Written by:
Matthew Delman Data loss prevention (DLP) is a vital component of a data security strategy. Protecting critical information from accidental or intentional removal means setting up a solution whereby the organizations’ vital data is secured against exposure. According to research, 2.5 quintillion bytes of new data are created every day, with 180 zettabytes of global data expected to be created by 2025.
The sheer volume of data created every day and every year makes preventing its accidental or intentional loss a major problem. On top of that, not all corporate data is created equal. Blog post drafts from a marketing website don’t have the same enterprise impact as financial records, and as such need entirely different protection strategies.
DLP solutions are powerful tools for safeguarding sensitive information, but their effectiveness is limited when you try to apply them too broadly. Protecting all corporate data at the highest level is also a way to ensure that your users try to skirt policy to do their work, all but ensuring that accidental data loss is going to occur.
The answer for how to solve this problem lies in data classification. Data classification is the process of meticulously identifying, labeling, and categorizing an organization’s data based on its confidentiality, integrity, and availability requirements. This empowers your security team to apply the most restrictive DLP permissions to the most critical data. Ultimately, using data classification is the only way to get the most out of your DLP solution.
Why Data Classification is Essential
Data classification in practice ensures that the most vital data is readily identified for protection. Security teams have a lot of network architecture and information to protect. Applying the same restrictive standards to each system and each data storage location is impossible purely from a time perspective. A more realistic method is to apply the strongest protections to the most critical data.
Data classification ensures that they can apply that approach. By classifying data, you can define specific DLP policies for different categories. For instance, highly confidential data like financial records might be encrypted, while transfers of moderate-risk data like marketing materials might be blocked or monitored.
DLP solutions can be resource-intensive, constantly scanning and analyzing data flows to ensure that no data is accidentally lost or intentionally extracted. Data classification helps you optimize resource allocation to the most sensitive data. When you’ve classified the most critical information, you avoid spending too many resources on protecting low-risk information.
Furthermore, many data privacy regulations like GDPR and HIPAA require organizations to identify and protect specific data types. Data classification helps you meet compliance mandates. Classifying all the customer data in your systems along with all the personally identifiable information means that you can better secure those records and also know where they are to prove that you have complied with regulatory standards.
In the unfortunate event of a data breach, data classification allows for a swift and effective response. Data classification allows you to quickly identify the type of information compromised in a data breach and take appropriate remediation steps to minimize damage.
Tips for Robust Data Classification
Data classification is as much about technology as it is about process. Finding the right tool to help with programmatically assigning data levels is vital for your ongoing data loss prevention efforts. Prior to looking for a solution, however, you need to figure out the bare bones of your data classification program.
- Define Data Sensitivity Levels
The first step is creating a classification scheme with different levels (e.g., Public, Confidential, Highly Confidential) based on the sensitivity of your data. This scheme serves as the foundation for categorizing your information. Public information might be the materials stored on the marketing website. Confidential information could be sales proposals or other data that is shareable but can only be sent out under a nondisclosure agreement. High Confidential information could be financial records or employee payroll information. Some information may also change levels as time goes on, such as earnings releases for publicly traded companies.
- Develop Classification Rules
Establish clear guidelines for how data should be classified. Consider factors like content type (e.g., financial data, customer records), format (e.g., documents, emails), location (stored on servers, employee laptops), and access permissions. These guidelines ensure consistent classification across the organization.
This is especially key because teams in different geographies under distinct regulatory regimes may have their own local laws to comply with. Data classified as confidential in one region may potentially be highly confidential in another based on local laws.
- Automate Classification to Simplify Data Identification
Utilize automated data discovery and classification tools whenever possible. Automation helps ensure consistent labeling across large datasets, saving valuable time and resources. The sheer volume of data under management in most organizations makes automation imperative for any sort of accurate or consistent data identification and classification.
- Conduct User Education and Training
Educate your employees about data classification and how to properly handle and classify information based on its designated level. This empowers your workforce to become active participants in data security. Users are going to create new data every day. If they’re properly educated on how to classify information based on confidentiality, they are more likely to accurately apply data labels to their work.
- Perform Regular Reviews and Updates
The data landscape and regulations are constantly evolving. Regularly review your data classification scheme and update it as needed to maintain its effectiveness. New regulations are passed every year, so keeping your data classification strategy fresh can be key in ensuring that you remain in compliance.
By implementing a well-defined data classification system, you lay the groundwork for a successful DLP strategy. A robust data classification system empowers your DLP solution to effectively safeguard your organization’s sensitive data, minimizing risks and ensuring compliance.
How Sotero Makes Data Classification More Efficient
The Sotero platform features built-in artificial intelligence and machine learning models that automatically discover and classify critical data. The Sotero AI scans data in cloud and on-premises environments, parsing both structured and unstructured data for potential sensitive attributes. This data is then automatically classified based on potential severity level, empowering security teams to deploy the tightest security on the most critical information.
The Sotero solution empowers you to customize sensitivity parameters based on your data governance policies and compliance needs. These attributes can be customized to align with your specific requirements, ensuring the greatest possible flexibility in-platform. This ensures that you can classify data at the proper level and protect it.
Sotero customers can be confident in our comprehensive data security for both structured and unstructured data, whether on premises, in the cloud, or in hybrid environments. With Sotero, data loss prevention is more effective and critical data becomes more secure.