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Description

Gemini 3.8 Flash Cyber represents Google's most advanced cybersecurity model, offering top-tier performance in identifying vulnerabilities and automating patching processes with remarkable speed for rapid iteration. Tailored for trusted defenders, it is accessible via the Fairwind Program. On CyberGym, a recognized industry benchmark for detecting vulnerabilities, this model showcases exceptional autonomous vulnerability discovery, outperforming both Gemini 3.5 Flash Cyber and larger frontier models. Furthermore, Google assessed its effectiveness on an internal benchmark that spans complex codebases across 20 programming languages, achieving a success rate of over 70% in identifying various vulnerabilities. Unlike many models that focus on offensive strategies, Gemini 3.8 Flash Cyber emphasizes the importance of fixing vulnerabilities, providing defenders with advanced tools that enhance their ability to stay ahead of cyber attackers. This focus on proactive defense represents a crucial shift in the cybersecurity landscape, prioritizing the safeguarding of systems over mere exploitation capabilities.

Description

Microsoft Frontier Tuning enables businesses to tailor one or multiple of Microsoft’s leading MAI models to fit their specific operational requirements, allowing for training in a secure setting rather than depending on a standard AI model. The customization process begins by outlining the objectives and criteria for success, followed by integrating data, workflows, and insights gathered from Microsoft 365 and other sources. Continuous improvement is achieved through ongoing training and iterative refinement, with the model being deployed in platforms like Microsoft Foundry or Copilot, where it can enhance itself based on actual usage patterns. This innovative approach ensures that the models are well-versed in the organization’s terminology, context, processes, and expertise while maintaining strict privacy and security for all data within the client’s ecosystem. Additionally, Microsoft Frontier Tuning empowers teams with greater control over their models, minimizes the risks of vendor lock-in, and maximizes the return on investment by providing cutting-edge performance paired with exceptional token efficiency. As a result, organizations can expect to see enhanced operational effectiveness and a stronger alignment with their unique business strategies.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Bash Yes 
C Yes 
Cursor Yes 
Dart Yes 
Factory Droid Yes 
Gemini 3.5 Flash Yes 
Gemini 3.6 Flash Yes 
Gemini 3.8 Flash Yes 
Gemini Spark Yes 
Google Yes 
Google AI Mode Yes 
Google AI Ultra Yes 
Java Yes 
JavaScript Yes 
Kotlin Yes 
Microsoft Azure No 
OfoxAI Yes 
PHP Yes 
SQL Yes 
Scala Yes 

Integrations

Bash No 
C No 
Cursor No 
Dart No 
Factory Droid No 
Gemini 3.5 Flash No 
Gemini 3.6 Flash No 
Gemini 3.8 Flash No 
Gemini Spark No 
Google No 
Google AI Mode No 
Google AI Ultra No 
Java No 
JavaScript No 
Kotlin No 
Microsoft Azure Yes 
OfoxAI No 
PHP No 
SQL No 
Scala No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Google

Country

United States

Website

google.com

Vendor Details

Company Name

Microsoft AI

Founded

2024

Country

United States

Website

microsoft.ai/models/microsoft-frontier-tuning/

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