VERITASCORE: A MULTI-AGENT CONCEPTUAL FRAMEWORK FOR SECURING SCIENTIFIC DISCOVERY

The agile growth of AI ushers in a new era of scientific and technological progress. It examines how the fast development of artificial intelligence is advancing scientific and technological progress while also creating new and complex security risks. This study compares traditional cybersecurity approaches with modern AI-driven security techniques that shape today’s threat landscape. It also reviews top-down transparency research and assesses how AI alignment methods can be applied to key safety concerns, including honesty, harmlessness, power-seeking behaviour, and resilience to manipulation. The findings show that AI-based transparency and security methods can effectively address a wide range of safety-critical challenges. These approaches demonstrate strong potential in identifying malicious behaviour, reducing system vulnerabilities, and improving the reliability and trustworthiness of AI systems used in scientific research. The analysis highlights the growing importance of AI-driven defences in countering advanced cyber threats. The paper outlines strategies for protecting the scientific “discovery engine” by securing models and datasets against adversarial machine-learning attacks such as data poisoning and model manipulation. It illustrates how AI-enabled security solutions can be integrated into scientific workflows to safeguard infrastructure, maintain data integrity, and ensure dependable research outcomes. This paper brings together AI-driven scientific innovation with cybersecurity and transparency research. By extending AI alignment and safety techniques to protect scientific models and data, it offers a novel framework for building secure and trustworthy AI-powered discovery systems.