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Published on December 20, 2021 AUTHOR - Privacy Research Team
The great cloud movement is not without its shortcomings. At one end of the spectrum, cloud migration has helped organizations overcome the age-old logistics issues and reduce operational overhead costs while adding scalability and continuous supply of horsepower for data computing and analysis at scale. On the other end, it has also given rise to many security and privacy threats that make it difficult for data management and governance teams to mitigate risks and meet compliance requirements.
The blog will touch upon the data management challenges that spawn during cloud migration, and how organizations can overcome these challenges and govern data efficiently by leveraging EDM Council’s new Cloud Data Management Capabilities (CDMC) framework.
Cloud is here to stay and grow for an unforeseeable future. In fact, statistics report that the cloud computing market size is expected to grow to $947.3 billion by 2026 at 16.3% CAGR.
Regardless of its immense market size and growing popularity, there are still many challenges that keep organizations from leveraging cloud computing’s innate potential. Take, for instance, the inherent security risks associated with the cloud.
According to a 2019’s cloud data security report, 56% out of 749 organizations cited security as one of the primary concerns behind slow cloud adoption. The concerns of such organizations are rightly placed as organizations move their massive volumes of sensitive data to the cloud, they open themselves up to security threats, such as malware attacks.
Similarly, when organizations migrate to the cloud, they must transfer some or part of their control over the data to the cloud service provider (CSP). This transfer of control creates further security risks, such as data leakage, and if left unattended, it may result in security breaches and attacks.
Lack of security measures, such as access control, is also a common challenge, especially for large-scale organizations that are planning to move to a multi-cloud environment. However, as part of the continuous compliance monitoring, organizations are often required to oversee where their sensitive data resides, who has access to it, and what they can do with their level of access. Non-compliance may put organizations in hot waters, attracting severe fines or penalties by data protection and privacy regulations like GDPR, HIPAA, PCI DSS, and CPRA, to name a few.
Seamless data sharing between legacy on-prem applications and multi-cloud applications is only possible with seamless integration. Interoperability can pose a great challenge for organizations moving to the cloud because of the inherent limitations in legacy applications, such as compatibility. Consequently, it creates an internal hesitation between teams because of the changing architecture and the reconfiguration of the applications to make that integration happen.
Apart from the challenges listed here, inefficient or slow cloud migration may also be the result of a lack of sound migration strategy, or it may also be associated with getting a new IT team on board that has the right skill set for cloud data management.
EDM Council is the leading association in the data privacy and security sphere that advocates the standardization and implementation of data management and best practices for tackling associated challenges.
With the contribution of hundreds of organizations, including Securiti, IBM, Google Cloud, and AWS, EDM Council’s new Cloud Data Management Capabilities (CDMC) framework addresses the cloud migration challenges and defines best practices.
CDMC framework establishes the 6 main pillars of best practices around managing data within the cloud, which are further divided into 14 level-2 controls that can help organizations efficiently operationalize data governance.
Let’s take a look at the following key controls for seamless cloud data migration and management.
Data assets should be at the core of an organization’s security posture because it is one of the top targets for cloud data breaches. The security and governance teams can’t protect an asset if they don’t know where it is, or it has been ignored which is something quite common when it comes to the unmanaged or shadow data assets spread across on-premise and the multi-cloud environment.
The path that leads to a robust security posture is having a controlled inventory of all the managed and unmanaged data assets, cataloged according to their residency, ownership, and lineage. A well-cataloged inventory of assets further allows security teams to define optimal security controls based on the sensitive data residing in those assets.
Lastly, organizations should shift to automation from the manual processes of listing inventories, fixing vulnerabilities, and monitoring security control sporadically. As threat actors are leveraging automated attacks for cyber breaches, it is high time for organizations to stay ahead of their foes by doing the same and embracing automated data asset discovery, cataloging, and security posture management.
The trio of data discovery, classification, and cataloging make the core parts of an organization’s data privacy and security strategies. Once an organization sifts through its on-prem and multi-cloud to find and catalog data assets, the next step is to look for the personally identifiable information (PII), including the sensitive personal information, stored on those data assets. An effective data discovery also takes into account the unstructured data that may live across spreadsheets, emails, etc.
Data discovery then leads to the data classification phase where all the sensitive data is then labeled according to its security and privacy labels. The security labels allow teams to make sure safe and authorized access to the data. The privacy labels enable the privacy teams to find the correct data and respond to data subjects' access requests, the right to be forgotten, the right to inform, the right to delete, and similar other data subject’s rights under regulations like GDPR and CPRA.
The data cataloging phase is where an organization creates an organized inventory of the data about the discovered and classified data (metadata), including tags, labels, or tables.
Data sovereignty and cross-border movement are governed by many global data protection regulations, mandating organizations to keep track of their cross-border data, place robust security measures, and ensure that the data transfer process meets judicial compliance.
To ensure all that, it is imperative to first understand where the sensitive, cross-border data resides, and what kind of sensitive data it is, such as medical, financial, etc. The further concerns include the type of jurisdictional regulations that apply to the data, such as CPRA or LGPD.
With a clear picture of the sensitive data in various cloud data systems, organizations must also govern access to this data. The data discovery, classification, and cataloging phases simplify most of the heavy lifting at this point of the phase. To further proceed with the access governance phase, organizations must first identify the business role associated with the data as it allows the governance team to better regulate and monitor users’ access to the data. Organizations must establish role-based access control, starting with least privilege access, to make sure that the data security and integrity remain intact.
Automate enforcement, such as encryption or data masking, wherever possible to further strengthen the security of data while allowing teams to share and use it in a secure manner.
Data processing is constantly changing. Traditional tools that are just a snapshot won’t truly operationalize a program in a scalable way long-term. Securiti delivers an AI-powered autonomous data governance framework that operationalizes at a granular level, allowing organizations to integrate with their existing on-prem or multi-cloud environments with native integration, discover shadow and managed data assets and the structured and unstructured data across those assets:
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