Data classification: Without control over data, no reliable AI initiatives
Data classification involves categorising information based on how sensitive and business-critical it is. By labelling data into levels such as public, internal, protected, and strict, you can control access, protection, and usage – crucial for both AI initiatives and cybersecurity.
Companies today face two clear challenges, or driving forces if you will: the external cyber threat with stricter requirements from laws and regulations, and the opportunity to transform their business with AI and innovation. In the rush to avoid falling behind, many companies risk missing the most fundamental thing: having control over their data.
We often meet company leaders who do not know which data is business-critical. This makes it completely impossible to control how information may be used and how it should be protected. When AI becomes a natural part of employees' everyday life, this ignorance becomes dangerous. Without clear information classification, every discussion about how you can utilise AI becomes ineffective and decision-making reactive. With correct classification in place, you get a solid basis for decision-making to know: What must work, what can wait and what you can never compromise on.
What is data classification?
It is therefore high time to consider your information classification, or data classification, and how well your experience of control matches reality. With correct classification, you are a step on the way. Here it is important to ask yourself the questions:
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Which data is business-critical and worth protecting, and which is not?
- What may be used for AI and decision support, and what absolutely may not?
- Which data must work during a security incident, and what can wait?
If answers and structure around these are lacking, the risk is that every decision, AI investment, security initiative, backup strategy and incident plan becomes a guess rather than a well-founded decision. With an anchored classification, management and your employees get a common language for your priorities.
Why data classification is a management issue
When AI technology and decision support are woven into your operations, data classification and resilience are no longer only the responsibility of the IT department. The issue moves into the boardroom. This also reflects the demands in the EU's security framework NIS2.
It is management who must guide the business through crises and incidents. If you do not know which data is important, sensitive or regulated, it is impossible to make informed, precise decisions, something that is absolutely crucial during a security event when every second counts.
If no one owns the issue, the risk is that decisions are based on incomplete or incorrect information, or that information ends up in the wrong hands. Risks that can lead to both financial and trust consequences for your company.
How do we get started with data classification?
You don't need to feel that you must start with a perfect, comprehensive framework from the beginning. The important thing is to create enough structure to be able to make better decisions.
- Determine the purpose. What do you want to be able to decide better about? Is it AI initiatives, investments, supplier selection, backup, incident management?
- Choose a classification model. Develop a simple classification model with, for example, four levels: public, internal, valuable, strict.
- Appoint information owners. Choose owners per business area, not per system. Owners should know which information is critical for the operation to function.
- Link classification to security requirements. Decide on security requirements and usage rules for your classes. For example MFA, encryption, backup, logging, access control and what may be used in AI tools.
- Get started! Make it "good enough" first, then fine-tune. A common pitfall where many get stuck is wanting to classify everything perfectly from the start.
What business benefits does information classification bring?
Having data classification in place will lead to business benefits in several ways:
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Effective decision-making. Activities based on relevant data, instead of sensitive and outdated information, are no longer guesses. They are strategic decisions.
- Cyber resilience. Information classification facilitates your incident management, reduces downtime and simplifies recovery.
- Proactivity and increased control. Classification shows the board, employees and customers that you have things under control. That you work proactively and actively with information and risk management.
- Regulatory compliance. Information classification helps you meet high demands in laws and regulations.
Information classification ultimately concerns management's ability to govern deliberately even when something goes wrong. Without control over which information is important, sensitive and useful, both security, AI initiatives and crisis management become guessing games. With a simple but well-thought-out information classification you get a common decision basis for what must work, what can wait and what should never be compromised
5 common questions and answers about data classification
- What is cyber resilience?
Cyber resilience goes beyond "just" preventing breaches. It is more about your ability to withstand, manage and recover from incidents. - How does Nordlo help us with information classification?
We run your information classification in Microsoft Purview which means we label information based on how sensitive it is and automatically apply the right protection. The classification follows the information whether it is shared via email, Teams or documents, which reduces the risk of mishandling and information leaks. - What is the difference between data classification and information classification?
The terms are often used interchangeably. Data classification focuses on the technical categorisation of data, whereas information classification more often emphasises the business context and how the information is used in the organisation. - Why is data classification a management issue?
Management is responsible for guiding the organisation through crises and incidents. Without knowing which data is critical or sensitive, it is not possible to make informed decisions, something that is crucial when every second counts. - How do we get started with data classification?
Start by determining the purpose, choose a simple classification model with e.g. four levels, appoint information owners per business area and link the classification to concrete security requirements. Aim for "good enough" first, then fine-tune afterwards.
Data classification: Without control over data, no reliable AI initiatives
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