The Role of Data Categorization in Information Security Policies

Information has always been the lifeblood of civilization, yet not all information holds the same weight. Historically, society didn’t pay much attention to data flow organization, separation, reservation, masking or encryption of information as it is today. Usually it was somehow natural, rarely regulated and, sometimes, even, chaotic, when compared to nowadays. First of all, that was due to limited information exchange, storage and processing capabilities. However, as the digital age dawned and the flow of information rapidly accelerated, traditional approaches began to falter and, in some cases, cause harm.

Technological advancements emerged as a catalyst, creating a multitude of avenues for data acquisition, processing, storage, and dissemination. Alongside with countless privileges, new possibilities and revolution in social, scientific and technological fields, not all entities displayed responsibility in handling this valuable resource. As personal data is the most common type of information, personal data leaks, losses, unauthorized sharing, and unlawful utilization touched almost every user of global network.

To deal with these problems there is a need in a system approach. Governments intervened at the state level, instituting regulations while also focusing on key data categories. At the corporate level, the solution manifested in the creation and implementation of robust information security policies, standardization of management and adopting best cybersecurity practices. At the individual level, assumed a more active role of person in safeguarding their confidential data, increasingly prioritizing awareness and adopting methods of personal data security.

Categorizing information

Categorization of information is one of the initial steps in effective and secure system development. However, presently, there is no universally adopted global methodology for categorizing data. Different countries and organizations often adopt their proprietary approaches that, yielding a diverse array of classification criteria. Absence of such unified reference model creates extra complexity during implementation, especially, if companies’ operations exceed one jurisdiction, like US or EU.

Information policies operate on two distinct levels. The first level entails rules that are universally applicable to all data, representing the baseline standards. The second level centers on distinct data types. A common classification approach involves several broad groups based on key criteria such as data source, storage duration, context etc. The end result of a such classification, ideally, should cover all data types used and collected by the company.

For instance, a simplified classification based on data source might look like this:

1. Primary user data: data directly provided by users during registration or usage.

2. Derived user data: data generated algorithmically based on user behavior.

3. Processor data: data entered or generated by developers.

4. Employee data: personal data of company employees and entities associated with the company.

5. Third-party data: data obtained through contractual relationships with external entities.

Data security policy implementation

Data security policies are not mere theoretical constructs but actual documents, or sometimes a set of documents that contains directives, regulations, limitations, and recommendations. These elements collectively dictate data-related activities within an organization aimed on information security. Policies are created for internal consumption, which grants a degree of flexibility in their formulation. During the policy formulation process usability, expandability and adaptability to evolving requirements must be considered.  Beyond fundamental aspects like information management and organizational workflow, policies can also include secondary provisions. The most common secondary provisions are employee qualifications, the authority of the individual responsible for information security, internal training vectors, and external personnel or teams interaction regulations.

Technical Aspects of Policies

The technical aspect of policies contains base functions of the system, outlining the technical and software tools utilization and their interplay. This aspect is aimed to reduce the risks based on hardware and software excluding human factor. It can include potential technical vulnerabilities scenarios, key technical personnel qualification requirements, system monitoring frequency and methods.

Creating an effective data security policy requires decent level of clarity and structure. To simplify the formulation process in initial stages certain fundamental queries can be used. Adopting information from the answers can change the future policy trajectory and make it more suitable for your use case. Provided further queries could be useful to capture the unique characteristics of the company. However, the list should not be limited by these queries and must be extended based on context and company specialization.

What data is being collected and processed?

What attributes characterize the collected information?

How should different categories of information be stored?

What are the appropriate protocols for transporting diverse information types?

How should sensitive data be handled?

What are the typical scenarios for data usage?

How should employee access rights to various information categories be distributed?

How should automated information processing systems be controlled?

Which regulatory statutes should be considered?

What methodology should be employed to conduct ongoing risk assessments?

What protocols should be developed in response to possible negative events?

What measures should be taken in the aftermath of such incidents?

How does the life cycle of distinct information categories unfold?

Sensitive Information

Common practice entails users entrusting processors with personal data, anticipating its secure storage and responsible use. Yet, different personal data types got various attributes. Sensitive data got heightened value and respectively higher risk connected to processing and storage. This refers to data whose exposure or breach could lead to significant harm for an individual, spanning from physical health to career. While the categories of sensitive data categories might vary based on jurisdiction, the base definition remains unchanged – the high likelihood of negative consequence upon unauthorized disclosure. Highlighting such an important information is a part of data categorization process, that can save from various unwanted complications, if done properly and in time. 

Sensitive data constitutes various categories:

1. Race and ethnicity

2. Political, religious, and ideological beliefs

3. Health status

4. Sexuall life

5. Genetic and biometric data

6. Geolocation data

7. Criminal record information

Conclusions

• Modern data exchange offers many benefits, but the risks to private data are high.
• A three-level security system (Government, Company, Individual) is needed to reduce data-related problems.
• Government-level rules cover basics and critical data types.
• Government regulations vary by jurisdiction and may not always align without additional steps.
• Company-level policies put regulations into practice and include aspects crucial to the company and users.
• Individuals play a role in understanding rights and managing personal data.
• There is no one-size-fits-all data categorization model.
• Data categorization aims to create a structure for managing data safely.
• Technical aspects of data policy are as important as the principles.
• Different data categories need different handling conditions.