On March 29, 2024, the California Privacy Protection Agency (CPPA) can begin enforcing regulations under the California Privacy Rights Act (CPRA), ushering in a new era of data privacy requirements for businesses.

The CPRA represents a milestone in the evolution of data privacy, raising the bar for consumer data protection. Enacted to strengthen the California Consumer Privacy Act (CCPA), the CPRA introduces enhanced privacy rights and imposes additional obligations on businesses that collect data from California residents.

From understanding the intricacies of data flow to fortifying security measures and fostering a culture of privacy, organizations can prepare by leveraging effective data management strategies. Three strategies, in particular, will set businesses up for success: data mapping, data classification, and data minimization.

But first, let’s look at what the CPRA requires.

An overview of the CPRA

The CPRA builds upon the California Consumer Privacy Act (CCPA), enhancing privacy rights and protections for California residents.

In essence, the law grants consumers these key rights:

• know what personal and sensitive information businesses collect and why,
• prohibit the sale of their personal information, and
• hold businesses responsible for the security of their collected data.

Businesses must also notify consumers of how long they plan to retain consumers’ personal information. Additional rights apply to “sensitive personal information,” which includes Social Security and driver’s license numbers, credit card numbers, access codes and passwords, precise geolocations, and demographic details such as racial or ethnic origin, religious and political beliefs, genetic data, and more.

The CPRA also gives consumers the right to correct erroneous personal data, opt out of disclosures of their personal data for advertising, and restrict the use of their sensitive personal information. 

The CPRA applies to businesses that operate for profit and do business in California that meet these criteria:

• have a gross annual revenue of more than $25 million in the preceding calendar year;
• buy, receive, or sell the personal information of 100,000 or more California residents, households, or devices; or
• earn at least 50% of their annual revenue by selling or sharing California residents’ personal information.

To comply with the CPRA, businesses must notify consumers of their rights, including amending their privacy policies and opt-out provisions, limit the data they collect, and ensure that any data they collect is collected for a valid business purpose.

Penalties for noncompliance with the CPRA

Noncompliance can result in significant financial repercussions, including fines for violations. For unintentional violations, businesses may face fines of up to $2,500 per violation. However, if the violation is deemed intentional, the fine can increase to $7,500 per violation. Importantly, each instance of noncompliance is considered a separate violation, which can accumulate to substantial sums, especially for large-scale data breaches or ongoing noncompliance.

Moreover, businesses found in violation of the CPRA may also face legal action from consumers. Under the CPRA, consumers have the right to initiate civil lawsuits against companies for unauthorized access, theft, or disclosure of their personal and sensitive information, particularly if it is nonencrypted or nonredacted. 

Let’s look at how data management can aid in CPRA compliance.

Three proactive data management strategies that boost CPRA compliance

As your organization prepares for regulatory enforcement of the CPRA, look for ways to reduce the amount of data you have that is subject to the law. These three data management strategies may help.

1. Data mapping

Before businesses can effectively comply with CPRA and implement targeted privacy measures, they must understand their data landscape. Data mapping sets the stage by helping organizations understand what information they collect, how and where they store it, and why and how they process it.

Consider these steps to create a data map for your business:

• Identify data sources: List all data sources in your organization, including databases, applications, file systems, cloud services, and third-party vendors.
• Document data elements: For each data source, create a comprehensive list of its data elements, such as the data’s structure, format, and metadata.
• Record the data lifecycle: Document the lifecycle of each data element from its creation through usage and storage to deletion or archival.
• Map data flows: Trace the flow of data as it moves between systems and processes, recording how data is collected, processed, stored, and transmitted. Document any transformations or modifications to data during its lifecycle, such as cleansing, aggregation, or conversion. Understand how data in one system or process relates to data in another.
• Designate data owners and stakeholders: Assign responsibility for different sets of data to specific individuals or departments.
• Consider applicable legal and regulatory requirements: Take into account any regulatory requirements governing your data, such as the CPRA or any industry-specific regulations.
• Assess data privacy and security measures: Understand what data security and protection measures are in place for each data source, such as encryption and access controls.
• Update your data maps regularly: Data maps are living documents. Regularly update your data map to reflect changes in systems, processes, or data types.

Mapping data is only the first step. To understand what data must be protected, organizations must also classify their data.

2. Data classification

Data classification is a crucial process that involves evaluating and categorizing data based on its sensitivity, value, and required level of protection. With proper data classification, organizations can efficiently implement targeted security measures required for regulatory compliance.

Here are the basic steps organizations should follow to set up a data classification system:

• Identify applicable laws and regulations: Privacy laws (including the CPRA, the General Data Protection Regulation (GDPR), and others), industry-specific regulations, and internal policies may dictate how businesses should handle different data types. These laws and regulations establish the framework for classification.
• Define clear data classification criteria and categories: What criteria are relevant for your organization? Consider using criteria such as sensitivity, confidentiality, criticality to the organization, legal requirements, and the potential impact of unauthorized disclosure to sort data into categories. Common categories may include these:
  • public information, which can be shared openly;
  • internal information, which is restricted to employees; and
  • confidential information, which is highly sensitive data with limited access.

• Classify or sort the data: Once you’ve established your criteria and categories, use a unique identifier like a metadata tag or label to mark data according to its category. Technology can automate this process, leveraging machine learning, natural language processing, and optical character recognition (OCR) to classify data based on predefined rules.
• Establish access controls: Limit access to sensitive data to only those who need it for their roles. Use encryption, two-factor authentication, and other security measures to protect classified information.
• Audit data access: Audit the effectiveness of your classification and access control measures to ensure that they are working as expected.
• Monitor data access: Set up a system to detect and report any attempt to access sensitive data.
• Review and update your data classification framework: As your organization and the broader privacy landscape evolve, more data may require protection. Conduct regular risk assessments to identify new regulatory requirements as well as new threats and vulnerabilities, and adjust your approach accordingly.

Properly classifying data enables businesses to fine-tune their data management strategies, striking the right balance between operational efficiency and privacy protection.

3. Data minimization

By limiting the amount of personal data it holds, an organization can reduce its privacy risks and prevent unnecessary exposure in the event of a data breach. Data minimization is a subset of information governance, which encompasses the policies, processes, and technologies that organizations use to manage information throughout its lifecycle. As it relates to the CPRA, data minimization limits the collection and processing of personal information to what is strictly necessary to achieve the stated purpose for that data’s collection.

Once you’ve mapped and classified your organization’s data, follow these steps to strategically minimize data:

• Establish a data minimization team: Form a dedicated team to oversee the data minimization policy, including representatives from the legal, compliance, IT, and privacy functions.
• Define data minimization criteria: Establish clear criteria for determining what constitutes necessary and proportionate collection and processing of personal information and other sensitive data. Consider factors such as the purpose of data collection and processing, the relevance of specific data elements, and the potential risks involved.
• Double-check user consents for data processing: Ensure that you have clearly communicated the purposes for which collected data will be used. Avoid requesting consent for overly broad or vague purposes.
• Establish data retention periods: Define and document clear data retention periods for each category of information outlined in your data classification strategy. Ensure that you retain information only as long as necessary to fulfill the purpose for which you collected it. Once the purpose is achieved, dispose of or anonymize the data appropriately. Periodically review and update these retention periods based on legal requirements and business needs.
• Leverage technology: Consider adopting technology to automate and enforce data minimization practices, including automatically applying data retention policies. Modern tools can also apply anonymization, pseudonymization, or data masking techniques to eliminate or reduce the visibility of personally identifiable information. Anonymization removes personally identifiable information, while pseudonymization replaces identifiable information with artificial identifiers.
• Create a culture of privacy by design: Emphasize the importance of data privacy by integrating data minimization principles into the initial design and default settings of systems and processes rather than adding these protections later.

Effective data management requires a holistic approach involving people, processes, and technology. By integrating these practices throughout the organization, you can establish a strong foundation for CPRA compliance and demonstrate your commitment to respecting consumer privacy.

Elevate your CPRA compliance with Onna

The CPRA represents a paradigm shift in data protection, demanding a proactive commitment to safeguarding consumer privacy. By building strong data mapping, data classification, and data minimization strategies into your overarching information governance framework, your organization can foster a culture of privacy, build consumer trust, and ensure sustained compliance with the CPRA.

Data management needn’t consume all of your team’s time, either, if you automate these processes with cutting-edge technology, including artificial intelligence and automation.

Get in touch to learn how Onna’s Data Management Platform can help you corral your data and easily comply with proliferating data privacy requirements.