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Adapted from the IBM article by Joanne Godfrey

Five best practices for data discovery and classification

Discovering and classifying data across the enterprise is crucial to any data protection strategy, but it can be complicated due to the constantly shifting nature of the cybersecurity landscape, the difficulty of unifying processes across diverse environments and the sheer scale of the task at hand.

Best Practices For Data Discovery
Five Best Practices For Data Discovery And Classification

If you’re feeling overwhelmed trying to keep track of and meet the myriad data security and compliance requirements organisations face today, the following five best practices can help you develop effective data discovery and classification processes, which can help address the data security, data privacy and compliance requirements for your organisation.

1. Industrialise Data Discovery processes

In today’s data-centric world, it’s simply no longer possible to do data discovery and classification manually. It’s inaccurate and inconsistent, and thus, very risky. People make mistakes, and these mistakes can mean that your data is misclassified or not classified at all. As a result, your data may not be protected properly, or you may not be in compliance. Manual classification is also incredibly time-consuming.

Look for a solution that automates data discovery and classification and supports multiple methods for classification, such as catalog-based search, regular expression and patterns, as well as next-generation data classification, which can search data directly from within a table. This enables more expressive results and delivers higher accuracy.  Read more: Industrialise your GDPR programme for faster, more assured compliance

2. Plan your journey

Don’t start your data discovery and classification journey without a goal. Ask yourself, why are you classifying data? For security, compliance, privacy Subject Access Requests? Are you looking for personally identifiable information (PII), payment card data, IT data? Remember, there are many types of sensitive and regulated data.

It’s also important to determine where you want to start. Maybe you have a customer relationship management (CRM) database that you know is likely to contain a lot of sensitive data. That might be a good place to start.

Once you have a plan, make sure your solution supports your specific needs. If your objective is General Data Protection Regulation (GDPR) compliance, then your solution should include built-in patterns for the GDPR. If your needs are more niche, look for a solution that can support custom classification.

3. Look beyond the horizon

While you want to follow an initial plan and focus on the data sources that introduce the highest risk to your business, be prepared for surprises and deviations from the plan.

Remember, sensitive data can be anywhere and everywhere — on-premises, in the cloud, in shadow IT, and in testing and development systems — and it can be in many different formats. Look for flexible solutions that can support you wherever the journey takes you, no matter the type of data or where it lives.

4. Repeatable and scalable actions

Data is dynamic, distributed and in demand. New data and new sources are added all the time, and data is constantly shared, moved and duplicated. Moreover, data changes over time. At one point in time, it may not be sensitive, but then it is changed and becomes sensitive — and sensitive data is risky data. Automation makes the data discovery and classification process repeatable and scalable.

5. Action to be taken

Data discovery and classification should serve as the foundation for your security strategy. Use the insights you have garnered to assess risk and prioritise remediation efforts. Start with hardening sensitive data sources, then implement effective access policies. Continuously monitor to detect suspicious and outlier behavior. Deploy controls to protect sensitive data, such as blocking and masking data, as well as flexible encryption solutions.

Businesses are migrating to the cloud to increase agility and productivity while facing a relentless barrage of cyberattacks and an ever-increasing number of data compliance regulations. Therefore, the need for data discovery and classification is more important than ever. Intelligent automation, strategic planning, focused execution and thorough preparation can provide the foundations for a successful security and compliance strategy for your organisation.

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