The CDOs and CISOs relationship is critical in today’s data-driven business environment for maximizing value of the data. Organizations can keep both sides of the aisle happy by deploying data analytics strategies that integrate best-in-class security solutions.
The rapid expansion of cloud data warehouses demonstrates that cloud data consolidation is widespread. Of course, over the last year, the move to facilitate remote working environments has accelerated this trend.
Cloud migration brings valuable cloud data, which is a moderate priority for 61 percent of firms and a critical or high priority for 25%, according to Forrester’s 2020 Business Technographics Data And Analytics Survey. The demand for cloud data insights not only increases the responsibilities of the chief data officer (CDO), but it also necessitates collaboration between the CDO and the chief information security officer (CISO) in order to maintain data security across the analytics pipeline. Each bears a significant amount of responsibility, and the success of an organization is determined by how well the two are balanced.
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On the one hand, CDOs are ecstatic about the massive flood of new data and the insights it can provide, while CISOs, are burdened with the task of ensuring these newly mined assets do not become sources of risk. Companies can get valuable insight by analyzing and sharing the vast amounts of cloud data they generate, but doing so without the right safeguards increases the danger of data breaches and the resulting regulatory fines.
So, how can businesses get the best ROI from data while maintaining best-in-class security standards?
Integrating data-centric security controls into the analytics pipeline to safeguard data during creation, transit, storage, and processing is the key to keeping both CISOs and CDOs happy. This helps businesses to make the most of their data while also maintaining its security both internally and when shared with others. Here are five strategies to make the most of the data while still safeguarding it, no matter what enterprises do with it.
Determine Data Value
Every bit of data that enters a cloud environment should be recorded and assigned a value as soon as it is created. This helps the organization prioritize its importance and guides data management practices. Data such as intellectual property, customer buying insights, and sensitive information should be prioritized over office wide policy documents or annual vacation itineraries, for instance.
Assign Risk Scores
Credit card numbers, customer Social Security numbers, and other personal identifiable information (PII) are examples of data that does not provide crucial insight but is extremely sensitive. Every piece of data should be assigned a risk score that determines how to secure it. It’s crucial to remember that identifying risk levels isn’t always a decision made at the discretion of the organization; privacy regulations like CPRA, GDPR, and HIPAA specify which datasets should be regarded the most sensitive.
Appropriate Protection Measures Should Be Implemented
Data security isn’t a one-size-fits-all concept; numerous factors influence how data is protected. Data value and risk ratings are two important drivers, but it’s also important to evaluate how and where data is used. Unstructured data entering the data analytics pipeline, such as raw transaction logs, photos, and text documents, requires less complicated protection than refined and structured data leaving the pipeline. When engaging in data-sharing activities, the security measure is even more critical because data values can be assessed without revealing PII associated with the data.
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Determine the Policies for Access Control
Many businesses take a zero-trust security posture. Access control policies that determine who can and cannot access certain data in specific formats are a significant component of such an approach, as is a fail-safe method in which the default stance is to restrict access. Strict access control may significantly limit the risk of data exposure, especially as data becomes more valuable as it moves through the analytics pipeline and is shared.
Data Should Be Monitored Throughout Its Life Cycle
Data, in any form, attracts risk. Organizations that keep a close eye on their data can spot anomalies early on and take proactive steps to prevent data exposure or, at the very least, minimize the damage.
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