In my last post, I covered the important topic of Data Access — determining who, why, and when of using data sources. In this blog post, I delve into its sibling and prerequisite, Data Classification, which designates the “what” of data, and explore best practices I recommend to our clients.
What is Data Classification
First, let’s begin with a quick definition. Data classification is a basic IT concept that categorizes information based on its sensitivity and criticality. It’s a systematic approach that enables organizations to manage data efficiently and ensure the appropriate levels of protection and access. By classifying data into various tiers, such as public, internal, confidential, and highly sensitive, organizations can implement targeted security measures that safeguard sensitive information from unauthorized access, alteration, or disclosure. This proper classification streamlines data handling procedures, helps allocate resources more effectively, and ensures the organization meets relevant data protection regulations.
Best Practices for Effective Data Classification
I often run into two obstacles to properly classifying data. The first is organizational inertia. Setting up a classification system and categorizing all an organization’s data assets takes time and effort. The task can seem overwhelming. It doesn’t have to be. The key is to just get started. Leadership plays a key role in overcoming this hurdle. Leaders must prioritize data classification, allocate the necessary resources to set up the system, and drive it to completion. Second, organizations often classify their data, set up the appropriate controls, and, then, think the job is done. It is not. Classification never stops. Data is dynamic. You must monitor and refine your classification system as new data types emerge. Unfortunately, there is no set-it-and-forget-it system, but the right processes make refinements much easier.
Here are some best practices I recommend to our clients to get them started and keep going:
- Commit to Data Classification: Commit to creating an effective data classification process as part of a robust cybersecurity strategy. Inconsistent or ad-hoc data classification approaches undermine your broader cybersecurity efforts and produce confusion and ambiguity among employees. Confusion creates friction in your business operations and leads to unnecessary risks.
- Get started: You do not need to achieve perfection out of the gate. As I said, data evolves so your data classification approach and systems will as well. Start small but get started. Classification will become easier and more effective over time.
- Data Discovery and Inventory: Conduct thorough data discovery to identify and inventory all data assets within the organization. This includes structured and unstructured data across various storage locations and systems. This effort cannot be outsourced. This work is 100% on you because only you know your data. If you delegate this responsibility, you are planting the seeds for a potential “fiasco.” For example, you do not want a subfolder structure with random names that make no sense and lead to improper access.
- Clear Classification Policies: Establish well-defined and documented data classification policies that outline the criteria for categorizing data based on sensitivity, regulatory requirements, and business impact. Classification systems are worthless if they are not documented, updated, and shared appropriately. Waident is a stickler about documentation. https://www.waident.com/documentation-your-it-canary-in-a-coal-mine/
- Collaboration and Training: Use training to foster collaboration and ensure consistent classification practices among business units, IT, and security teams. Regular training sessions can enhance employees’ understanding of data classification principles, appreciate accessibility roles, and enable them to make informed decisions when handling data.
- Data Loss Prevention (DLP) Systems: Implement DLP systems that monitor data flows and enforce policies to prevent unauthorized data disclosure. These systems can detect sensitive data in transit, at rest, or in use, ensuring compliance and protection. You might be surprised that a basic level of DLP is included in Microsoft Office 365. For example, activating DLP makes it easy to monitor data such as bank accounts, Social Security Numbers, credit card information, and other items typically found in company data sources. You can create more complex rules to meet your needs with a little more time, skill, and testing. In other words, you may already have the tools, just dive in!
- Rinse and repeat: As I said earlier, you can’t just set up a classification system and forget it. Build regular, periodic evaluations of your classification system, use the appropriate tools to “pressure test” your process, keep educating your employees, and update and document your policies accordingly.
Conclusion
Data classification can seem overwhelming at first, but it doesn’t have to be. It is a process that precedes your data access approach and is a powerful tool to mitigate risks, comply with regulations, and safeguard sensitive information. Implementing effective classification strategies is an investment that pays off by keeping your data safe, your people productive, and your enterprise running in a data-driven world.
Extra Credit:
How to protect Small Business with NIST Framework
Learn about data loss prevention
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