Balancing accessibility and security: Safeguarding citizen-sourced biodiversity data in the age of AI and open-sourced software

Artificial Intelligence (AI) and open-source software are revolutionizing biodiversity monitoring by democratizing access to citizen-science datasets. While these advancements facilitate conservation efforts and scientific research, they pose significant risks for data misuse. Researchers who reduce barriers to accessing such biodiversity datasets are responsible for safeguarding sensitive data. • Citizen-sourced biodiversity data from platforms like iNaturalist, eBird, and social media are revolutionizing biodiversity monitoring but also introduce new risks. • Open-source software and AI tools enable large-scale, low-cost biodiversity analysis, but location precision increases vulnerability to misuse (e.g., poaching, privacy violations). • Risks extend beyond species to include culturally significant landscapes and Indigenous knowledge, highlighting the need for consent-based, community-led safeguards. • We propose workflows where AI assists in safeguarding data, including content-aware screening, metadata sanitation, and automated obfuscation of sensitive features. • We call for ethical frameworks and inclusive governance to ensure that citizen-sourced biodiversity data benefit conservation while protecting people, species, and cultures.