Data Intellect: Building a Ring-Fence around Enterprise Data

The Context

In the era of digitalization, data is generated, stored, and processed every day in a huge batch size for computational and administrative purposes. 

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.

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? 

In this backdrop, if we consider the IT security perspective, isn’t 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. 

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), streams every bit of data in an organization into an AI/ML model to understand the contextual patterns of data. 

The use case

Let us think of a scenario. 

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?

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’t that an unnecessary access? It automatically broadens the risk of unauthorized access.

By not having data intellect model, an organization risks: 

• Misuse and abuse of data 
• Exposing data 
• Loss of data 
• Illegitimate access to data 
• No classification of data based on its importance 

Today, while we write and discuss a lot about secure access control, security of digital identities, endpoint protection, cloud access security, at the same time, data assessment, data segregation, and contextualization of data are equally important to building the foundation of a comprehensive IT security infrastructure. Without knowing who is accessing what and not building a solid ring fence around data, the insider threat vector intensifies.

 If we do not track what has happened with a specific set of data (like who has accessed it, when and why), then we might end up exposing the data beyond analysis and security.

Solution

Monitoring this sea of data is practically a herculean task. As an information security solution expert, ARCON has developed an interactive analytical system known as “Data Intellect,” a critical component of a robust Endpoint Privilege Management (EPM) solution.

 This AI/ML-based tool builds a ring-fence around enterprise data even at granular levels. This contextual security layer is developed based on the user’s login behaviour and profile activities in a certain period of time. As a result, it creates a category of accessed data for the user and restricts access to other categories, reducing the risk of unauthorized access.

 Data Intellect enables the classification of data, itemization of the exposed data, categorization of the critical data, and understanding of the “where” and “what” of data. With this, it provides actionable insights on the data that is useful for forensic analysis and overall information security. Indirectly, it helps the organization stay compliant with the regulatory requirements.

Conclusion

Data Intellect contributes to wiser decision-making of an organization’s access management practice. It leverages AI and ML technologies to build a solid ring-fence around enterprise data and helps understand the patterns and contextualization of the data.