Welcome to our Blog

Release 4.31 contains several changes and enhancements including: importing OTM YAML files through the UI, a revamped Technical Countermeasures Report, and user collaboration in Community Edition.

The wait is over - real AI powered threat modeling is here! Over 100 Azure V2 components to deliver unparalleled accuracy and effective threat modeling.

Building a robust LLM pipeline on Azure requires careful consideration of various factors, including data management, model training, deployment, monitoring, and feedback loops.

In a world where artificial intelligence (AI) is no longer the stuff of science fiction but a tangible reality, the EU Artificial Intelligence Act’s (EU AI Act) emphasis on transparency and risk management aligns well with security-by-design best practices. However, it also introduces additional layers of complexity.

Google Cloud Platform (GCP) provides a versatile toolkit to create a scalable and secure infrastructure for these complex models. Let's explore the technical components and protocols involved in building an LLM pipeline on GCP.

Our best product yet has landed. Bringing unrivalled ease of use, together with an emphasis on speed and scale. Our many advancements combine to bring you faster navigation of the application and intuitive building of threat models.

Each step of the AWS LLM pipeline offers the flexibility and the performance these complex models need. But with this power comes responsibility.

We take a look at the varying roles prompt engineering can play, potential bias within your own prompts, as well as how it can aid threat modeling and creation of secure architecture.

We recommend six core application security activities to identify, manage and mitigate risk effectively; Threat Modeling, Secure Coding, Security Testing, Access Control, Encryption and Patch Management are crucial for AppSec.