What is sensitive data?

Sensitive data is any information that must be protected due to its confidential, personal, or financial nature and the harm that could come if it is disclosed, misused, or accessed without authorization or in violation of legal or compliance requirements governing its handling.

Sensitive data overview

Sensitive data includes a wide range of information categories, each with its own requirements for privacy and security. The protection of sensitive data is important for individuals, organizations, and governments to prevent privacy violations, identity theft, financial loss, or harm to an individual’s or organization’s reputation.

What is the difference between personal data vs sensitive data?

The terms personal data and sensitive data are frequently used (and misused) in the context of privacy and data protection. While related and sometimes overlapping, these terms refer to different categories of information, each with implications for how data should be handled and protected. It is important to note that while all sensitive data is personal data, not all personal data is classified as sensitive data.

Sensitive data, data security, and data breaches

Sensitive data requires data security protection to prevent data breaches, which can result in negative impacts, especially for failure to adhere to regulatory requirements. The handling and processing of sensitive data are subject to stringent legal and regulatory requirements aimed at protecting individual rights and privacy.

Data security measures ensure the confidentiality, integrity, and availability of sensitive data.

Commonly deployed data security solutions include access controls, encryption, network security, incident response planning, and employee training.

Sensitive data, data classification, and data privacy

The classification of sensitive data is critical to effectively protecting data privacy. Sensitive data classification ensures that organizations can manage their data assets securely, comply with privacy regulations, and protect individuals’ privacy rights by associating appropriate controls with information.

Data classification organizes data into categories that make it easier to manage and protect by tagging it based on its sensitivity level, regulatory requirements, and value. Usually, data is classified into categories such as public, internal use only, confidential, and highly confidential, with sensitive data often falling into the latter categories.

Data classification facilitates:

1. Access control by restricting access to sensitive data to only authorized users.
2. Compliance by aligning sensitive data handling practices with legal and regulatory requirements.
3. Risk management by identifying sensitive data and the potential risks associated with its exposure to enable organizations to prioritize security measures to mitigate risks.

Types of sensitive data

Following are several categories of sensitive data.

Health and medical information

Health and medical information involves an individual’s well-being and privacy. Unauthorized access not only violates a person’s privacy but can also be used to commit insurance fraud.

Examples of health and medical information that are classified as sensitive data include:

1. Biometric data (e.g., fingerprints, DNA, or retina images)
2. Genetic data
3. Health insurance identification numbers
4. Information on physical or mental health conditions
5. Medical history
6. Payment for health care
7. Prescription information
8. Treatment and diagnosis information

Financial information

Because unauthorized access to financial information can lead to fraud and monetary loss, it is categorized as sensitive data. Examples of financial information categorized as sensitive data include:

1. Bank account details
2. Bank account numbers and sort codes
3. Credit card numbers
4. Credit reports and credit scores
5. Debit card details
6. Investment records
7. Payment history
8. Tax filings and associated identifiers

Intellectual property and trade secrets

Intellectual property and trade secrets are data related to an organization’s proprietary knowledge, which can lead to competitive harm and financial loss if accessed without authorization. Examples of this category of sensitive data include:

1. Contract details
2. Corporate Information
3. Financial forecasts and reports
4. Internal audit reports
5. Organizational plans and strategies
6. Patent details
7. Proprietary research
8. Research and development data
9. Trade secrets
10. Unpublished patent applications

National security information

Examples of national security information that is considered sensitive data are that which, if disclosed, could potentially harm a nation’s security and interests, such as:

1. Counterterrorism activities
2. Cybersecurity defenses, vulnerabilities, incident response plans, and cyber operations
3. Details about critical infrastructure
4. Diplomatic communications
5. Economic security information
6. Government continuity plans
7. Military plans

Legal and investigative data

Information that is part of legal proceedings or investigations is considered sensitive data. This type of sensitive data includes:

1. Background check results
2. Certain court documents
3. Criminal records
4. Law enforcement investigation information
5. Legal disputes and litigation information
6. Personal legal matters

Educational records

Educational records are a type of sensitive information that includes personal information about minors and young adults in educational settings. Examples of educational records categorized as sensitive data are:

1. Admission applications
2. Disciplinary records
3. Enrollment
4. Financial aid information
5. Grades
6. Student identification numbers
7. Transcripts

Employment records

Employment records include personal information related to employment that is personal nature and has the potential for misuse that could result in workplace discrimination or harassment as well as impact personal and professional reputations and relationships. Examples of employment records that are considered sensitive data include:

1. Background check information
2. Disciplinary records
3. Employment history
4. Performance evaluations
5. Personal documents submitted for human resources purposes
6. Salary and payroll information
7. Workplace incidents

Government-issued identification numbers

Government-issued identification numbers serve as official identifiers and are highly sensitive as they can be exploited for illegal activities if compromised. Several examples of government-issued identification numbers that are considered sensitive data are:

1. Driver’s license number
2. National identification number (NIN)
3. Passport number
4. Social Security Number (SSN)
5. Voter ID number

Sensitive personal information

Sensitive personal information is a subset of personal data that includes:

1. Data concerning sex life or sexual orientation
2. Political opinions
3. Racial or ethnic origin
4. Religious or philosophical beliefs
5. Trade union membership

Sensitive data and data privacy regulations

Many data privacy regulations mandate protections for sensitive data. These data privacy regulations are legal frameworks designed to protect individuals’ privacy rights and ensure the responsible collection, processing, storage, and sharing of personal and sensitive data.

Data privacy regulations set forth detailed requirements for organizations that handle sensitive data.

Key areas covered include consent for the collection of sensitive data, data protection measures, breach notifications, and individuals’ rights over their data.

Governments worldwide have implemented strict privacy laws to safeguard sensitive data and protect individuals’ privacy. Organizations that handle sensitive data need to understand these regulations as well as the ongoing updates and additions to the data privacy legislation landscape to ensure compliance, maintain customer trust, and avoid potential legal and financial repercussions.

The following are examples of the many global data privacy laws that mandate sensitive data protection. Each of these and many other data privacy laws carry stringent penalties for noncompliance.

The European Union’s (EU) General Data Protection Regulation

The GDPR is a comprehensive data protection law that enforces strict rules for collecting, storing, and processing the personal data of any EU resident. It grants individuals significant control over their personal information, including the right to access, correct, and delete their data.

Health Insurance Portability and Accountability Act (HIPAA)

This U.S. federal law mandates data privacy and security protections to safeguard medical and health records. All healthcare providers, health plans, and healthcare clearinghouses in the U.S. are required to comply with HIPAA.

Personal Information Protection and Electronic Documents Act (PIPEDA)

PIPEDA is a Canadian law that sets data privacy standards for managing personal information by private sector organizations. It emphasizes consent, limits data collection, and ensures transparency in the handling of personal and sensitive data.

California Consumer Privacy Act (CCPA)

This state law enhances privacy rights and consumer protection for residents of California. It provides California residents with the right to know about the personal data collected about them, the right to delete personal data, and the right to opt out of the sale of their personal data.

Sensitive data best practices

The specific approaches to data protection for sensitive data vary by organization, the types of information they handle, and how it is used. The following best practices provide an overview of several of the many tactics commonly used to protect sensitive information from unauthorized access.

Data discovery and classification

Identifying and classifying data based on its sensitivity and value is the first step in implementing effective data protection measures. This process helps in understanding what sensitive data the organization has, where it resides, and how it is used. Understanding this enables targeted security strategies.

Access control and least privilege

Access to sensitive data should be tightly controlled, ensuring that only authorized personnel have access based on their roles and responsibilities. Employing the principle of least privilege (PoLP) minimizes the risk of unauthorized access by limiting user access to the minimum necessary to perform their job functions.

Anti-malware protection

Comprehensive anti-malware solutions should be used to protect against various forms of malware that can compromise sensitive data. This should include the automation of regular system updates and malware scans to detect and neutralize emerging threats.

Data encryption

Encryption should be applied to all sensitive data, both at rest and in transit, regardless of where it is stored or how it is transmitted. Even if data is intercepted or breached, encryption can render it unreadable to unauthorized parties.

Data masking

When displaying data for testing or development purposes, data masking is used to hide sensitive information to ensure that developers or testers do not have access to real data and that sensitive data is not accidentally exposed during reviews.

Data minimization

Organizations should collect only the data necessary for the specified purpose and limit data retention periods. This reduces the risk and impact of data breaches.

Employee training and awareness

Human error is a common cause of data breaches. Regular training programs can significantly reduce this risk by raising awareness among employees about data security best practices, recognizing phishing attempts, and securely handling sensitive information.

Incident response planning

A well-defined incident response plan ensures that the organization can respond swiftly and effectively to data breaches, minimizing damage and restoring operations as quickly as possible. Incident response plans should be tested and updated regularly to ensure that all aspects are optimized and everyone involved understands their role.

Security audits and monitoring

Conducting regular audits and continuous monitoring of systems and networks helps proactively identify vulnerabilities, unauthorized access attempts, and other security threats. This enables prompt and effective response and mitigation.

Sensitive data FAQ

Here are the answers to some frequently asked questions about sensitive data.

What qualifies as sensitive data?

Sensitive data includes any information that, if exposed, could result in harm to an individual or organization. This includes personally identifiable information (PII), educational records, employment records, financial information, government-issued identification numbers, health and medical information, intellectual property and trade secrets, legal and investigative data, national security information, and sensitive personal information.

How should sensitive data be stored?

Sensitive data should be stored with strong encryption both at rest and in transit. Access should be restricted to authorized users only with a specific need (i.e., least privilege), employing strict authentication and access control measures. Additionally, organizations should ensure that storage solutions comply with relevant data protection and privacy requirements.

How can sensitive data be identified within an organization?

Organizations can identify sensitive data by conducting data discovery and classification processes. This involves scanning applications, storage systems, and databases to locate sensitive information and then classify it based on its level of sensitivity and the required protection measures.

What should be done to dispose of sensitive data securely?

Secure disposal methods vary depending on the type of data. For physical records, secure shredding or destruction is recommended. For electronic data, use cryptographic erasure or physical destruction of storage media in compliance with regulatory requirements and industry standards.

Who is responsible for managing sensitive data within an organization?

While specific roles such as data protection officers (DPOs) or IT security teams are directly responsible for managing sensitive data, the protection of sensitive data is a shared responsibility that requires the vigilance and support of everyone in the organization who handles or accesses it.

How often should sensitive data policies be reviewed and updated?

Sensitive data policies should be reviewed and updated regularly, at least annually, or whenever there are significant changes to data processing activities, legal requirements, or the organization’s IT environment.

Sensitive data comes with strict rules that require focus

Protecting sensitive data involves implementing robust security measures and adhering to complex regulatory requirements. Organizations and individuals must be vigilant about efforts to ensure the protection of sensitive data. Specific attention is required to how sensitive data is handled to ensure privacy and security that prevents breaches and ensures compliance with regulations.

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