{"id":14131,"date":"2022-09-30T18:02:19","date_gmt":"2022-09-30T12:32:19","guid":{"rendered":"https:\/\/arconnet.com\/?p=14131"},"modified":"2026-03-24T14:26:23","modified_gmt":"2026-03-24T08:56:23","slug":"data-intellect-building-a-ring-fence-around-enterprise-data","status":"publish","type":"post","link":"https:\/\/arcon.xyz\/staging01\/data-intellect-building-a-ring-fence-around-enterprise-data\/","title":{"rendered":"Data Intellect: Building a Ring-Fence around Enterprise Data"},"content":{"rendered":"

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The Context<\/b><\/p>\n

In the era of digitalization, data is generated, stored, and processed every day in a huge batch size for computational and administrative purposes.\u00a0<\/p>\n

Furthermore, a growing number of organizations are managing their workloads in hybrid IT environments. And in a typical enterprise IT environment, there are hundreds of end-users, standard IT users, super users, privileged IT users, and several IT administrators accessing various types of IT resources every now and then.<\/p>\n

As a result, data of different patterns, such as generic data, critical data, or highly confidential data, is generated. However, do organizations keep track of such data patterns? Do organizations follow a data storage methodology category-wise, or importance-wise? How do organizations know which data is exposed to security vulnerabilities?\u00a0<\/p>\n

In this backdrop, if we consider the IT security perspective, isn\u2019t it crucial for an organization to understand the data contextuality? If there is no segregation methodology to understand who, where, what, and when of data usage, then it could definitely invite complex data security vulnerabilities within the IT environment.\u00a0<\/p>\n

In order to make sense of every bit of data, there has to be a contextualization of data. To overcome this challenge, Data Intellect, which is a component of ARCON | Endpoint Privilege Management (EPM)<\/a>, streams every bit of data in an organization into an AI\/ML model to understand the contextual patterns of data.\u00a0<\/p>\n

The use case<\/b><\/p>\n

Let us think of a scenario.\u00a0<\/b><\/p>\n

A typical organization has various departments like finance, administration, marketing, HR, and IT. To segregate deeper, the IT department has a host of software developers, software analysts, programmers, quality assurance teams, etc. Every user from every team accesses different systems or applications for different tasks at different hours and generates a considerable amount of data every day. Does the organization perform any analysis of this accumulated data? Also, what about the data that is processed consistently?<\/p>\n

Here we come to know the importance of data analysis and data contextualization. From a security perspective, the first and foremost question that comes to our mind is why does a developer from the IT department need access to the sensitive data of marketing or finance? Isn\u2019t that an unnecessary access? It automatically broadens the risk of unauthorized access.<\/p>\n

By not having data intellect model, an organization risks:\u00a0<\/p>\n