The increased focus on data privacy regulations has us thinking a lot about confidential data, often assuming it’s just personal information. But the definition can be surprisingly complex, even for personal data!

1. The Regulatory Maze: Personal Data Definitions
2. Confidential Data: Beyond Personal Information
3. Examples of Confidential Data:
4. Securing Confidential Data
5. Confidential vs. Sensitive Data: Understanding the Distinction
6. Remember:

The Regulatory Maze: Personal Data Definitions

Data privacy regulations around the world share some common ground, but also have key differences. Here’s a glimpse into how three major regulations define “personal data”:

• POPIA (South Africa): Focuses on information relating to identifiable living individuals and, in some cases, juristic persons. Examples include opinions about individuals, identifying numbers, and correspondence.
• GDPR (European Union): Takes a broader approach, encompassing any information related to an identified or identifiable natural person. This includes online identifiers like IP addresses and cookie IDs, along with genetic data.
• NDPR (Nigeria): Similar to the GDPR, it defines personal data as information concerning an identifiable individual but offers no specific examples.

As you can see, all three regulations emphasize identifiable individuals. However, POPIA and NDPR provide more specific examples, while the GDPR offers a broader definition encompassing various data types.

Confidential Data: Beyond Personal Information

Confidential data goes beyond personal information. It refers to any information that an organization considers private and not intended for public disclosure. Let’s break it down into three categories:

1. Protection of Own Data: This includes information belonging to the organization itself, such as research findings, financial data, and internal communications.
2. Protection of Others’ Data: Data belonging to others, like employee and student information, also falls under confidential data. This type of data is often protected by specific laws and regulations.
3. Protection of Personal Data: This encompasses information related to identifiable individuals, including basic employee, student, and IT user data.

Examples of Confidential Data:

• Research findings and financial data
• Employee and student data
• IT user data (usernames, passwords, certificates)
• IT usage data (logs related to IT service use)
• Business-critical data (strategic documents, accounting data, donor information)
• Intellectual property (patent applications, trade secrets, know-how)
• Sensitive personal data (information regarding race, ethnicity, political views, religious beliefs, union memberships, health data, and sexual orientation)

Securing Confidential Data

Organizations typically use various measures to protect confidential data, including:

• Encryption: Scrambles data to make it unreadable without a decryption key.
• Access Controls: Limits access to authorized individuals only.
• Data Masking: Replaces sensitive data with non-identifiable values for specific purposes.
• Strict Access Policies: Clear guidelines outlining how confidential data can be accessed, used, and shared.

Confidential vs. Sensitive Data: Understanding the Distinction

Both confidential and sensitive data are considered private, but there’s a key difference in their level of sensitivity and the potential consequences of exposure.

• Confidential Data: This is generally non-public information, but not always inherently risky. It can include things like business plans or credit card numbers.
• Sensitive Data: This is highly confidential information that could cause significant harm if leaked. Examples include health data, financial information, and biometric data.

Sensitive data requires a higher level of protection due to the potential for severe consequences like identity theft or financial loss if exposed. Stringent regulations often govern sensitive data, and its misuse can result in serious legal repercussions.

Remember:

Both confidential and sensitive data are important to protect. By understanding the differences and implementing appropriate safeguards, organizations can minimize the risk of data breaches and ensure the security of valuable information.