Immediately, we’re asserting Sec-Gemini v1, a brand new experimental AI mannequin targeted on advancing cybersecurity AI frontiers.
As outlined a yr in the past, defenders face the daunting process of securing towards all cyber threats, whereas attackers have to efficiently discover and exploit solely a single vulnerability. This basic asymmetry has made securing programs extraordinarily troublesome, 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 mix permits it to attain superior efficiency on key cybersecurity workflows, together with incident root trigger evaluation, menace evaluation, and vulnerability influence understanding.
We firmly consider that efficiently pushing AI cybersecurity frontiers to decisively tilt the steadiness in favor of the defenders requires a powerful collaboration throughout the cybersecurity neighborhood. Because of this we’re making Sec-Gemini v1 freely accessible to pick 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 Menace Intelligence (GTI), OSV, and different key information sources. Sec-Gemini v1 outperforms different fashions on CTI-MCQ, a number one menace intelligence benchmark, by at the very least 11% (See Determine 1). It additionally outperforms different fashions by at the very least 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 Menace Intelligence benchmark.
Determine 2: Sec-Gemini v1 has outperformed different fashions in a Cybersecurity Menace Intelligence-Root Trigger Mapping (CTI-RCM) benchmark that evaluates an LLM’s capacity to know 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 gives a complete description of that menace actor, due to its deep integration with Mandiant Menace intelligence information.
Subsequent, in response to a query concerning 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 in addition 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 fascinated by collaborating with us on advancing the AI cybersecurity frontier, please request early entry to Sec-Gemini v1 by way of this way.
