5 tips for enterprise data privacy and security

Data privacy and integrity work together to create a strong security plan that protects sensitive information, keeping it confidential and reliable. Prioritizing these strategies builds trust, ensures compliance with rules, and reduces risks like data breaches or unauthorized changes.

According to Cisco’s 2022 Consumer Privacy Survey, individuals are more likely to trust organizations that are transparent about how they will use their personally identifiable information (PII).

It's why data privacy and integrity have become important topics of conversation, and why organizations have started putting in place regulations around data security.

How to take control of your data security and privacy

In the past few years, data privacy has become a top priority for companies worldwide. Our 2024 State of Physical Security Report shows that 31% of physical security professionals are actively adopting privacy protection methods to counter cybersecurity threats.

 

 

Here are some key strategies to protect your data, ensuring its integrity, confidentiality, and availability.

  Collect and store only what you need

The first rule for an enterprise's data security is to collect and store only the information you absolutely need. By reducing the volume of data you store, you minimize the potential impact in case of a security breach. Regularly review and audit the data you possess, disposing of any unnecessary information responsibly.

  Limit access to sensitive data

To enhance data security, you need to limit access to sensitive information. Follow data-sharing best practices, such as anonymizing the data to safeguard individual privacy. This involves removing personally identifiable information to add an extra layer of protection.

These techniques, including randomization, pseudonymization, tokenization, generalization, and data masking, are crucial for de-identification—a process that plays a vital role in anonymizing personal information while retaining its use.

• Randomization adds noise to numerical values, making re-identification challenging. This can be done by adding random noise to numerical values such as an individual’s age or income.
• Pseudonymization replaces identifying details with pseudonyms or codes. For example, you can replace names with unique identifiers.
• Tokenization involves replacing sensitive data with unique tokens, enhancing security. For example, you can replace credit card numbers with tokens that have no direct correlation to the original numbers.
• Generalization groups specific data into broader categories, preserving privacy. This can be done by converting exact birthdates to age ranges.
• Data masking obscures parts of the data, ensuring sensitive information remains protected. An example of this would be to show only the first few digits of a phone number or an email address.

  Be transparent and get user consent

Building trust through transparency and user consent is essential. Clearly communicate with users, providing information about the data collection process—what data is collected, why it's collected, and how it will be used.

This transparency not only builds trust but also allows users to make informed decisions about their data. Prioritize obtaining explicit consent from individuals before collecting and processing their data, ensuring a clear outline of the scope of data usage. This approach promotes transparency and respects user privacy preferences.

  Choose a reliable data storage provider

Who is storing your data is as important as how they are storing it. During your vendor search, make sure you understand how long they keep data and update storage to fit your needs. If you use third-party services, check that they have strong security measures and reliable ways of handling data.

When dealing with an entire enterprise’s data security, comprehensive strategies must be put in place so there aren’t any gaps in the data collection and storage process. Using encryption, educating employees about cybersecurity, and keeping up with data privacy laws are all starting points to create a strong data protection strategy, especially for big data across an organization.

  Establish strong policies

One effective approach to protecting your data over time is to implement strong policies across your organization. This can be done by enforcing a transparent chain of custody.

Creating a clear chain of custody ensures accountability and traceability at every stage of the data lifecycle. This involves documenting how data moves within your organization, covering creation, modification, and transmission.

By implementing tools such as a digital evidence management system (DEMS), you can safeguard the chain of custody by providing a secure platform for managing digital evidence. It ensures the reliability and traceability of evidence throughout its lifecycle. Using features like version control and audit trails, the system tracks the creation, modification, and sharing of digital evidence. This approach helps maintain a clear and secure chain of custody, essential for legal processes and upholding the credibility of digital evidence in investigations.

 

 

Remember, data security is an ongoing process. Regularly update your protocols, stay informed about emerging threats, and continuously educate your team on best practices. In doing so, you’ll build a strong organization-wide standard for big data security and contribute to a safer digital landscape for everyone.

To help you stay on top of data privacy compliance, we’ve put together a short checklist you can share with your colleagues.