Right now, we’re saying Sec-Gemini v1, a brand new experimental AI mannequin centered on advancing cybersecurity AI frontiers.
As outlined a yr in the past, defenders face the daunting job of securing towards all cyber threats, whereas attackers have to efficiently discover and exploit solely a single vulnerability. This basic asymmetry has made securing techniques extraordinarily tough, time consuming and error inclined. AI-powered cybersecurity workflows have the potential to assist shift the steadiness again to the defenders by pressure multiplying cybersecurity professionals like by no means earlier than.
Successfully powering SecOps workflows requires state-of-the-art reasoning capabilities and in depth present cybersecurity data. Sec-Gemini v1 achieves this by combining Gemini’s superior capabilities with close to real-time cybersecurity data and tooling. This mixture permits it to realize superior efficiency on key cybersecurity workflows, together with incident root trigger evaluation, menace evaluation, and vulnerability influence understanding.
We firmly imagine that efficiently pushing AI cybersecurity frontiers to decisively tilt the steadiness in favor of the defenders requires a robust collaboration throughout the cybersecurity group. Because of this we’re making Sec-Gemini v1 freely obtainable to pick out organizations, establishments, professionals, and NGOs for analysis functions.
Sec-Gemini v1 outperforms different fashions on key cybersecurity benchmarks on account of its superior integration of Google Risk Intelligence (GTI), OSV, and different key information sources. Sec-Gemini v1 outperforms different fashions on CTI-MCQ, a number one menace intelligence benchmark, by a minimum of 11% (See Determine 1). It additionally outperforms different fashions by a minimum of 10.5% on the CTI-Root Trigger Mapping benchmark (See Determine 2):
Determine 1: Sec-Gemini v1 outperforms different fashions on the CTI-MCQ Cybersecurity Risk Intelligence benchmark.
Determine 2: Sec-Gemini v1 has outperformed different fashions in a Cybersecurity Risk Intelligence-Root Trigger Mapping (CTI-RCM) benchmark that evaluates an LLM’s capability to grasp the nuances of vulnerability descriptions, establish vulnerabilities underlying root causes, and precisely classify them in line with the CWE taxonomy.
Beneath is an instance of the comprehensiveness of Sec-Gemini v1’s solutions in response to key cybersecurity questions. First, Sec-Gemini v1 is ready to decide that Salt Hurricane is a menace actor (not all fashions do) and offers a complete description of that menace actor, due to its deep integration with Mandiant Risk intelligence information.
Subsequent, in response to a query in regards to the vulnerabilities within the Salt Hurricane description, Sec-Gemini v1 outputs not solely vulnerability particulars (due to its integration with OSV information, the open-source vulnerabilities database operated by Google), but additionally contextualizes the vulnerabilities with respect to menace actors (utilizing Mandiant information). With Sec-Gemini v1, analysts can perceive the chance and menace profile related to particular vulnerabilities quicker.
If you’re involved in collaborating with us on advancing the AI cybersecurity frontier, please request early entry to Sec-Gemini v1 through this manner.
