These days, organizations’ data security and privacy infrastructures are undeniably complex. With multiple platforms, tools, and internal policies in place, as well as regulatory requirements to adhere to, having a strong foundation that supports accuracy and efficiency is a must. And that’s where automated data classification delivers.
Automated data classification for enhanced security
Sensitive data classification involves labeling a piece of information after it’s been discovered or created based on criteria, such as data’s level of sensitivity, corresponding privacy regulations, and any internal policies it must adhere to. Classification will ultimately dictate how a piece of data is secured, used, and eventually disposed of, how it must be treated by other platforms and processes in your security stack, and who can access it.
Benefits of automated data classification
While classification is an essential part of your data security strategy, it has the potential to work against you if not executed properly. This is often the case with manual classification. With other automated platforms in your supply chain quickly churning out high volumes of data, it’s impossible to expect manual classification to keep up, especially without error. Even if flawlessly executed manual classification could be guaranteed, it’s extremely time-consuming and will no doubt inhibit your organization’s operational productivity.
Automated classification tackles these efficiency issues while also enhancing the efficacy of your greater security strategy through standardized tags/labels. From your data loss prevention (DLP) policy and authorizing user access to your Zero Trust approach and threat response procedure, having sensitive data labeled with common nomenclature ensures that all your sensitive data is properly defined in policies and procedures and can be processed accordingly by the tools used to execute them.
This of course eliminates the security risks associated with inconsistent labeling at the user level and fortifies efforts to uphold compliance while data is both at rest and in motion.
Objections to automated data classification
Objections to automated data classification are few and far between, but they exist. One of the most common to pop up is automated classification’s inability to classify data with the detail it requires. Applying broad rules and labels to large swaths of sensitive data can certainly lead to the same vulnerabilities and business disruptions as a manual misclassification, but the key is choosing a sophisticated solution that considers data’s context, so accurate tags for purpose, process, privacy, and sensitivity can be applied to all file types.
Automation is the only way forward
As data continues to fuel organizations all over the world, it’s also being targeted more frequently by sophisticated cyberattacks. To combat this, organizations are actively implementing a Zero Trust approach to data security, for which context-rich data labels are the foundation. They’re needed to verify and authorize any attempts—from both users and applications—to connect with sensitive data, ensuring the interaction aligns with company policies. The level of detail provided by automated classification allows Zero Trust to work at maximum efficacy.
Enhance your data security and privacy initiatives with Spirion’s automated data classification tool
Spirion offers highly accurate, automated data classification so sensitive data can be tagged and secured as soon as it enters your organization’s environment. With context-rich labels that are easily understood by other tools in your security stack, you’re able to execute an effective Zero Trust approach while your data is at rest. As it moves, you can track and monitor sensitive data to ensure it sticks to internal security policies while upholding compliance.
Contact us today to learn how our scalable solutions can help strengthen your data-centric security approach, make business operations more efficient, and reduce risk within your environment.