At the GÉANT TNC22 Conference, I presented PreventUEBA, an AI-driven cybersecurity solution designed to shift organisational security from reactive detection to proactive risk prevention.
The presentation focused on how user behaviour can be leveraged as a key signal for identifying early exposure to cyber threats. PreventUEBA analyses behavioural patterns across multiple data sources to detect risk indicators associated with activities such as phishing susceptibility, unsafe browsing, and credential misuse.
By applying machine learning techniques, the solution builds behavioural profiles and risk models that allow security teams to prioritise high-risk users and implement targeted preventive measures. This approach enables earlier intervention, reduces the likelihood of successful attacks, and improves overall security posture.
The work highlights the importance of integrating behavioural analytics into modern cybersecurity strategies, particularly within large-scale and distributed environments such as research and education networks.