Compare Netskope vs. Noma in 2026

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Description

Today, there are more users and data outside of the enterprise than inside. This is causing the network perimeter we know to be dissolved. We need a new perimeter. One that is built in cloud and tracks and protects data wherever it goes. One that protects the business without slowing down or creating unnecessary friction. One that allows secure and fast access to the cloud and the web via one of the most powerful and fastest security networks in the world. This ensures that you don't have to compromise security for speed. This is the new perimeter. This is the Netskope Security Cloud. Reimagine your perimeter. Netskope is committed to this vision. Security teams face challenges in managing risk and ensuring that the business is not affected by the organic adoption of mobile and cloud technology. Security has been able to manage risk traditionally by using heavy-handed controls. However, today's business wants speed and agility. Netskope is changing the definition of cloud, network and data security.

Description

Transitioning from development to production, as well as from traditional data engineering to artificial intelligence, requires securing the various environments, pipelines, tools, and open-source components integral to your data and AI supply chain. It is essential to continuously identify, prevent, and rectify security and compliance vulnerabilities in AI before they reach production. In addition, monitoring AI applications in real-time allows for the detection and mitigation of adversarial AI attacks while enforcing specific application guardrails. Noma integrates smoothly across your data and AI supply chain and applications, providing a detailed map of all data pipelines, notebooks, MLOps tools, open-source AI elements, and both first- and third-party models along with datasets, thereby automatically generating a thorough AI/ML bill of materials (BOM). Additionally, Noma constantly identifies and offers actionable solutions for security issues, including misconfigurations, AI-related vulnerabilities, and non-compliant training data usage throughout your data and AI supply chain. This proactive approach enables organizations to enhance their AI security posture effectively, ensuring that potential threats are addressed before they can impact production. Ultimately, adopting such measures not only fortifies security but also boosts overall confidence in AI systems.