ThreatModeler, provider of the industry’s #1 Automated Threat Modeling Platform, announced the release of its ThreatModeler Cloud Edition for Azure. The new release follows the recent availability of the Cloud Edition for AWS. The Azure-specific edition provides developers with a tool to proactively spot and mitigate threats early in the development cycle. It works seamlessly with Azure’s built-in security controls that include identity and access management, firewalls and DDoS protection tools, encrypted key vaults, and tools to spot security issues within Azure workloads.
Companies moving their digital assets to cloud infrastructures can operate with more confidence through usage of ThreatModeler’s platform. It consistently and concretely identifies threats, whether they’re in-house, on AWS or the Azure platform. It’s the only automated threat modeling solution that features a wizard-style plugin that allows companies to create threat model diagrams in minutes and receive instant actionable insights. These diagrams indicate the workflow of system components and allow threats to be uncovered in a visual representation.
ThreatModeler provides out-of-the-box capabilities with a library of Azure architectural components unique to the platform. It also features a custom threat library that covers all of the issues that can threaten Azure environments.
“We’re the leading threat modeling platform because we consistently innovate and provide customers with powerful and easy-to-use tools,” said†Archie Agarwal, CEO and Founder of ThreatModeler. “Our new ThreatModeler Cloud Edition for Azure follows our AWS edition launch and represents our continued focus on helping firms move securely to the cloud. With our custom threat library, we save our clients time and money and allow them to trace specific threats back to their root causes. Companies are seeing the benefits of the proactive instead of reactive security approach, and ThreatModeler is leading that charge into the future.”
ThreatModeler allows clients to reduce their reliance on full-time internal cybersecurity staff by automated threat detection early in the development cycle.
