Building a Robust Data Classification Policy

What is a data classification policy?

A data classification policy is a set of guidelines and procedures that actively define how data should be categorized and protected within an organization. It outlines the criteria for classifying data based on its sensitivity, importance, and potential risks. The policy provides clear instructions on how to label, handle, store, transmit, and dispose of different types of data. By implementing a data classification policy, organizations ensure that sensitive information is properly protected, access controls are enforced, and appropriate security measures are in place to prevent unauthorized disclosure or misuse of data.

Data classification standards

Data classification standards are guidelines and rules for categorizing and handling data based on its sensitivity and importance. Here’s a shortened list of key aspects of data classification standards:

1. Data Categories: Classify data into categories like public, internal, confidential, and restricted.
2. Labels and Tags: Assign clear labels or tags to data to indicate its classification.
3. Access Control: Define who can access and modify data based on its classification.
4. Retention and Deletion: Determine how long data should be retained and how it should be securely disposed of.
5. Handling Procedures: Provide guidelines on how to handle, share, and store data at each classification level.
6. Training: Ensure employees are trained on data classification and handling practices.
7. Audit and Monitoring: Implement monitoring mechanisms to track data access and changes.
8. Incident Response: Establish procedures for responding to data breaches or unauthorized access.
9. Compliance: Ensure that data classification standards align with legal and regulatory requirements.
10. Documentation: Maintain records of data classification decisions and policies.
11. Periodic Review: Regularly assess and update data classification to reflect changes in data and risks.

These standards help organizations protect sensitive information, maintain data integrity, and comply with data privacy regulations.

Understanding the types of data classification

The types of data classification can vary based on the specific needs and requirements of an organization. Here are three commonly used types of data classification:

1. Confidentiality-Based Classification: This type of classification focuses on the level of sensitivity or confidentiality of data. It categorizes data into different levels such as confidential, internal use, public, or restricted access based on the potential harm or impact if the data is disclosed to unauthorized individuals.
2. Regulatory-Based Classification: This classification type is driven by compliance requirements. It involves categorizing data based on the specific regulations or legal requirements that apply to the organization. For example, data may be classified as personally identifiable information (PII), protected health information (PHI), or financial data, based on the relevant regulatory frameworks such as GDPR, HIPAA, or PCI DSS.
3. Criticality-Based Classification: This classification focuses on the criticality or importance of data to the organization’s operations. It involves categorizing data based on its significance in supporting business functions or the potential impact if the data becomes unavailable or compromised. Data may be classified as critical, essential, or non-critical based on its importance to the organization.

Data classification policy template example

1. Data Classification Levels:

• Public Data: Information that is intended for public consumption and can be freely accessed by anyone. Examples include blog posts, public comments, and general website content.
• Internal Data: Information used internally by blog administrators and staff, not meant for public access. Examples include editorial calendars, unpublished drafts, and administrative logs.
• Subscriber Data: Information related to blog subscribers or users who have registered on the blog. This includes names, email addresses, and preferences.
• Analytics Data: Data collected for the purpose of analyzing blog traffic and user behavior. This may include IP addresses, user agents, and browsing history.

2. Data Handling Guidelines:

Public Data:

• Public data is openly accessible to all visitors.
• Ensure content aligns with the blog’s publishing guidelines.
• Implement spam filters and moderation for comments.

Internal Data:

• Access restricted to authorized blog administrators and staff.
• Use strong passwords and two-factor authentication for access.
• Store internal documents on secure servers with access controls.

Subscriber Data:

• Handle subscriber data with care, following data protection laws.
• Collect only necessary information and obtain explicit consent.
• Securely store subscriber data using encryption and access controls.

Analytics Data:

• Use analytics data solely for the purpose of improving the blog.
• Anonymize data wherever possible.
• Protect analytics tools and data repositories with strong security measures.

3. Compliance:

• Comply with applicable data protection regulations (e.g., GDPR, CCPA).
• Provide transparency to subscribers regarding data collection and usage.
• Ensure that third-party tools and plugins used for analytics also comply with data protection laws.

4. Security Measures:

• Regularly update and patch the blog platform and plugins.
• Implement strong encryption for data in transit (e.g., SSL/TLS).
• Monitor and log access to sensitive data.
• Train staff on data protection best practices.

5. Data Retention:

• Define data retention periods for different data types (e.g., subscriber data).
• Regularly review and delete data that is no longer needed.
• Document data retention policies and procedures.

6. Incident Response:

• Develop an incident response plan to address data breaches or security incidents.
• Establish procedures for notifying affected individuals and authorities if necessary.
• Learn from incidents to improve data protection practices.

7. Review and Updates:

• Regularly review and update this data classification template to reflect changes in data handling practices and regulations.
• Conduct periodic audits to ensure compliance with the template and relevant laws.

Minimizing data breach risk with data classification policies

Having a robust data classification policy in place can significantly contribute to avoiding data breaches and enhancing breach response. Here’s how:

• Risk Identification and Prioritization: A data classification policy helps identify and categorize data based on its sensitivity and importance. This enables organizations to prioritize their security efforts. For example, highly confidential data can be given the highest level of protection, while less sensitive data may have fewer security controls.
• Access Control: Data classification policies establish who can access specific types of data. By clearly defining access rights and permissions, organizations can limit access to sensitive data only to authorized personnel, reducing the risk of unauthorized access and data breaches.
• Encryption and Data Protection: Based on data classification, organizations can implement appropriate encryption and data protection measures. Highly sensitive data may require stronger encryption methods, while less sensitive data may have less stringent encryption requirements.
• Data Retention and Deletion: Data classification policies often include guidelines for data retention and deletion. Organizations can specify how long data should be retained and when it should be securely deleted when it’s no longer needed. This reduces the risk of retaining unnecessary data that could be a target for attackers.
• Data Handling Procedures: Data classification policies define how different types of data should be handled throughout their lifecycle. This includes data collection, storage, transmission, and disposal procedures. Having clear guidelines ensures that data is managed securely at every stage.
• Incident Response Planning: Data classification helps organizations tailor their incident response plans to the types of data they handle. In the event of a breach, having predefined responses for different data categories allows for a more efficient and effective response. Highly sensitive data breaches, for instance, may require more immediate and specialized actions.
• Employee Training and Awareness: Data classification policies can be used as a basis for employee training and awareness programs. Employees can better understand the importance of data security and their roles in protecting sensitive information when they know how data is classified and why it matters.
• Legal and Regulatory Compliance: Many data protection laws and regulations require organizations to classify and protect personal and sensitive data appropriately. Having a data classification policy in place helps demonstrate compliance with these laws, reducing the risk of legal penalties.
• Data Auditing and Monitoring: With data classification, organizations can implement more focused data auditing and monitoring. This allows for the detection of unusual or unauthorized access to sensitive data, enabling quicker response to potential breaches.
• Vendor and Third-Party Management: Data classification policies can extend to how third-party vendors handle data. Organizations can specify their expectations regarding data protection, classification, and handling in contracts with vendors, reducing third-party risk.

A shared responsibility

Creating a successful data classification policy involves the active participation of the following roles:

• Data Owners: Actively participate in defining the classification criteria and determining the sensitivity levels of data within their respective domains. They have the responsibility to understand the value and importance of data assets and collaborate in the classification process.
• Information Security Team: Actively works on developing the data classification policy, including the classification categories, criteria, and guidelines. They ensure that the policy aligns with security best practices and regulatory requirements. They also play a crucial role in implementing appropriate security controls for classified data.
• Legal and Compliance Experts: Actively provide guidance on legal and regulatory requirements pertaining to data protection and privacy. They ensure that the data classification policy adheres to applicable laws, regulations, and industry standards. They may also assist in drafting relevant sections of the policy.
• Data Governance Team: Actively oversees the overall management and governance of data within the organization. They collaborate with data owners and the information security team to define and enforce data classification standards and procedures. They may also provide guidance on data lifecycle management and retention policies.
• IT Department: Actively supports the implementation of the data classification policy from a technical perspective. They provide expertise in deploying and managing tools and technologies necessary for data classification, encryption, access controls, and data monitoring.
• Employee Training and Awareness: Actively involved in educating employees about the data classification policy, its importance, and their responsibilities in handling classified data. They conduct training sessions, create awareness campaigns, and provide guidance to ensure employees understand and follow the policy.
• Management and Leadership: Actively support the development and implementation of the data classification policy. They provide the necessary resources, endorse the policy, and promote a culture of data protection within the organization. They also actively communicate the importance of data classification and ensure compliance across the organization.

Creating effective data classification policies with BigID

BigID is a data intelligence platform for privacy, security, and governance that helps organizations improve their data classification policy efforts in a simple and effective manner. Here’s a simplified explanation of how BigID assists organizations:

• Automated Discovery: BigID utilizes advanced data discovery techniques to automatically scan and discover sensitive data across various systems, applications, and data repositories. It identifies and classifies data based on predefined or customizable classification categories, such as personally identifiable information (PII), financial data, or intellectual property.
• Contextual Understanding: BigID goes beyond basic data identification and classification. It analyzes the contextual information surrounding the data, such as data relationships, user access patterns, and data usage, to provide a more comprehensive understanding of the data’s sensitivity and importance.
• Advanced Classification: Using machine learning and artificial intelligence algorithms, BigID applies intelligent classification techniques to accurately categorize data based on its content, context, and metadata. This enables organizations to achieve more accurate and consistent data classification results.
• Risk Assessment and Remediation: BigID’s Risk Scoring App and Data Remediation App offers risk assessment and remediation functionalities that help organizations identify high-risk data areas and prioritize their classification efforts. It assists in evaluating the impact of data breaches, assessing the effectiveness of security controls, and providing recommendations for remediation.
• Compliance Reporting: BigID’s Privacy Suite has a wide range of tools for generating comprehensive compliance reports and documentation, which can be used to demonstrate adherence to data classification policies, regulatory requirements, and industry standards. These reports help organizations in audits, regulatory assessments, and proving compliance to stakeholders.

To enhance your data classification efforts and streamline categorization of sensitive data— get a 1:1 demo with BigID today.