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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BAND creates robust interaction frameworks designed for enterprise-level applications of distributed AI agents. The platform facilitates immediate, collaborative interactions among both agents and humans, incorporating a runtime control plane that upholds policies, defines authority limits, and ensures transparency across diverse systems.
Additionally, BAND empowers developers, engineering teams, and leaders of enterprise platforms who are managing multi-agent ecosystems spanning internal infrastructures, SaaS solutions, and environments shared with partners. This support enhances operational efficiency and fosters innovation within complex organizational structures.
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Kodosumi
Kodosumi is a versatile, open-source runtime environment that operates independently of any framework, built on Ray to facilitate the deployment, management, and scaling of agentic services in enterprise settings. With just a single YAML configuration, it allows for the seamless deployment of AI agents, minimizing setup complexity and avoiding vendor lock-in. It is specifically crafted to manage both sudden spikes in traffic and ongoing workflows, dynamically adjusting across Ray clusters to maintain reliable performance. Furthermore, Kodosumi incorporates real-time logging and monitoring capabilities via the Ray dashboard, enabling immediate visibility and efficient troubleshooting of intricate processes. Its fundamental components consist of autonomous agents that perform tasks, orchestrated workflows, and deployable agentic services, all efficiently overseen through a user-friendly web admin interface. This makes Kodosumi an ideal solution for organizations looking to streamline their AI operations while ensuring scalability and reliability.
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Jozu
Jozu functions as an AI-driven platform focused on securing supply chains by validating artifacts prior to their execution, managing agent activities in real-time, and maintaining a record of all actions taken afterward. The Jozu Hub acts as a self-hosted repository for models, agents, MCP servers, and skills, ensuring that each artifact is consolidated with cryptographic signatures, attestations, thorough scanning, policy regulations, and audit trails. This platform's security analysis, tailored specifically for AI, addresses various threats including concealed executable code within model packages, compromised weights, data poisoning, prompt injection, insecure tools, and violations of licensing. Users can create policies once, which are then distributed as signed OCI artifacts, and these policies are enforced during the processes of pulling, promoting, admitting, or executing artifacts. Additionally, Jozu Agent Guard operates in conjunction with workloads across servers, desktops, edge devices, and isolated systems, implementing local filtering for prompts and input-output, access controls for tools, requirement for approvals, and enforcement of policies in real-time. Through this comprehensive approach, Jozu not only enhances security but also ensures a robust framework for managing and safeguarding AI-related artifacts throughout their lifecycle.
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