NEXUSAI

My main use case for ThreatModeler Platform is for reviewing client applications end to end, where many applications developed by their team create DFDs in the tool. We review those applications and modify them in the tool if they are not well designed and identify threats, some of which are tool-generated, based on the application components. We check the countermeasures, generate a final report, and that encompasses our end-to-end use case.

A specific example of a project I reviewed using ThreatModeler Platform involved a banking client's account opening procedure application. In this application, users manually provide details to a bank executive at the branch, which are processed through a web UI to a database. Upstream and downstream applications fetch this data to offer services to users. We created the DFD in ThreatModeler Platform, identified potential threats, and assessed data flow and security measures, documenting findings for our final report.

Regarding my main use case with ThreatModeler Platform, I believe we have covered most aspects, including the application assessment process. The tool significantly aids our efforts in this regard.

ThreatModeler Platform positively impacts my organization by integrating with the initial design stages of application development, allowing teams to check designs and fit it into their ecosystem, thus serving as a foundation for other processes like PT, SAST, and DAST by providing relevant references.

ThreatModeler Platform has definitely reduced the time spent on manual reviews compared to other tools, while also offering unique features that cover a broad spectrum of threats, producing comprehensive and optimal findings.

ThreatModeler Platform's ability to measure and mitigate risks has greatly assisted my organization in adapting to diverse and evolving attack surfaces, becoming a vital tool across our security ecosystem.

ThreatModeler Platform has helped our security team keep pace with DevOps sprints, ensuring that rapid application changes can be reviewed quickly, enabling better overall efficiency.

Since implementing ThreatModeler Platform, I have observed improved application coverage, as legacy applications now undergo reviews that were previously neglected, achieving coverage of around 85 to 90% of applications.

ThreatModeler Platform has enabled us to meet tight delivery dates by allowing for quick updates and proactive reviews, which greatly contributes to our overall efficiency.

The best features of ThreatModeler Platform include the ability to customize components by adding manual countermeasures and threats, and a highly specific, up-to-date list of questions, which recently produced excellent results with AI applications. The integration with other applications like Jira, commonly used for ticketing, is also a standout feature I appreciate.

Out of the features I mentioned, the up-to-date threat library and countermeasures have had the biggest impact on my workflow, as they have optimized the review process, reduced research work, and tracked current threats effectively, with parameters updating frequently to benefit users.

Customizing threat framework components in ThreatModeler Platform was initially challenging, but with the development team's adaptation and guide provided, we overcame those hurdles, leading to successful collaboration on diagram design.

To improve ThreatModeler Platform, I suggest adding more components and increasing AI usage, particularly for automating DFD creation and customizing threat assessments, which could greatly enhance the tool's functionality.

Regarding improvements, I would suggest enhancing the speed, as I have noticed delays during model updates and loading, which seem to be a recurring issue despite being possibly regional.

I have been using ThreatModeler Platform for around the past four years, and I used it before the merger; it has now migrated to IriusRisk, which I currently use, although I used ThreatModeler Platform about six to seven months back.

ThreatModeler Platform is stable, but some performance issues persist, particularly loading and model refresh times, which need improvement.

ThreatModeler Platform's scalability with AI-driven component suggestions indeed benefits my team, as we have seen positive outcomes from AI applications, although evolving threats require continuous updates to maintain effectiveness.

Customer support is good, with proactive responses and a reliable ticketing process, along with regular check-ins from the support team.

ThreatModeler Platform was our first solution, and we did not use any previous tools, as we operated entirely manually before its adoption.

ThreatModeler Platform has influenced our training costs and reduced project hours by approximately 30 to 40%, yielding significant time and cost savings.

We definitely see a return on investment; for instance, the number of employees needed for application reviews has decreased from five to two or three, significantly increasing productivity and the effectiveness of threat identification.

Regarding pricing, I find it competitive with industry peers, and while it may be more effective, I am uncertain how it compares with other vendors, though it has reduced several interdependent costs for us.

We evaluated IriusRisk, Microsoft Threat Modeler, and SD Elements, ultimately choosing to proceed with ThreatModeler Platform after thorough research.

I would rate ThreatModeler Platform an 8 out of 10.

I assign an 8 rating primarily because it offers industry-leading features and has proven itself as a reliable tool over the years, though I feel there are opportunities for advancement compared to some competitors.

Concerning ThreatModeler Platform's AI capabilities, I believe governance is crucial to comply with local laws and regulations, and AI feature integration needs to prioritize customer data privacy to foster trust and adoption.

The accuracy and reliability of ThreatModeler Platform's output depend on model training; if we execute well-structured test cases, I believe both aspects will improve, enhancing customer satisfaction as users experiment more with the features.

ThreatModeler Platform is deployed in both cloud and on-premises environments within my organization.

We use multiple cloud providers, including Azure, AWS, and Alibaba for some applications in China due to the size of our organization.

The purchase of ThreatModeler Platform was made through the AWS Marketplace, and besides improvements already mentioned, nothing else comes to mind.

We have yet to explore the workflow automation and governance feature launched in version 7.3.

My advice for others considering ThreatModeler Platform is that it is a strong tool adaptable to both small and large organizations.

Our main use case for ThreatModeler Platform is application threat modeling during the design and development life cycle. We use it to analyze the application architecture, data flows, trust boundaries, and potential attack paths. This platform helps to automatically identify security threats based on the application model, allowing us to review and prioritize the findings with the application and security teams. This approach helps us identify and address security risks earlier before the application moves into production.

In a recent application onboarding, we used ThreatModeler Platform to model the applications and data flows, external interfaces, and trust boundaries before they moved towards production. During the assessment, the platform identified potential security risks around an external application component and its communication path to the back-end service. The results and findings with the application and security teams validated the actual architecture, and the team implemented the required access controls and security measures before deployment. This proactive approach helped us identify risks earlier and avoid issues during security testing.

The automated threat identification capabilities of ThreatModeler Platform are one of the most useful features for us. It quickly highlights potential risks from the application architecture itself, while the visual modeling of application components, data flows, and trust boundaries makes complex decisions easier to understand. I also find the centralized threat tracking application helpful, as it standardizes threat modeling across different applications instead of relying completely on manual assessments. Its integration with development and security workflows is another strong advantage.

ThreatModeler Platform positively impacts my organization by making application security more proactive and consistent across our development processes. It allows us to identify architectural security risks earlier, preventing significant escalation problems when moving applications into production without addressing vulnerabilities. The automated threat identification reduces manual analysis efforts from the security team, improving collaboration between security, application, and architecture teams. It provides a common view of threats and mitigations of vulnerabilities, ultimately helping us reduce late-stage security findings and making threat modeling a structured part of our SDLC.

I would like to see deeper integration of ThreatModeler Platform with CI/CD pipelines, cloud platforms, and infrastructure as code tools. More automation to automatically update threat models when application architecture changes would reduce manual maintenance. AI-driven recommendations for threat prioritization and mitigation could expedite reviews significantly. Better integration with vulnerability management and ticketing platforms would enhance end-to-end tracking. Overall, increased automation and real-time integration would make ThreatModeler Platform even more effective in the DevSecOps environment.

I would like to see improvements mainly in the user interface and integrated reporting capabilities of ThreatModeler Platform. A more modern and intuitive UI would make it easier for new users to create and navigate complex models. Reporting could offer more customizable dashboards and executive-level summaries, so more flexible report options would also assist in sharing security findings with application and management teams. These enhancements would improve the platform's usability and reporting experience.

We have covered most improvement areas needed for ThreatModeler Platform, such as real-time integration with CI/CD pipelines. In terms of reporting, I mentioned earlier the need for clear and easily understandable security documentation. The UI could become more intuitive when working with large and complex application models, and stronger AI-based threat prioritization and remediation recommendations would minimize manual review efforts. Enhanced integration with cloud and ticketing or vulnerability management platforms would also provide a more complete end-to-end security workflow.

I have been using ThreatModeler Platform for around two to three years, initially working with version seven and currently using version seven point five.

ThreatModeler Platform is very stable based on my experience, especially for our applications security workflow. We started with version seven point two and seven point three, and currently use version seven point five. The platform handles multiple application assessments and tracking mitigation activities without major disruptions. Overall, I would rate the stability around nine out of ten, with room for continuous improvements in usability and integrations.

In terms of scalability, ThreatModeler Platform has proven to be highly scalable for our environment. We manage threat models for multiple applications from a central platform, and as our application portfolio expands, we can create separate models while maintaining consistent security standards. The automated threat identification helps the security team handle more assessments without increasing manual efforts, allowing multiple application and security teams to collaborate on different models. Overall, I would rate scalability around nine out of ten for enterprise applications in a security environment.

Customer support for ThreatModeler Platform is very good, and I have had a positive experience overall.

Before ThreatModeler Platform, we primarily handled threat modeling activities using manual architecture reviews, security checklists, and documentation-based assessments. That approach worked well for individual applications but became difficult to scale as our application portfolio grew. For instance, earlier manual architecture reviews wasted two to three hours of time due to increased manual effort. We adopted ThreatModeler Platform to analyze processes and automate initial threat identification, with visible modeling and centralized tagging facilitating collaboration between security and application teams. The transition aimed to make threat modeling more consistent, scalable, reliable, and efficient.

The initial setup of ThreatModeler Platform is manageable, and we were able to integrate it into our existing application security workflows without major infrastructure changes. Licensing and pricing are handled by our procurement and vendor management teams, so I do not have direct visibility into contract values or specifics. However, the platform effectively reduces manual modeling effort and streamlines implementation. I would recommend evaluating licenses based on the number of applications, users, and required integrations, as the overall cost is reasonable compared to the security and efficiency benefits.

We purchased ThreatModeler Platform through the Azure Marketplace as part of our organization's standardized enterprise vendor and procurement process. Our technical team is responsible for using and administrating the platform, while recruitment and vendor management teams handle the commercial agreement. We use ThreatModeler Platform as part of our application and security workflow across our hybrid environment. My experience is primarily from the technical and operational side rather than the purchasing process.

We primarily see a return on investment through time savings and reduced manual effort. For typical application assessments, automated threat identification saves approximately one to one point five hours compared to manual initial assessments, and sometimes it can save even up to two hours. This helps us identify and fix assessments, enabling earlier resolution of security issues before deployment, minimizing rework during late-stage security reviews. This standardized workflow allows our security team to handle more applications without proportionally increasing manual efforts, resulting in improved efficiency and faster security assessments.

I was not offered a gift card or incentive for this review.

We did not evaluate other options before choosing ThreatModeler Platform. Our previous methods only included manual architecture reviews, which consumed considerable time and resources. Thus, we switched to models like ThreatModeler Platform, standing out due to its combination of visual architecture modeling and automated threat identification, prompting us to adopt this platform.

ThreatModeler Platform fits into our workflow mainly during the application design and security review stage. We use it before production deployment to understand the application architecture, data flows, trust boundaries, and potential threats. The security team generates findings with the application and architecture teams and tracks the required mitigations. It complements our vulnerability scanning and other security testing rather than replacing them, helping us shift security assessments earlier in the SDLC and providing a more consistent threat modeling process.

The automated threat identification feature has made our process more efficient by providing a quick initial assessment of potential threats. Instead of relying solely on manual analysis, once we build the application model, ThreatModeler Platform identifies relevant threats based on the architecture and data flows. This allows our security team to focus more on validating findings and collaborating with developers on mitigation. It also ensures a consistent approach across different applications, overall reducing manual analysis efforts and helping us identify security risks earlier in the development lifecycle.

One feature I particularly value is the combination of visual threat modeling and threat identification, which makes complex application architecture easier for both security and development teams to understand. The centralized tracking of threats and mitigations also improves collaboration and follow-up. Version seven point five provides a smoother experience compared to older versions like seven and seven point two, making features more consistent and preferable in our approach to application security.

In our hybrid cloud environment, we primarily utilize Microsoft Azure alongside our own on-premises infrastructure. ThreatModeler Platform is integrated into our application security workflow across both environments before deployment. These threat models help us maintain consistent security reviews based on both on-prem and Azure-hosted applications, which is particularly useful when applications have dependencies across both environments.

ThreatModeler Platform is deployed as part of our application security and threat modeling workflow. We have been using this platform for around three years, initially with version seven and currently with version seven point five. Security and application teams access the platform to create and maintain threat models for the application during design and review stages, considering components, data flows, trust boundaries, and external interfaces. The platform serves as our centralized threat modeling solution in a hybrid cloud environment.

ThreatModeler Platform helps us assess risks across different application attack surfaces in a structured way. We model components such as internet-facing interfaces, APIs, authentication points, data stores, and back-end services, along with the data flows between them. The platform assists in identifying threats associated with those components and trust boundaries. We review findings with application and security teams, prioritizing applicable risks for mitigation, giving us better visibility into the overall attack surface rather than considering individual components in isolation.

ThreatModeler Platform has indeed helped our security team keep pace with DevOps sprints by integrating threat modeling earlier into the application development processes. For instance, when application architecture or APIs change during a sprint, we can utilize the platform to update the threat model and quickly review newly identified risks. Automated threat identification reduces the time required for initial security assessments, allowing security and development teams to focus on validating relevant findings and tracking remediation. This integration helps avoid making threat modeling a separate activity that delays sprint production releases.

We primarily see a return on investment through time savings and reduced manual effort. For typical application assessments, automated threat identification saves approximately one to one and half hours compared to manual initial assessments, and sometimes it can save even up to two hours. This helps us identify and fix assessments, enabling earlier resolution of security issues before deployment, minimizing rework during late-stage security reviews. This standardized workflow allows our security team to handle more applications without proportionally increasing manual efforts, resulting in improved efficiency and faster security assessments.

The flexibility to automate specific parts of our threat modeling process with ThreatModeler Platform reduces repetitive manual training necessary for day-to-day assessments. Once we establish a standard modeling workflow, team members can follow a consistent approach rather than learning different methods for every application. While new team members still require training on ThreatModeler Platform and threat modeling, the standardized workflow simplifies onboarding. I did not measure a specific percentage reduction in training costs, but the primary benefit lies in less training effort and faster onboarding.

ThreatModeler Platform enables our organization to meet tight delivery dates for product teams by providing quick initial assessments through automated threat identification. This feature is invaluable when an application has a short release window. We can swiftly review the architecture, validate, generate threads, and focus solely on the high-priority areas on the list to reduce security review delays, allowing application teams to address issues promptly before the release. It is especially useful when multiple applications require security assessments within the same delivery cycle.

ThreatModeler Platform has benefited our team by making the threat modeling process more structured and consistent. Automated threat identification reduces the amount of manual analysis needed for each application, providing a common platform for security and applications teams to review architectural risks and track mitigations. In practice, it helps us complete the initial threat assessments faster and identify issues earlier in the SDLC. Overall, this improvement enhances team efficiency and collaboration without eliminating the need for technical validation. I rate my overall experience with ThreatModeler Platform as nine out of ten.