Jscrambler is the leader in Client-Side Security, protecting the code, data, and digital interactions that power today’s modern web applications.
Modern applications are changing rapidly as development teams adopt AI-generated code, rely on third-party software components, and embed AI-powered agents into digital experiences. Meanwhile, sensitive information is increasingly created and processed in the browser itself. This creates a critical gap in enterprise security: organizations need to govern not only what code enters an application, but how that code, scripts, and data behave when the application runs.
Jscrambler closes that gap with a Client-Side Security Platform built around its Behavioral Enforcement Core. The platform continuously monitors and enforces how application code, including AI-generated code, third-party scripts, AI agents, and sensitive data behave in the browser. By enforcing software integrity and data governance at runtime, Jscrambler gives organizations control at the point where code executes and data is created—before sensitive information can be exposed or transmitted.
Organizations across retail, financial services, travel, healthcare, and other industries use Jscrambler to detect and stop client-side attacks, protect against risks introduced by AI-developed and third-party code, control AI-driven data flows, and strengthen compliance with requirements including PCI DSS, GDPR, HIPAA, CIPA, CCPA, and the EU AI Act.
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Aikido is the all-in-one security platform for development teams to secure their complete stack, from code to cloud. Aikido centralizes all code and cloud security scanners in one place.
Aikido offers a range of powerful scanners including static code analysis (SAST), dynamic application security testing (DAST), container image scanning, and infrastructure-as-code (IaC) scanning.
Aikido integrates AI-powered auto-fixing features, reducing manual work by automatically generating pull requests to resolve vulnerabilities and security issues. It also provides customizable alerts, real-time vulnerability monitoring, and runtime protection, enabling teams to secure their applications and infrastructure seamlessly.
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F5 NGINX Gateway Fabric
The NGINX Service Mesh, which is always available for free, transitions effortlessly from open source projects to a robust, secure, and scalable enterprise-grade solution. With NGINX Service Mesh, you can effectively manage your Kubernetes environment, utilizing a cohesive data plane for both ingress and egress, all through a singular configuration. The standout feature of the NGINX Service Mesh is its fully integrated, high-performance data plane, designed to harness the capabilities of NGINX Plus in managing highly available and scalable containerized ecosystems. This data plane delivers unmatched enterprise-level traffic management, performance, and scalability, outshining other sidecar solutions in the market. It incorporates essential features such as seamless load balancing, reverse proxying, traffic routing, identity management, and encryption, which are crucial for deploying production-grade service meshes. Additionally, when used in conjunction with the NGINX Plus-based version of the NGINX Ingress Controller, it creates a unified data plane that simplifies management through a single configuration, enhancing both efficiency and control. Ultimately, this combination empowers organizations to achieve higher performance and reliability in their service mesh deployments.
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open-appsec
open-appsec is an open-source initiative that builds on machine learning to provide pre-emptive web app & API threat protection against OWASP-Top-10 and zero-day attacks.
It can be deployed as add-on to Kubernetes Ingress, NGINX, Envoy and API Gateways.
The open-appsec engine learns how users normally interact with your web application. It then uses this information to automatically detect requests that fall outside of normal operations, and sends those requests for further analysis to decide whether the request is malicious or not.
open-appsec uses two machine learning models:
1. A supervised model that was trained offline based on millions of requests, both malicious and benign.
2. An unsupervised model that is being built in real time in the protected environment. This model uses traffic patterns specific to the environment.
open-oppsec simplifies maintenance as there is no threat signature upkeep and exception handling, like common in many WAF solutions.
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