• Train team members on GDPR‑compliant AI workflows and plagiarism checks—aligned with the ERA Forum 2024 guidelines for responsible AI in research .

Conclusion

AI can be a powerful accelerator for grant proposal writing, but it must be handled with transparency, legal awareness, and strict data safeguards. By proactively addressing eligibility, privacy, copyright, and patent risks—and aligning with partners and consultants—you can ensure that AI becomes an asset rather than a liability.

Key Takeaways:

• Treat AI as a support tool, not a replacement.
• Disclose and document AI usage in compliance with funding rules.
• Protect sensitive data and intellectual property.
• Establish clear agreements with partners and consultants.

Responsible AI use is not only a compliance issue; it is a way to protect the integrity of your research, safeguard intellectual property, and build trust within your consortium.