
Kitecyber: Data & Gen AI Security, Built on the Endpoint
Your most sensitive data—customer records, source code, financial data, IP, now leaves through browsers, Gen AI prompts, SaaS uploads, and the clipboard, faster than any network tool can react. Kitecyber stops that at the source, with a single lightweight agent that runs directly on the endpoint and acts the instant data is touched, not after it's already gone.
Because it lives on the device, Kitecyber has full context: device posture, OS, process, data, user, and network activity together, in real time. That's the vantage point network- and cloud-only tools simply don't have.
Data security that keeps up with your data. Kitecyber classifies sensitive information with LLM-powered, context-aware intelligence across 80+ categories — PII, PHI, PCI, source code, IP — at over 90% accuracy, not brittle keyword matching. It tracks data lineage through screenshots, encoding, and file conversion that defeat traditional scanners, and blocks violations inline, before data ever leaves the endpoint.
Gen AI security for the age of AI agents. Kitecyber tracks sensitive data pasted or uploaded into tools like ChatGPT, Claude, and Gemini and stops it in real time. It discovers shadow AI reaching your devices and extends visibility to the AI agents now acting on your users' behalf, the blind spot identity- and network-based tools were never built to see.
Trusted globally. Kitecyber protects fintech, SaaS companies, Gen AI companies, BFSI, manufacturing, healthcare and SMB organizations across the USA, Europe, the Middle East, and APAC, and partners with GRC leaders like Vanta and Scrut Automation to unify security and compliance. It's SOC 2 Type II compliant, deploys in about a day, and delivers enterprise-grade protection without enterprise complexity.
See what full-context data and Gen AI security looks like. Learn more at kitecyber.com.
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Veeam Data Platform is a unified data resilience solution that helps organizations recover from cyberattacks and outages, and protect workloads across hybrid and multicloud environments – all without vendor lock-in.
The platform includes built-in security capabilities designed to mitigate ransomware and other data-loss threats, including AI-backed threat detection across a broad range of workload types. This is intended to help teams identify and respond to threats before they disrupt operations.
Veeam Data Platform supports workload portability across hypervisors, cloud providers, and on-premises environments, allowing organizations to move or recover data across infrastructure without being tied to a specific vendor or platform.
For organizations looking to simplify deployment, Veeam Software Appliance provides a pre-built, hardened, and highly available option, reducing the manual setup and maintenance work required from IT teams.
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Google Cloud Container Security
To bolster the security of your container environments across GCP, GKE, or Anthos, it's vital to recognize that containerization significantly enhances the efficiency of development teams, enabling them to deploy applications swiftly and scale operations to levels never seen before. As enterprises increasingly embrace containerized workloads, it becomes crucial to integrate security protocols throughout every step of the build-and-deploy process. This involves ensuring that your container management system is equipped with essential security features. Kubernetes provides a suite of powerful security tools designed to protect your identities, secrets, and network communications, while Google Kubernetes Engine takes advantage of GCP's native functionalities—such as Cloud IAM, Cloud Audit Logging, and Virtual Private Clouds—alongside GKE-specific offerings like application layer secrets encryption and workload identity, ensuring unparalleled Google security for your workloads. Additionally, maintaining the integrity of the software supply chain is of utmost importance, as it ensures that the container images you deploy are secure and free from vulnerabilities, preventing any unauthorized modifications. By adopting a proactive security strategy, you can ensure the reliability of your container images and safeguard the overall security of your applications, allowing organizations to embrace containerization confidently while prioritizing safety and compliance. This comprehensive focus on security not only protects assets but also fosters a culture of accountability within development teams.
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Confidential AI
Confidential AI provides a secure infrastructure designed for private AI inference, agent environments, and confidential computing solutions specifically aimed at AI laboratories, inference service providers, routers, and organizations handling sensitive information. Its hosted inference service leverages open-weight models that run in hardware-backed trusted execution environments (TEEs), employing technologies like Intel TDX and AMD SEV-SNP confidential virtual machines, complemented by NVIDIA GPUs in a secure computing setup. This framework guarantees that prompts, responses, and workload details are protected from infrastructure operators, allowing clients to validate attestation before sharing sensitive data. Users can either implement an OpenAI-compatible API to create inference endpoints and manage agent workloads or obtain the confidential-computing stack for deployment on their own infrastructure, bare metal servers, or across leading cloud platforms. This service proves particularly advantageous in situations where model deployment requires restricting operator access to both prompts and model assets, as well as when handling sensitive AI tasks in a dedicated confidential computing environment. Furthermore, the focus on stringent security protocols cultivates trust in the management of sensitive information, empowering clients to drive innovation while maintaining privacy and confidentiality in their operations. Ultimately, this enables organizations to navigate the complexities of AI technology with greater confidence and security.
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