Innovative Regulation of Open Source Intelligence and Deepfakes AI in Managing Public Trust

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Abstract
This study investigates the regulatory and ethical dimensions of Open Source Intelligence (OSINT) and deepfake technologies, analyzing their impact on public trust, privacy, and societal stability. Using data from the Global Dataset of Events, Location, and Tone (GDELT), sentiment analysis and time-series regression identified a significant decline in public sentiment (β = -0.23, p = 0.01) and societal stability due to deepfake incidents and OSINT misuse. The Deepfake Detection Challenge Dataset (DFDC) was analyzed using machine learning models, with neural networks achieving the highest accuracy (92%) and precision (91%). Regulatory frameworks were evaluated using the OECD database, where enforcement capacity demonstrated the strongest impact on reducing misuse cases (β = -0.42, p = 0.002). Recommendations include the establishment of globally coordinated regulatory frameworks, public awareness campaigns, investment in advanced detection systems, and ethical integration of AI into OSINT practices.

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