With the rise of hybrid-cloud and multicloud systems, a comprehensive strategy is needed to maintain control over who can and cannot access sensitive data across the organization, and to protect personal information.
For many companies, the pandemic accelerated digital transformation and data-driven initiatives far faster than what would have otherwise occurred. Today, if we’re looking at enterprise trends — from cloud to software-as-a-service and artificial intelligence — it’s the line-of-business (LoB) side of the house that is first to rush into new and emerging technologies. Operating at a lightning speed, however, can cause some leaders to overlook one crucial component that can significantly strengthen their business: data privacy.
With the rise of hybrid-cloud and multicloud systems, a comprehensive strategy is needed to maintain control over who can and cannot access sensitive data across the organization. The privacy of individuals’ data is paramount in that effort.
The First Step to Accountability: Strategies to Secure Data for the Enterprise
We’re seeing many companies recognize the importance of securing data so that it can be used without infringing upon individuals’ privacy. An increasing number of LoB leaders, such as marketing executives, are taking greater care over how they are monetizing data. Here are three strategies LoB leaders can implement to ensure data security:
- With multiple computing environments in use across the enterprise, it’s no longer good enough to rely only on security protections where data resides. In addition to at-rest data protection, leaders need to implement a cohesive data security strategy that accounts for data wherever it resides — while at rest, in motion, and in use. This lessens the chance of exposing information at any point throughout the data life cycle.
- Equally important, LoB leaders must reconsider whether they have the right tools and controls for each job. For example, encryption may make the most sense for large troves of data at rest, but this type of protection renders the data useless for AI and machine learning initiatives. Instead, leaders may want to consider fine-grained protection techniques such as tokenization or anonymization, which can preserve the format of the original dataset while still protecting any sensitive information. In many cases, they might also want to apply more than one technique across their various environments.
- Last, but certainly not least, leaders must have multiple layers of defenses for securing data. Most companies already have a clear defense strategy for endpoint, application, or network security. Unfortunately, the data itself is often overlooked. For enterprises that want to innovate without disruption, sensitive data must be fully protected, so that in the event of a breach, any stolen data remains confidential and secure.
Training: A Critical Foundation for Data Security Success…
