Mitigating the Risks of Sensitive Data Sprawl Within Streaming Environments

In today's data-driven business landscape, data is the most valuable asset for organizations. However, with data moving across multiple systems, platforms, and locations, data sprawl has become an ever-growing concern for businesses.

The uncontrolled expansion of data makes it increasingly challenging to manage and secure data, especially in cloud and multicloud environments. While streaming services like Apache Kafka, Amazon Kinesis, or Google Pub/Sub provide exponential value to organizations by increasing the ability to share data with a variety of business lines, the risk of sending sensitive data downstream without proper identification leaves organizations vulnerable to data breaches and regulatory fines.

In this blog post, we will delve into the challenges of sensitive data sprawl within streaming environments and discuss how organizations can take steps to confidently control and secure their data in transit.

Data sprawl is the uncontrolled expansion of data across multiple systems, platforms, and locations. As more data is created and shared, it becomes increasingly difficult for businesses to track, manage, and secure their data. According to IDC, the Global Datasphere is expected to reach 175 zettabytes by 2025, highlighting the scale of the problem.   (https://www.datanami.com/2018/11/27/global-datasphere-to-hit-175-zettabytes-by-2025-idc-says/)

In traditional on-premises environments,  controlling the movement of data between systems, and who was consuming data, was much easier. A limited number of source systems pushed data to data warehouses or data marts, mainly using replication or ETL tools.

Now in the vastness of cloud and multicloud environments, the paradigm has changed. The proliferation of easy-to-pin-up data platforms has led to the generation of more data than ever, with data moving across various systems and locations, contributing to the growing problem of sensitive data sprawl. While it's now easier to set up environments, managing how data moves and is shared has become exponentially more difficult, shifting the burden from infrastructure management to data management.

Streaming services like Apache Kafka, Amazon Kenisis, or Google Pub/Sub are valuable tools that allow organizations to efficiently share data between multiple systems in cloud environments. However, these services can exacerbate the problem of sensitive data sprawl. The streaming buses act as highways for moving data traffic between various cloud-based systems, making it easy for sensitive data to be distributed to multiple systems automatically, significantly expanding the organization’s sensitive data footprint.

The problem is compounded in cloud streaming environments because consumers and systems that subscribe to a topic have access to all data within that topic. This means that whenever data is published on that topic, subscribers can import it into their own systems or republish it. If a stream contains sensitive data, that data will be compromised further if a subscriber exposes it or sends it downstream.

The first step to addressing sensitive data sprawl is to understand and manage sensitive data before it is proliferated to downstream systems. Organizations must identify which data in the streaming environment is sensitive. A solution should be used that can rapidly scan and identify sensitive data, classify and tag it. This is critical because gaining insight into where sensitive data resides, how much of it exists, and where or how systems and users are consuming it, is vital in helping to control the widespread impact of sensitive data sprawl.

Once organizations have an understanding of how sensitive data is moving, they can limit how much and what types of data are published downstream. They can also implement policies, like data masking or limiting access to certain data sets, to prevent sensitive data from being inadvertently exposed. For example, they can use data masking to hide sensitive data, limit access to certain data sets,

Data sprawl is a growing concern for businesses, and it’s essential to take steps to control and secure data. With the right tools, policies, and approaches, organizations can gain insight into their data, identify sensitive data, and protect it from exposure. As the volume and complexity of data continue to grow, data-centric security will become increasingly important in helping businesses stay ahead of the curve and protect their most valuable asset – their data.

Securiti’s Data Flow Intelligence and Governance provides a solution that enables organizations to protect this most valuable data asset. Leveraging AI and machine learning, the solution automatically identifies and tags sensitive data in streaming topics, allowing organizations to gain insight into what sensitive data exists within their streaming environment.