Dark Data: The Cloud’s Unknown Security And Privacy Risk
Over the last few years, multicloud frameworks have drifted into the mainstream. Organizations now create, store and manage enormous volumes of data across different cloud platforms. Despite the magnitude of this trend, there’s a stark and often disturbing truth: Businesses often have little or no visibility into much of the data that resides in these clouds.
The culprit? Dark data.
As organizations construct multicloud frameworks and slide data across various containers and components, identifying and assessing all the data is extraordinarily difficult. Not surprisingly, dark data presents significant security and compliance risks. There is no easy way to identify data systems, what data is held in cloud components and how this data is protected.
As a result, organizations must advance beyond basic inventory tools that deliver limited insights into cloud data. It’s critical to adopt a more sophisticated approach that provides comprehensive visibility into the organization’s asset footprint and enables specific steps to reduce risk.
Multicloud environments aren’t going away. Upwards of 90% of organizations rely on multicloud environments, and adoption rates continue to rise, according to Statista.
At the heart of the dark data problem is a basic fact: Multicloud environments significantly expand data storage and security concerns. Traditionally, organizations rely on native content security policy tools to oversee cloud inventory, such as AWS Systems Manager, Azure Security Center asset inventory and Google Cloud asset inventory.
Yet, these tools, while valuable, can’t detect dark data since their primary focus is to deliver native asset discovery. In addition, these content security policy tools don’t provide multicloud visibility. This means that when an enterprise forklifts data from unstructured data systems (MySQL, Hbase, Elastic, Mongo, Redis and others), they risk the data flying under the radar and going dark.
This problem occurs as organizations move data to their preferred cloud service providers without making any technical changes to the underlying data systems. While the process is fast and simple because there’s no need to worry about changing the underlying infrastructure or tables, schemas and other elements, security and data compliance risks rise in the end.
It’s a big problem. As the volume of data grows and the footprint of multicloud environments increases, dark data can wind up strewn across numerous cloud service provider accounts, regions and jurisdictions. Lacking visibility into the organization’s data footprint, complying with security, privacy and data compliance laws, regulations and requirements becomes far more difficult.
Gaining A Clearer View
The ability to discover dark data and manage it more effectively is at the center of data governance, security and privacy best practices. With the right foundation in place, an enterprise can also improve its access management and data storage frameworks.
With those guidelines in mind, here are six critical steps for breaking down data silos and stamping out dark data.
1. Discover. It’s critical to identify all shadow and native data assets that reside across major cloud service providers such as AWS, Azure, GCP and OCI. Once detected, an organization must extract them into an asset catalog.
2. Detect risks. Organizations must also identify any security posture and compliance risks associated with dark data assets and remediate exposures.
3. Establish sensitive data intelligence. Once the classification and labeling process has taken place, an enterprise can search and visualize the distribution of sensitive data elements across both structured and unstructured data systems. It’s also possible to synchronize the data with third-party catalogs in order to populate metadata associated with tables or columns.
4. Secure systems and govern access. At this point, an organization can build a relationship map between discovered personal data and its owners. With this visibility, an enterprise can fully understand its security risks and fulfill data privacy rights obligations.
5. Fulfill privacy obligations. With personal data mapping in place, an enterprise can execute timely and accurate fulfillment of data subject requests (DSRs), breach notifications and consent reports.
6. Comply with regulations. At this point, an organization can establish automated processes and workflows to comply with global data privacy laws and industry-specific mandates in healthcare, finance, human resources and more.
Implementing these best practices makes it possible to address dark data challenges in a holistic and seamless way. Make no mistake, shedding light on dark data can help your organization adopt a more enlightened approach to security, privacy and governance.
Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT and many more.
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