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Published on September 23, 2021 AUTHOR - Privacy Research Team
Data being the “new oil” is a statement that not many would agree with. Data is more valuable than oil, as it is a non-fungible asset that is unique, irreplicable, and ever-expanding.
Data insights help enterprises make better business decisions, enhance operational agility, improve core competencies, and understand consumers to deliver improved tailored services. According to Deloitte’s survey, data analytics helps businesses make key strategic decisions and improve a business-consumer relationship.
While a high data volume helps enterprises gain better insights, it also makes it crucial to have efficient mechanisms in place to comply with the regional security and privacy laws, such as GDPR and CCPA, to name a few. Failure to do so can lead enterprises to class-action lawsuits, severe penalties, and above all, the loss of brand trust.
Small and mid-sized enterprises that deal with a moderate volume of data may not face many difficulties with privacy regulatory compliance. Enterprises may find it difficult to comply because of data sprawl, which is often due to the data being scattered across multi-cloud and SaaS applications.
Multi-cloud refers to enterprises leveraging multiple cloud computing environments to meet their infrastructural and operational needs. According to IDC’s forecast, over 90% of enterprises will deploy their services to multiple environments, such as multi-cloud, private cloud, SaaS-based applications, and legacy servers.
Enterprises are migrating to the multi-cloud to add more efficiency to their processes, streamline business operations, reduce on-premise costs, and improve performance across the board. Here are some other factors behind the tremendous shift to multi-cloud adoptions:
Every Cloud Service Provider (CSP) delivers varying functionality, features, and capabilities. By shifting to multi-cloud, businesses can leverage the best-of-breed technologies to cater to different objectives and specialized operations.
Privacy regulations such as HIPAA and GDPR impose strict data privacy policies when it comes to data transfer and storage across borders. Multi-cloud can help businesses to comply with such privacy regulations and honor customers’ preferences should they choose to keep their data on either local cloud or multi-cloud.
Migrating all the sensitive data and processes to a single cloud platform can propose serious risks. In case of a data breach, the enterprise may end up exposing all its sensitive data. If the cloud platform seizes to operate due to any circumstances, all business-critical operations may come to a halt. Multi-cloud resolves these concerns by offering redundancy, availability, and risk management.
As mentioned above, multi-cloud environments have a set of challenges that enterprises must resolve to comply with data security and privacy regulations.
In a multi-cloud environment, it is difficult to discover and track data because the data assets are scattered across different platforms. IT teams have to keep track of the data through the traditional configuration management database (CMDB), keeping records in spreadsheets, docs, and other traditional formats.
A manual data assets inventory management system leads to human errors and consumes excessive time, resources, and expense.
A viable solution to such business challenges is to create a centralized data asset management system that can keep track of all data assets across platforms, automate cloud and self-managed data asset discovery, and auto-update CMDB accordingly. The centralized tool should deliver the enterprise a comprehensive view of all its data assets under one roof.
Many enterprise data discovery applications have limited capabilities to detect and discover sensitive data. Some tools offer data discovery of structured data, while others tools are exclusive to unstructured data discovery. Moreover, every tool has different integrated data detection algorithms and supports limited PI identifiers.
A fragmented approach to data discovery offers poor efficacy, and a lot of time and efforts go into creating separate policies for every system.
Enterprises need to have a unified detection system that offers universal integration, such as with self-managed cloud servers, SaaS applications, and various cloud service providers. The detection system should offer an all-inclusive coverage of attributes relating to sensitive data that is required under different privacy regulations globally.
Moreover, the unified system should leverage AI/ML algorithms and contextual analysis methodologies to accurately discover and track sensitive data.
Hyperscale cloud environments can now handle petabytes of data which the traditional data discovery tools cannot deal with due to limited support. Moreover, added resources go into scanning such a high volume of data which could take months to complete. Also, enterprises cannot rely on traditional tools because of data residency regulations and high transfer costs.
A modern data scanning engine should be devised that can handle petabyte-scale data and offer provision and scaling accordingly. The architecture should support Big Data formats and configurable optimization techniques for large-scale scanning.
Manual compliance with privacy regulations using traditional data mapping practices isn’t feasible for handling a high volume of data. Manual scanning of users’ personal and sensitive data can provide only point-in-time insights and not the updated insights into new data attributes captured in real-time. This can seriously affect an enterprise to comply with Article 30 of GDPR, DSR, and other data breach notification regulations.
Enterprises require an automated PrivacyOps solution that creates and maintains a People Data Graph that can effectively correlate and map people’s data in real-time. The automated solution should also deliver a dynamic workflow that can auto-sync consent management across the database, allowing enterprises to entertain DSR requests.
Traditional cloud security posture management systems treat all data the same which leads to many false positives. Furthermore, a misconfigured system can create complications that can lead to failure of compliance with security frameworks, such as CIS and NIST.
A dynamic solution is essential for enterprises that can combine data risk postures and security misconfigurations, and automate the rectification of such misconfigurations to enable security teams to reduce and resolve security risks effectively.
The AI-powered suite of solutions helps enterprises working in hyper-scale environments automate data discovery, classification, and cataloging, along with auto-discovering security misconfigurations. By adding AI-driven automation to their security suites, enterprises can ensure the security of sensitive data and compliance with security frameworks and regulations globally.
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