Best Data Management Software for Netskope

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

Kriptos is an AI & Data Governance Platform for unstructured data. It discovers, classifies, and labels sensitive documents across endpoints, servers, and cloud — including the 72% of enterprise files with no PII that traditional tools miss. Kriptos adds intelligence to your existing DLP, CASB, SIEM, and IAM stack, enabling risk-based access governance, insider threat detection, and secure AI adoption.

The NVISIONx data risk intelligence platform provides organizations with the ability to take charge of their enterprise data, thereby minimizing risks associated with data, compliance requirements, and storage expenses. The exponential growth of data is becoming increasingly unmanageable, leading to heightened challenges for business and security leaders who struggle to secure information they cannot effectively identify. Simply adding more controls will not resolve the underlying issues. With extensive and unlimited analytical capabilities, the platform supports over 150 specific business use cases, equipping data owners and cybersecurity professionals to proactively oversee their data throughout its entire lifecycle. Initially, it is essential to identify and categorize data that is redundant, outdated, or trivial (ROT), which allows companies to determine what can be safely eliminated, thereby streamlining classification efforts and cutting down on storage costs. Subsequently, all remaining data can be contextually classified through a variety of user-friendly data analytics methods, empowering data owners to assume the role of their own analysts. Finally, any data deemed unnecessary or undesirable can undergo thorough legal evaluations and records retention assessments, ensuring that organizations maintain compliance and optimize their data management strategies.