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What is Devici?

Devici helps security and engineering teams build threat models that are consistent, reviewable, and easy to maintain as systems evolve. It focuses on capturing the architecture first, using a visual canvas where teams lay out processes, data stores, external entities, and trust boundaries. As components are described and attributes are applied, Devici identifies relevant threats and suggested mitigations from its built-in library, reducing the manual effort normally required to reason through diagrams or ad-hoc checklists. The tool is designed so that AppSec specialists, developers, and security champions can work together without specialized training. Real-time editing, in-model comments, and version history support collaborative reviews and allow teams to trace how decisions and risks change over time. Reusable templates and patterns make it simple to model common services or shared architecture pieces, helping larger organizations avoid duplicated work across teams. Devici also supports pushing mitigation tasks into existing development workflows through optional integrations, ensuring threat modeling outputs don’t remain isolated from day-to-day engineering. Its structure helps organizations move away from one-off diagrams and inconsistent documentation toward a predictable, repeatable approach that fits into modern development practices. Overall, Devici offers a practical way to raise the quality and clarity of threat modeling efforts without requiring complex onboarding or heavyweight tooling.

What is AppGuard?

Each day, adversaries develop over a million new types of malware. Traditional security solutions heavily rely on historical threat data, employing techniques such as behavioral analytics, artificial intelligence, and pattern recognition to spot malware; however, these methods often fall short in providing adequate real-time defense against new strains that may have unique features not seen before. While current security approaches emphasize malware detection, it raises the question of whether this strategy is indeed the most effective way to ensure safety. A variety of techniques are used for malware identification; for example, antivirus software depends on signature databases from past threats, machine learning algorithms generate predictive models using historical data, and behavioral analytics scrutinizes previous threats to forecast future actions. The main limitation of detection-oriented technologies is their dependency on existing malware information, which means they can only pinpoint threats already recognized and are likely to overlook emerging or adaptive threats. As the cyber threat landscape evolves rapidly, it becomes ever more vital to investigate proactive strategies that can foresee and reduce risks before they occur. Emphasizing prevention over detection may lead to a more robust defense against the continual barrage of new malware.

Media

Media

Integrations Supported

SD Elements

Integrations Supported

SD Elements

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Security Compass

Date Founded

2004

Company Location

Canada

Company Website

www.securitycompass.com/devici/

Company Facts

Organization Name

AppGuard

Company Location

Japan

Company Website

www.blueplanet-works.com/en/solution/appguard.html

Categories and Features

Categories and Features

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