What are the key principles and steps for developing a data classification policy?

I would like to add the intangible risk of loss of reputation. In today’s competitive market where digital entrepreneurship is the goal of most organizations they also need to be aware that they are just a tick away from disaster and one incorrectly labelled document could cause irreversible reputation damage. A strong on-boarding process for new employees and continuous refresher trainings on information security with emphasis on data classification is paramount. Further, proactive security policies like data restrictions (restrict USB copy, Web filtering etc.) must be well introduced and explained to the user community and made more acceptable.

There can be various objectives for developing data classification policy, the top five objectives are as below: Data Protection: The primary objective of data classification is to protect sensitive information and prevent unauthorized access, use, or disclosure. Compliance: Data classification helps organizations meet regulatory and legal requirements Risk Management: It supports risk management efforts by allowing organizations to focus on protecting the most critical and sensitive data. Resource Allocation: It allows organizations to prioritize security measures based on the sensitivity of data. Incident Response: Data classification enables organizations to respond promptly and effectively to security incidents.

Why: Define the expected objectives behind the data classification initiative and determine how it will help in achieving these objectives Who: Appoint a person responsible for leading the data classification initiative in addition to developing the register of stakeholders to be engaged as part of this initiative. What: Determine the scope of the initiative by identifying the informational asset that will be subject to classification How: define classes to be used Aligned with the expected objectives and how the data will be organized into categories and subcategories When: Determine the frequency of the initiative, is it limited or continuous over time? Where: Determine the departments and systems affected by the classification.

First, identify data categories like PII, financial data, intellectual property, or proprietary information. This determines the policy's scope. Next, define classification levels such as Public, Internal Use, Confidential, and Highly Confidential, each with clear criteria. Determine appropriate handling and access controls for each level, specifying who can access, modify, transmit, or delete data. Implement security measures like encryption and multi-factor authentication. Utilize data classification tools to automate the process, scanning and analyzing data based on rules. Communicate the policy to all employees and stakeholders, providing training on data classification importance and handling.

Defining objectives and scope is the cornerstone of an effective data classification strategy. By understanding stakeholders, legal obligations, and organizational needs, we can build a robust data-centric security architecture. Engaging data owners, planning user awareness, and employing best-of-breed controls ensures that our approach is both compliant and aligned with our business goals. Embracing a data-centric view not only enhances security but propels digital finance strategy forward.

Depending on the laws, regulations, or standards that apply to your organization, you may need to add additional categories of data that go beyond "sensitivity-based" categories. You should make sure you take into account all standards you are required to follow, and make sure your classification categories and criteria cover all data your organization may handle. For example, your organization could potentially be involved in handling healthcare or credit card data, which almost certainly would include responsibilities that go beyond what the general classification categories outlined above require.

I believe before establishing categories, an important step is to understand your data landscape. Understanding the data landscape involves gaining a comprehensive overview of the types, sources, locations, and flow of data within an organization. This can be achieved using the below steps: 1. Data Inventory - Identify all data inventories 2. Data mapping - Determine how data flows within the organization 3. Data types and sensitivity 4. Data flows and connectivity - Determine how data flows between different systems, departments and individuals. 5. Data Vulnerabilities - Identify all data sources that have some kind of vulnerabilities and their associated risk levels. 6. Data retention and disposal - Identify data retention procedures.

Classifying your data is divided into two main criteria. The first is public data where anyone can access it and the other is PI (Personal Information) data that should remain private as it could be used by hacker to impersonate you. Example of public data : your email address, your office location, your work phone number. Example of private data : your residential address, your mother's maiden name, your date of birth, your personal phone number. Although both can be classified manually or automatically, the resulting classification should be validated again by auditor after the classification is done to avoid any mixup regarding private and public data.

Identify stakeholders and establish a team: Involve key stakeholders such as IT, legal, compliance, data owners, and senior management to ensure a holistic approach to data classification. Establish a cross-functional team responsible for developing and implementing the policy.

Furthermore, it is crucial to establish a robust data governance framework to support the effective implementation and enforcement of your data classification policy. A well-defined data governance framework provides the necessary structure, processes, and accountability to manage data throughout its lifecycle.

Implementing technology controls is a crucial aspect of data classification and protection. Hence, before enforcing the policy required controls needs to be implemented. The following list provides some of these controls: 1. DLP Systems 2. Access controls 3. Encryption requirements 4. Endpoint security 5. Security Configuration Management 6. Data backup and recovery

This is the part where buy-in from the business is critical. If your goal was simply to document that you have a data classification policy, but the business does not understand why it’s important, then the policy will sit on a shelf and collect dust. On the other hand, if the business understands the risk that this mitigates, they will fully support new business processes to implement the policy.

As with many other parts of an ISMS there is often a perspective that data classification and all that goes with it is a security team responsibility. The awareness and education of the context of data classification and how it affects all areas of an organisation must be a key part of the ISMS strategy as this element underpins all other areas of information security and controls. If the business does not know why it is doing it and understands what type of data and information it is using then it simply becomes another tick box exercise for compliance that comes about once a year before the auditors come on site.

Principles: - Data sensitivity, - Ownership, - Impact Upon a breach Steps: - Identify critical assets - Classification (ex. Based on CIA) - Labelling - relevant controls - Determine review period