Empower cybersecurity teams to significantly enhance their threat detection, management, and mitigation capabilities, directly addressing the growing challenges faced by organizations in securing increasingly complex digital environments while reducing cognitive load. 

Threat modelling is an engineering technique to identify threats, vulnerabilities, and corresponding countermeasures within a system design before attackers can exploit them. Traditional threat modelling processes are manual, inconsistent, and overly complicated, limiting their effectiveness and efficiency. Due to jargon-heavy outputs, teams are frequently burdened by outdated methods, incomplete threat vectors, and communication difficulties. As systems grow more sophisticated, manually maintained threat models rapidly become obsolete, leading to overlooked vulnerabilities and increased cybersecurity risks.

ThreatModelling-GPT addresses these challenges through automation and innovation using fine-tuned Artificial intelligence (AI), namely the latest large language models (LLMs), specifically adapted to cybersecurity contexts. This solution empowers security analysts and developers in various sectors, including finance, government, and critical infrastructure, to systematically and proactively identify potential threats from initial design stages to deployment. By automatically generating clear, structured threat analyses aligned with recognised industry frameworks such as STRIDE, MITRE ATT&CK, and NIST 800-53, the tool enhances accuracy, reduces manual effort, and improves communication across technical and non-technical stakeholders.