DATA CLASSIFICATION IN ADDITION TO DATA LOSS PREVENTION

Governments and organizations need to adopt new guidelines for user safety in cyberspace as a result of the growing digitization of society. The need for software for the classification and protection of sensitive data is growing along with the limitations associated with its processing and storage. When working with financial data (accounting, finance), personal data (sales, HR), or data that gives them a competitive edge (projects, price lists), almost every employee creates sensitive data. There are several methods to handle this data: it may be copied, cut into restructuring paraphrased, and so on...

Data protection becomes much more difficult as a result. Data that has been organized into a table or document with an agreed-upon layout may be protected rather easily. Nevertheless, if the data is unstructured and consists of memos, emails, or non-standard contracts, it is far worse.

What steps can we take to secure the data?

The answer lies in three main areas: Encrypt them – data encryption. Classify them into a given category – data labeling. Enforce protection rules on them – data loss prevention.

Data classification in the DLP (Data Loss Prevention)

The system uses parameter-based scanning (personal information, credit card information, self-defined criteria) to find sensitive data, and it subsequently applies policies depending on the search findings. What constitutes sensitive data and how it is handled are centrally decided by the IT department with business cooperation. The DLP system is the sole option available for proactive implementation of policies because the IT security department is ultimately responsible for digital data. "Get the manager's confirmation if the file contains personal data and is to be sent outside the corporate network," for instance.

Classification in Data Labeling systems

Consider user is aware of the type of data he is dealing with and uses the appropriate label (such as "Public" or "Private") to categorize it. In this instance, the worker becomes a point of contact for the IT department by actively engaging in the data classification procedure, which reduces the overall number of false-positive outcomes while simultaneously enhancing information management knowledge. As an example, "I classify files related to important business initiatives as secret." Note that while data labeling solutions let you "tag" data, they do not support remedial actions like blocking or alerting.

Why is knowing who owns the data so crucial?

Because of the aforementioned variations, using a single method to identify the information we deem crucial to the business puts us at risk of missing something or classifying files incorrectly. This will increase the number of false positives or put us in immediate danger of legal action. Classification errors are mostly caused by complex internal connections (mostly involving unstructured data) and a high degree of subjectivity; for example, an employee's definition of "public" may differ from the company's definition of "secret." Here, data owner awareness is essential.

How do I classify data with the use of a DLP system?

To classify data, DLP systems have built-in classifiers:

• dictionaries / keywords – searches based on comparison of strings;
• regular expressions—regex—make it possible to match the classifier to the type of data, but remember that a “narrow” regex (too narrowly defined) will catch everything;
• scripts – these are regexes plus a validating algorithm, e.g. not every 11 digits is a PESEL number;
• machine learning – the system “learns” from the test data and then detects documents with a certain probability for the test sequence, here the key lies in choosing the right test data,
• fingerprinting – a magnum of DLP systems – we create “hashes” of sensitive data, e.g. fragments of a commercial contract, and use them to compare with data detected elsewhere; resistant to file structure changes, detect unstructured data.

But what if anything is overlooked by the system?

Well, what if the system incorrectly or never qualifies something in our mountain of paperwork (and it is hard to find someone who would have complete control over the papers generated and handled in the organization)? This is where data labeling comes in handy, enabling the user to categorize the document that he is working on. The document's creator will be the most knowledgeable source when it comes to classification.

What are the advantages of combining data labeling with DLP, then?

• Complete and Accurate Classification : A file is classed twice when a data labeling policy is added to a DLP system: once by the IT department and once by the user. Files that one of the parties ignored can be categorized with this help (the DLP system categorizes the data without a “label,” and the user can add a “label” to a file that DLP skips).
• Double validation – What would happen if the employee marked the file as "Public" and attempted to transfer it via email to someone outside the company, and the DLP system discovered that the file included customer data? Security professionals may manage such scenarios and fix errors by utilizing both platforms.
• Reduction of false-positives – the outcome of double validation, but it's important enough that it deserves its own discussion topic. Double validation allows us to greatly enhance our experts' job when the system is in use across the entire organization. Would you be able to imagine managing five thousand notifications a day effectively?

The next development in data protection is the combination of DLP and Data Labeling systems, which enable users to collaborate with security teams and foster a culture of data usage. We support the growth of knowledge and best practices for handling data within the organization in addition to adding another layer of security.