A well-planned data management process has become crucial for all tech companies. With time, enterprises are having to handle more sensitive data that can contain the personal information of their customers and even their staff.
Sensitive data requires companies to install powerful tools and practices that help maintain the confidentiality, privacy, and security of data. One such leading security approach is known as Data Classification.
What Is Data Classification? A quick read through this article should help you understand what it is and the necessity of having it in your institution.
What is Data Classification?
Data Classification is the method of organizing data and then separating it into different classes if they have matching characteristics. Data is classified based on the criteria of sensitivity of the data, effects compliance regulation has on them, and the severity of the risks they present to the company if exploited.
Different data have different sensitivity levels. When companies classify them differently, it helps to understand the amount of access privileges they require and the security measures that need to be put in place to protect them.
However, companies tend to underestimate its effectiveness when it comes to ensuring an efficient handling of data.
Types of Data Classification
There are three primary types of data classification methods. These include:
Context-based Classification: This method classifies documents based on metadata from a few factors: The location or department from which the document has been created, the creator of the document, and the application used to create the document.
Content-based Classification: This method checks for the content in a document and classifies it accordingly.
User-based Classification: This method classifies documents based on the judgment of a user. Users rank the document according to a sensitivity scale.
What is the purpose of Data Classification?
Having a Data Classification process helps maintain a systematic handling of data. When data is organized, it is easier to analyze the security methods required to safeguard it efficiently. This practice serves essential purposes other than bolstering data security.
By identifying sensitive information and applying appropriate security controls, you can address critical business goals, notably confidentiality, data integrity, and availability. It safeguards sensitive data, like PII and financial information, by implementing security policies and encryption. Prioritizing data integrity necessitates robust storage, user permissions, and controlled access.
This approach ensures that information security and integrity are maintained, enabling efficient data sharing with authorized users.
Benefits of Data Classification
Looking at the recent surge in customer data breaches, it has become hard for companies to secure customers’ trust in their services. It has become imperative for companies to get security features such as Data Classification to help build up trust in their clients and partners.
Data classification ensures the security, confidentiality, and privacy of data. Data security, in turn, plays a prominent role in attaining compliance against top industry security standards. Compliance is your certification to your customers and partners, assuring them that you are safe to get involved with and they can trust their sensitive data with you.
It is not only a great way to boost business but also to save on data storage costs since unorganized data can spike storage costs.
Methods of Data Classification
Data Classification can be obtained through three different methods,
- Manual Classification: Manual Classification provides the power of classifying data and enforcing it at the hands of humans.
- Automated Classification: Classifications by humans possess risks of misjudgment, therefore, technological solutions are used to classify data. This also helps in automating the whole process of data classification.
- Hybrid Classification: This method is a combination of both human intervention and technological processes. Making it error-free and efficient at the same time.
Tools Used for Data Classification
Data classification relies on various tools and technologies, including databases, business intelligence (BI) software, and conventional data management systems. A notable BI software example for data classification is the one provided by CloudDefense.AI.
CloudDefense.AI’s BI methods empower companies to efficiently categorize and manage their data, ensuring that it aligns with their security and compliance goals. You can book a complimentary demo to learn more about our services.
The Relation Between Data Classification and Compliance
Data classification and compliance are intricately linked in data management and security. Data classification is necessary for organizations seeking to meet and uphold compliance with various data protection and privacy regulations, including GDPR, HIPAA, CCPA, and others.
It plays an important role in this pursuit by enabling organizations to identify and label sensitive or personally identifiable information (PII). Safeguarding such data is a prerequisite for complying with industry security regulations. Additionally, data classification helps define data handling requirements, ranging from access control and encryption to data retention and deletion.
Organizations can implement granular access controls based on data sensitivity, a requirement often stipulated by compliance standards. Furthermore, data classification facilitates the implementation of security measures specific to sensitive data, and it provides a framework for tracking, reporting, and auditing data handling practices.
Data classification adheres to compliance standards, reducing the risk of non-compliance and the associated legal and financial ramifications.
What Is GDPR?
General Data Protection Regulation, or GDPR, is a regulatory framework that helps protect the PII of residents in the European Union. GDPR treats any data as personal information if it can directly or even indirectly help in identifying a person. The GDPR policies took effect on May 25, 2018, and have been used to create a standard for data protection laws in the EU and worldwide to regulate companies that process the data on EU residents.
GDPR allows individuals to access, edit, and delete their personal data. On the company’s end, they must get permission from the user before processing their data, appoint Data Protection Officers, report data breaches, and demonstrate accountability and governance in data handling.
FAQ
Check out some of the queries that people have regarding data classification:
What are the 3 main types of data classification?
The 3 major classes in data classification are confidential data, internal data, and private data. Adjusting company policies to have fewer classes in data classification makes it easy to manage data.
What are the challenges in data classification?
There are some challenges involved in data classification. This may include mislabeling a document with the wrong class, misconfigurations in privilege management, and no attention to incidence response.
What are the best practices for data classification?
Some best practices that you can follow for data classification are:
- Identifying the compliance regulations that align with your Industry.
- Starting with realistic scopes.
- Validating classifications.
Conclusion
As our reliance on data grows, the importance of data classification becomes even more essential. It is the foundation for securing sensitive information, respecting privacy, and enabling data-driven decision-making. Therefore, a robust data classification framework should be an integral part of any data management strategy, allowing organizations to harness the power of their data while maintaining the highest levels of security and compliance.
Anshu Bansal, a Silicon Valley entrepreneur and venture capitalist, currently co-founds CloudDefense.AI, a cybersecurity solution with a mission to secure your business by rapidly identifying and removing critical risks in Applications and Infrastructure as Code. With a background in Amazon, Microsoft, and VMWare, they contributed to various software and security roles.
