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Investing.com -- Google (NASDAQ:GOOGL) has announced the launch of Sec-Gemini v1, an experimental AI model designed to advance the field of cybersecurity. The model, which was outlined a year ago, is designed to help cybersecurity professionals counteract threats more effectively. The AI model is aimed at addressing the fundamental asymmetry in cybersecurity, where defenders need to secure against all threats, while attackers only need to exploit a single vulnerability.
Sec-Gemini v1 combines advanced reasoning capabilities with near real-time cybersecurity knowledge and tooling. This combination allows it to outperform in key cybersecurity workflows, including incident root cause analysis, threat analysis, and understanding the impact of vulnerabilities.
Google believes that a strong collaboration across the cybersecurity community is necessary to push AI cybersecurity frontiers effectively. As a part of this belief, Google is making Sec-Gemini v1 freely available for research purposes to selected organizations, institutions, professionals, and NGOs.
Sec-Gemini v1 has shown superior performance on key cybersecurity benchmarks due to its advanced integration of Google Threat Intelligence (GTI), Open Source Vulnerabilities (OSV), and other key data sources. The model has outperformed other models by at least 11% on the CTI-MCQ, a leading threat intelligence benchmark. It also outperforms other models by at least 10.5% on the CTI-Root Cause Mapping benchmark.
One of the key features of Sec-Gemini v1 is its ability to identify and provide a comprehensive description of threat actors. For instance, it can determine that Salt Typhoon is a threat actor, a task not all models can perform. This is due to its deep integration with Mandiant Threat Intelligence data.
Furthermore, in response to a query about vulnerabilities in the Salt Typhoon description, Sec-Gemini v1 provides not only vulnerability details, thanks to its integration with OSV data, but also contextualizes the vulnerabilities with respect to threat actors using Mandiant data. This allows analysts to understand the risk and threat profile associated with specific vulnerabilities faster.
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