Data classification: the backbone of effective data security
How to effectively secure all that data and ensure its compliance with regulatory standards? The answer is data classification.
Statistics indicate that in 2024 we will produce about 147 zettabytes of data. That’s a lot, and although some of it may not really matter for your organisation, the part that matters is still enormous.
What is data classification?
Data classification is nothing else than the process of classifying data, meaning organising it in categories according to certain criteria, for example data’s level of sensitivity.
Such categorisation helps organisations manage and secure data more effectively.
Why is the data classification process important for your security posture?
Data classification process is of paramount importance for every organisation’s security posture as it allows organisations to identify and prioritise their most sensitive data and critical information, and implement effective access controls.
It also ensures compliance with security regulations, as many regulatory governing bodies require data to be labelled. In case of a data breach, data classification facilitates a more targeted and efficient response, needed to mitigate the impact of such an incident.
What are the types of data classification?
Data classification involves categorising information based on its attributes, sensitivity, and importance. There are many different types of classification that can be used by an organisation, depending on its specific needs and goals.
There are however three main types of data classification that can be used by businesses of all sizes and industries:
- content-based classification,
- context-based classification, and
- user-based classification.
Content-based classification allows to organise data depending on the content of the document, contact-based classification looks at the ways data is being used and at who is accessing it, while user-based classification relies on user-knowledge selection of the document.
Other common types of data classification include confidentially-based classification (which looks at whether data is public, confidential or restricted), regulatory-based classification (looking at whether it is PII – personally identifiable information, PHI – protected health information, or financial data), data type-based classification and lifecycle-based classification.
To respond to their specific needs and environment, organisations often use a combination of those classification types, creating a system that works for them and aligns with their business goals.
What are the levels of data classification?
The process of data classification involves assigning a different data classification level or tier to data, based on its sensitivity, importance, and confidentiality. Those levels include:
- public data, available locally or on the Internet, often shared, updated and passed around;
- internal data, intended to be used within a certain organisation and not by the public or external parties;
- confidential data, meaning sensitive information that requires protection;
- restricted data, meaning highly sensitive information, with access limited to a specific group of people.
What are examples of data classification?
The trickiest part of data classification is the moment of assigning data category. Here are some data classification examples that may help you in the process:
- documents considered public data include press releases, marketing materials, content of a website, addresses, phone numbers;
- documents considered internal include departmental reports, company newsletters, employee directories;
- documents considered confidential include financial reports, business plans, researches, internal policies, social security numbers, medical reports, customer data;
- documents considered restricted data include trade secrets, intellectual property, highly sensitive financial information, reports prepared by the government.
The intersection of classification of data and privacy regulations
Data classification plays a crucial role in meeting data protection requirements. By integrating data classification practices into their overall data management strategy organisations can align with privacy regulations, enhance their data protection practices, and demonstrate a commitment to safeguarding sensitive and personal information.
What’s more, data classification helps in achieving and maintaining compliance with evolving privacy requirements in various jurisdictions.
Read more about data security:
Best practices in data classification for enhanced security
To help you kick start with your data classification processes or improve your existing ones, here is a quick checklist of best practices in data classification for enhanced security:
- Understand data protection regulations relevant to your industry and geographic location;
- Establish a clear and understandable data classification policy;
- Involve stakeholders from different departments to ensure a comprehensive and well-rounded approach;
- Automate your data classification processes to reduce manual efforts and mistakes;
- Educate and train your employees about the importance of data classification and how to stick to it in everyday life;
- Be consistent in your labelling system;
- Regularly review and update your data classification methods to ensure they remain aligned with your evolving business needs.
Transform your data strategy with Future Processing – reach out now!
Data classification is a complex, long-term process that lies at the very heart of an effective data strategy of each company. But you don’t need to face it on your own. It is much better to use expertise and experience of professionals who are doing it on daily basis.
At Future Processing we have all it takes to allow you to make the most of your information assets and apply innovative data solutions to your business. Get in touch with us today to discuss your options and find the best solutions that will take your organisation to the next level.
