Protecting Personal Data in the Digital Age: The Role of PETs and Data Classification

Share on

Ravi Lingarkar
Chief Product & Engineering Officer

The safeguarding of sensitive data from unauthorized access, theft, and breaches is paramount to organizations in today’s digital age. Privacy regulations, such as General Data Protection Regulation (GDPR) and California Privacy Rights Act (CPRA), mandate that organizations take appropriate measures to protect personal data. Failure to comply can result in significant penalties and reputational damage. Organizations can leverage privacy enhancing technologies (PETs) and data classification tools to comply with these regulations and protect sensitive data.

PETs are technologies and techniques that reduce or minimize the amount of personal data shared or disclosed, enabling individuals to control their personal information and reduce the risk of data breaches or misuse. PETs are essential in various settings, including healthcare, finance, and e-commerce.

Here are some important examples of how PETs can be used to address data security and privacy concerns:

  1. Anonymization: PETs can protect privacy by removing identifying information from personal data, making it difficult to trace back to the individual. This can be useful when organizations need to share data for research purposes but must protect individuals’ privacy.
  2. Differential privacy: This technique adds noise to data to protect individuals’ privacy. It enables organizations to collect and analyze data without revealing individuals’ identities or sensitive information.
  3. Homomorphic encryption: This technique allows data to be encrypted while still usable. It is helpful when sensitive data must be stored in the cloud or shared with third parties.
  4. Privacy-preserving data sharing: PETs can enable secure data sharing without compromising individuals’ privacy. This is useful in healthcare settings where medical data must be shared between healthcare providers and protected to comply with privacy regulations.

Another important use case for PETs is in financial institutions. These organizations handle large amounts of sensitive data, including personal and financial information such as social security numbers, bank account numbers, and card numbers. PETs such as encryption and tokenization can help to protect this data, preventing it from falling into the wrong hands.

Data classification is the process of categorizing data based on its sensitivity or importance, enabling organizations to identify and protect sensitive data more effectively, reducing the risk of data breaches or misuse. Data classification is crucial for compliance with data protection regulations such as GDPR and CPRA.

Here are some important examples of how data classification tools can be used to address data security and privacy concerns:

  1. Identifying sensitive data: Data classification tools can be used to identify and ensure it is protected accordingly. This can be helpful when organizations handle large volumes of data and must prioritize their protection efforts.
  2. Access control: Data classification tools can enable access control based on data sensitivity, ensuring that only authorized personnel can access sensitive data and reducing the risk of data breaches.
  3. Data retention: Data classification tools can be used to develop a data retention policy that ensures data is kept only for as long as necessary, reducing the risk of data breaches caused by storing data for longer than required.
  4. Incident response: Data discovery & classification tools can be integrated to develop an incident response plan that outlines the steps to take in the event of a data breach. This enables organizations to respond quickly and effectively to minimize the impact of a breach and identify whose data are breached and notify them.

In conclusion, PETs and data classification tools are essential for addressing data security and privacy concerns. These tools enable organizations to protect personal data, comply with data protection regulations, and reduce the risk of data breaches or misuse. By using PETs and data classification together, organizations can take a proactive approach to data protection and privacy, which can lead to improved customer trust, reputation, and business success.

As the importance of data privacy and security continues to grow, we can expect to see more innovative PETs and data classification tools to address new challenges.


Protecting Personal Data in the Digital Age: The Role of PETs and Data Classification


In an increasingly data-driven world, organizations must harness the power of their data while maintaining robust information security. Data discovery, a crucial aspect of data management, can help organizations uncover valuable insights while simultaneously ensuring data protection and compliance. This blog post will discuss why organizations should consider data discovery from an information security perspective and provide a comprehensive approach to successfully implement it.

Data Discovery as a key tool for Information Security

Data discovery is a critical component of an organization’s information security strategy. It involves the identification and classification of all data within an organization’s infrastructure, including sensitive data, and is essential for developing effective security controls. Without data discovery, an organization may not be aware of all the data it possesses, where it is located, or who has access to it, leaving them vulnerable to data breaches and other cyber threats. Data discovery enables an organization to gain greater visibility into its data, implement appropriate security measures, comply with data privacy regulations and maintain the trust of their customers. From an information security standpoint, there are several reasons why organizations should consider data discovery:

1. Identify sensitive data:

Data discovery allows organizations to locate and classify sensitive data, such as personally identifiable information (PII), intellectual property, or financial data. Understanding where sensitive data resides is essential for implementing appropriate security measures and complying with data protection regulations like GDPR and CCPA.

2. Enhance access control:

By understanding the nature and location of critical data, organizations can establish role-based access controls and implement the principle of least privilege. Data discovery helps ensure that only authorized personnel have access to sensitive information, reducing the risk of unauthorized access and data breaches.

3. Monitor and audit data usage:

Data discovery tools can help organizations monitor and audit data usage, ensuring compliance with regulatory requirements and internal policies. By tracking data access and usage patterns, organizations can identify anomalies, such as unauthorized access or data exfiltration, and take prompt action to prevent potential breaches.

4. Improve data governance

Implementing data discovery allows organizations to establish a strong data governance framework. This framework includes policies, procedures, and controls to manage data usage, ensure data quality, and maintain data security throughout its lifecycle. Besides, data discovery can help organizations streamline their data management processes by eliminating unnecessary data and reducing the risk of data duplication or inconsistency.

Data Discovery for a strategic approach to Information Security

1. Develop a clear data discovery strategy:

Before embarking on data discovery, organizations should develop a clear strategy that outlines the goals, scope, and objectives of the initiative. This strategy should prioritize data security and compliance, ensuring that data discovery efforts align with the organization’s overall information security program.

2. Choose the right tools:

Select data discovery tools that cater to your organization’s specific needs and are capable of addressing information security concerns. Look for tools with features such as data classification, data lineage, and access control capabilities to enhance your data security posture.

3. Create a cross-functional team:

Establish a cross-functional team with members from data management, information security, and relevant business units. This collaborative approach ensures that data discovery efforts take into account diverse perspectives and maintain a strong focus on information security.

4. Implement data classification and labeling:

Use data discovery tools to classify and label sensitive data according to predefined categories and risk levels. This information can then be used to implement appropriate security controls and data handling procedures.

5. Monitor and audit regularly:

Continuously monitor and audit data access, usage, and security controls to ensure ongoing compliance with regulatory requirements and internal policies. Regularly review and update your data discovery strategy to address evolving security threats and business needs.


Data discovery offers a wealth of benefits, from uncovering hidden insights to driving innovation. However, organizations must also prioritize information security in their data discovery efforts. By developing a clear strategy, choosing the right tools, and fostering collaboration, organizations can unlock the full potential of their data while maintaining a robust information security posture. Results from the data discovery process should help organizations address their information vulnerabilities with thorough details, customized reports, data categorization, and risk assessments that can be used to design improvements and remediation action plans.

Exclusive Insights

SISA’s Latest
close slider