While digital transformation is necessary for technological advancement, it also introduces significant data security concerns that must be addressed.
Data security has become a top concern as companies increasingly move their operations to the cloud as part of their digital transformation strategy. Though the cloud offers numerous benefits, including scalability, cost-effectiveness, and agility, it also introduces new security risks that must be addressed. By understanding the unique security challenges posed by the cloud and implementing best practices for cloud security, businesses can fully realize the benefits of digital transformation while safeguarding their most valuable asset – their data.
Digital transformation combines the attack surfaces of networked solutions, making one digital product’s security vulnerability another’s data breach attack vector. Multiply this phenomenon across an entire organization and then again across all of its digital third-party solutions, and you get a vast attack surface landscape with many potential pathways to the sensitive data within it.
Digital transformation doesn’t just expand an organization’s attack surface; it adds to the enormous mass of data that an organization must secure and manage. Without a security strategy for securing and protecting data in the cloud, cloud adoption for digital transformation will always increase the risk of data loss. Thankfully, this risk of data loss can be reduced by implementing a proper framework for data security.
This approach to securing cloud data isn’t new. It’s a fundamental information security framework for securing data through each stage of its lifecycle, from creation to storage, usage, sharing, archiving, and finally, destruction. While this framework should be adapted to each organization’s unique data processing context, at a high level, it consists of five primary stages.
For our clients, this framework protects sensitive data stored in the cloud from unauthorized access and breaches.
This framework is one of many strategies utilized in our layered approach to protecting the cloud data of our clients.
The first step is to categorize all the data being processed by a solution based on increasing sensitivity. Risk assessments are an effective method of learning the context of each data environment to inform this classification process. The information collected from these assessments should reveal varying degrees of data sensitivity, so not all processes will require the same degree of security controls. Adopting a risk-based approach to data security is a far more secure and cost-effective approach to implementing a blanket security policy for all data stored in the cloud. This process lays the groundwork for an efficient security control strategy based on your unique data security requirements.
This is where data is stored according to its sensitivity level. There are various storage options available in the cloud, including volume storage (file and block), object storage, database storage, storage for caching, etc. Your choice of data storage architecture should be ideal for protecting data at rest for your unique use case. For example, the use of object storage for attaching metadata to content supports the automation of data classification and data governance in the cloud, which, therefore, enhances the overall security of the data at rest.
Highly sensitive data resources should be stored in sub-network regions of the architecture, inaccessible to general access users. This obfuscation strategy could protect sensitive data from compromise, even if hackers breach your cloud network.
Besides storage architecture, choosing the right computing architecture will further protect data in use (during processing or runtime). For example, we can leverage “confidential computing” in the cloud to enhance the security and privacy of highly sensitive data while it gets processed in the cloud.
Finally, various security controls are implemented to protect all data from unauthorized access. These controls should be commensurate with the degree of sensitivity of each data classification and its required storage architecture. An effective data protection strategy is designed for the unique security vulnerabilities of each data in-transit, at-rest, and in-use process.
Some common data security controls used in the cloud include:
Besides data security controls for the creation, storage, and usage stages of the data lifecycle, this framework should also address policies for data retention, deletion, and archival. To make a data protection program built upon this framework more comprehensive, it should also define policies for retention periods and formats and archiving, retrieval, and deletion procedures.
As should be the case for all assets in an organization, information about meaningful data events occurring inside the cloud boundary should be captured and monitored. This will place an organization in an optimal position to respond to potential data breach scenarios quickly and effectively.
Prioritizing data security in the cloud is essential to fully realizing the benefits of digital transformation while safeguarding against its evolving security threats. By implementing this framework, your business can not only confidently store its cloud data processes but also safely innovate alongside advancing cloud technology.