Zero trust, Data Security

Data Classification and Zero Trust Security

Five zero-trust tips

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Guest blog courtesy of CYRISMA.

Data classification is where strong information security begins. Your data assets are your crown jewels and what cybercriminals are ultimately after. To protect this data and apply appropriate security controls, you need to first classify it by sensitivity or criticality.

This article provides an introduction to data classification, data sensitivity assessment, some questions to determine what level of protection specific data types need, and why data classification is an essential prerequisite for zero trust security.

What is Data Classification?

Data classification is the act of organizing information into categories based on its importance and sensitivity. It’s like sorting your files into labeled folders, but for digital information within an organization. This process makes data easier to find and manage, meet compliance and data privacy needs, and build a strong foundation for zero-trust security.

Think of data classification as a way to understand how valuable and vulnerable your information is.

By classifying data, you can decide:

  • Who gets to access it: Only authorized individuals should have access to sensitive information.
  • How to protect it: Extra security measures might be needed for more important data.
  • What rules to follow: There might be legal requirements for handling certain types of data.

    • Data Classification and Labeling Considerations

    To determine the sensitivity levels of their data assets, organizations can ask a range of questions based on their specific business context and compliance requirements. Most have internal policies and guidelines to categorize data.

    Here’s a list of sample questions to consider (remember that the specific questions you include to assess data sensitivity will be based on the internal guidelines set by your InfoSec team).

    Data Sensitivity

    1. Data Type: Briefly describe the type of data being collected or stored (e.g., customer names, employee performance reviews, financial records).
    2. Regulatory Requirements: Does this data fall under any specific industry regulations or compliance standards (e.g., HIPAA, PCI DSS)?

      3. Data Access

      1. Authorized Users: Identify the minimum set of roles or individuals who require access to this data to perform their job duties effectively.
      2. Access Justification: Explain why authorized users require access to this specific data point.

        3. Data Risk Assessment

        1. Disclosure Impact: Could unauthorized disclosure of this data significantly harm an individual or the organization (e.g., financial loss, reputational damage)?
        2. Data Presence: Does this data include any of the following sensitive data types:
          • Personally Identifiable Information (PII) (e.g., names, addresses, birthdates)
          • Government Issued IDs (e.g., Social Security Numbers, Driver’s License numbers, Passport numbers)
          • Financial Information (e.g., bank account numbers, credit card information)
          • Healthcare Information (protected by HIPAA)
          • Login Credentials (usernames and passwords)

            • Compliance Considerations

            1. Data Subject Rights (GDPR, CCPA): Does this data pertain to data subjects (individuals) and if so, what rights do they have under relevant regulations (e.g., access, rectification, erasure)?
            2. Data Minimization (GDPR): Is the data collected the minimum necessary for the intended purpose? Could the purpose be achieved with less sensitive data?
            3. Data Breach Notification (Multiple): Does this data fall under any regulations requiring notification in the event of a breach (e.g., HIPAA, PCI DSS)?
            4. Data Residency (GDPR): Are there any geographical restrictions on where this data can be stored or transferred (e.g., GDPR and data storage in the EU)?

              5. Additional Considerations

              1. Data Lifecycle: What is the expected retention period for this data? Is there a legal or regulatory requirement for retention?
              2. Data Storage: Where is this data physically stored (e.g., local servers, cloud storage)? Does the storage location meet the security requirements for the data sensitivity level?
              3. Data Encryption (HIPAA, PCI DSS, SOC2): Is the data encrypted at rest and in transit, as required by relevant regulations?
              4. Access Controls (Multiple): What access controls are in place to restrict unauthorized access to this data (e.g., user authentication, least privilege)?
              5. Audit Logging (Multiple): Are there audit logs in place to track access and modifications to this data, as required by some regulations?

                6. By answering these questions comprehensively, you can gain a deeper understanding of your data, classify it appropriately, and implement controls to ensure compliance with relevant regulations.

                Determining Data Sensitivity Levels

                Data comes in various shades of sensitivity, and understanding these nuances is crucial for effective information security. Here’s a breakdown of common data classification levels. This is a broad framework and specific sensitivity levels for each organization would be different and context-dependent.

                Data Sensitivity Classification

                • Public: This data poses minimal risk if disclosed. Think of university directories or a company’s social media posts – freely accessible to anyone.
                • Internal: While not for public consumption, the potential harm from exposure is low. Examples include an organization chart or internal service manuals.
                • Confidential: As the name suggests, this data needs to be kept private. Think of employment contracts or student loan information – a leak could have negative consequences for the organization.
                • Restricted (Highly Sensitive): This highly sensitive data carries serious financial, legal, or regulatory risks if leaked. Examples include Social Security numbers, medical records, and bank account information.
                • Archived (Inactive): This category encompasses data no longer actively used but still retained for legal, regulatory, or historical reasons, such as old financial reports or personnel records.

                  • Many organizations also use basic High, Medium and Low-sensitivity categories

                  • High Sensitivity: This data requires stringent controls due to legal protections (GDPR, CCPA, HIPAA) and the potential for significant harm from a breach. This includes restricted data like Social Security numbers and medical records.
                  • Medium Sensitivity: This data is for internal use only, but a breach wouldn’t be catastrophic. Examples include non-identifiable employee data or architectural plans for a new building.
                  • Low Sensitivity: This public information faces minimal risk if exposed. Public webpages, job postings, and blog posts fall under this category.

                    • Choosing the Right Classification System

                    The appropriate classification system depends on your organization’s specific needs and industry regulations. A more granular level of detail may be required if you need to meet more stringent and complex privacy regulations.

                    Data Types Protected by Law – Examples

                    Organizations today collect a vast amount of information about individuals, employees, customers, and partners. Some of this data is highly sensitive and requires special protection due to its potential for misuse. Here’s a breakdown of data types commonly subject to legal safeguards under regulations like GDPR, CCPA, HIPAA, and PCI DSS:

                    • Personally Identifiable Information (PII): This is any data that can be used to directly or indirectly identify an individual. Think of it as a digital fingerprint – a combination of details that can pinpoint a specific person. PII can be further categorized into basic PII, which includes common identifiers like name, address, phone number, and email address; and Sensitive PII (SPII) which encompasses data that poses a higher risk of harm if leaked. Examples include Social Security numbers, passport numbers, driver’s license numbers, and biometric data (fingerprints, facial recognition)
                    • Financial Information: Data related to an individual’s financial standing requires robust security measures. This includes payment card information, bank account numbers and other account information, investment account information, insurance information, and tax information.
                      • Login Credentials: Usernames, passwords, and other authentication details act as the keys to online accounts. If compromised, they can provide hackers with access to a wealth of personal information, financial data, or even corporate resources.
                      • Health Information: Data pertaining to an individual’s physical and mental health is highly sensitive and protected by regulations like HIPAA. Examples include medical history, treatment records, insurance information and genetic data 

                        • Navigating the Legal Landscape

                        The specific legal requirements for protecting this data vary depending on the regulation, the industry, and the context in which it is collected and used. However, some general principles apply across most regulations:

                        • Data Minimization: Organizations should collect only the data necessary for a specific purpose and avoid stockpiling unnecessary information.
                        • Access Controls: Limit access to sensitive data to authorized personnel only, following the principle of least privilege (granting the minimum access rights needed to perform a job function).
                        • Data Encryption: Encrypt sensitive data at rest and in transit to safeguard it from unauthorized access, even if a breach occurs.
                        • Data Breach Notification: Report data breaches involving protected data according to regulatory requirements. This allows individuals to take steps to protect themselves from potential harm.
                        • Individual Rights: Certain regulations (e.g., GDPR, CCPA) provide individuals with rights to access, rectify (correct), or erase their personal data. Organizations must establish processes to accommodate these rights.

                          • By understanding the types of data that require legal protection and implementing appropriate security measures, organizations can ensure compliance with relevant regulations, safeguard sensitive information, and build trust with individuals who entrust them with their personal data.

                          Data Classification and Zero Trust Security

                          Here’s why data classification is an essential prerequisite for zero trust security and control implementation:

                          Zero Trust Relies on Knowing What Data Needs Strong Protection

                          At its core, zero trust is a security model built on the principle of “never trust, always verify.” This means every user and device attempting to access an organization’s resources needs to be continuously authenticated and authorized. But how can you determine the appropriate level of verification if you don’t understand the sensitivity of the data being accessed?

                          Data Classification Provides the Roadmap

                          Data classification acts as a roadmap for implementing zero trust controls effectively. By classifying data as high, medium, or low sensitivity, organizations can tailor their security measures to the level of risk involved. This allows them to:

                          • Focus Resources on High-Value Assets: Zero trust isn’t a one-size-fits-all approach. By identifying the most sensitive data (e.g., financial records, medical information), organizations can prioritize resources and implement stricter access controls for those specific data sets.
                          • Simplify Access Decisions: Granular data classification empowers administrators to define clear access rules. For instance, low-risk public data might only require basic authentication, while highly sensitive data might require a more rigorous verification process involving multiple factors.
                          • Reduce Friction for Users: Zero trust shouldn’t create excessive hurdles for authorized users. With data classification, organizations can streamline access for low-risk data while maintaining appropriate security for sensitive information. This helps maintain user productivity without compromising security.

                            • Data Classification Strengthens Zero Trust Defense

                            Zero trust aims to minimize the attack surface by limiting access to only the specific data required for a user’s role. Data classification enables this approach by:

                            • Identifying Data at Risk: Knowing which data is sensitive and where it resides helps organizations prioritize patching vulnerabilities and implementing security controls around those specific systems. This focused approach strengthens the overall security posture.
                            • Preventing Lateral Movement: In the event of a breach, data classification can help prevent attackers from moving laterally within the network and accessing high-value information. By compartmentalizing sensitive data and restricting access based on classification, organizations can limit the potential damage from a cyberattack.

                              • In essence, data classification provides the context for zero trust. It allows organizations to understand what needs to be protected, prioritize resources, and implement targeted security measures for a risk-based approach to information security.

                              How the CYRISMA Platform can help

                              CYRISMA’s all-in-one cyber risk and compliance management platform includes powerful data discovery, classification and risk mitigation capabilities. You can leverage its Sensitive Data Discovery scans to:

                              • Find and classify sensitive data in both your on-prem and cloud computing environments (Office 365, Google Workspace).
                              • Secure sensitive data by encrypting, deleting, or moving it to a secure location, or by modifying access permissions.
                              • Meet compliance needs using CYRISMA’s powerful Compliance module. Run gap assessments and close data privacy gaps from within the platform.

                                • Additional features like dark web monitoring, Active Directory monitoring, and estimates of the monetary value of your data make the capability even more powerful.

                                Request a demo for a deep dive!

                                Related

                                Related Terms

                                Asymmetric WarfareByteCipherCiphertextCyclic Redundancy Check (CRC)Data AggregationData Loss Prevention (DLP)DecryptionDiffie-HellmanDigital Signature

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