Key steps to implementing an effective data privacy framework

The collection of customer data means complying with data privacy and protection standards.

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These days, it has become virtually impossible for organisations to do business without collecting sensitive information about their customers. This is especially true in certain industries, such as healthcare, financial services or education, and it becomes even more of an issue as the business grows and expands its digital footprint.

For enterprises, there is a lot at stake, as the collection of customer data means complying with data privacy and protection standards, many of which are codified as law. Failing to comply with these regulations increases the risk of data being stolen or hacked, which can cause untold reputational damage to any business, resulting in a loss of customer loyalty and trust. Businesses can also face extreme financial losses, legal fines and courtroom battles over their failure to adequately safeguard their customers’ data.

Protecting against data loss has become even more important for organisations with the emergence of data privacy laws such as Europe’s GDPR and California’s CPRA, which stipulate that enterprises must not only implement reactive security measures, but also be proactive. These frameworks are binding, mind you, not only if your business is located in the relevant jurisdictions, but also if that’s where any of your website visitors are.

To comply, businesses must implement a data privacy management framework that’s comprehensive, strategic, scalable and agile. With such a framework in place, the task of protecting customer data becomes much more manageable.

Here are the steps you need to take in order to implement a data privacy management framework that works well for the specifics of your organisation.

The data discovery process is an essential first step that informs organisations about exactly what kind of data they have stored on their servers and how sensitive it is.

The process involves cataloguing the exact types of information the company is collecting, identifying its level of sensitivity and mapping out where and how that information is stored. In addition, enterprises must be sure they know who has access to this data, and the reason why it’s being collected.

When completing this first step towards developing a robust data privacy framework, it may become apparent that certain kinds of sensitive information are being stored for longer than necessary. Organisations may also discover the need for different levels of secure storage, based on how sensitive the data is.

Once the data discovery process is complete, the next step to safeguard that information is to classify it. By creating various classification designations and labelling each piece of data, it becomes far easier for team members within the organisation to follow data security protocols.

As an example, organisations might restrict access to data that’s labelled as “sensitive” or “confidential” to only those employees who absolutely need to be able to access it. Classifying data also makes that information easier to locate whenever it’s needed, and it eases headaches for security teams, who might have a need to understand the nature of the data they’re supposed to be protecting.

The goal of data classification is to ensure that internal teams can build up a clear picture of the company’s data landscape. When security teams understand exactly what data they’re dealing with, where it’s stored and who can access it, it becomes much easier for them to take a proactive stance and protect that information from possible threats.

The third step involves creating data privacy policies that help to define the organisation's objectives and protocols with regard to that information. As part of this process, there’s also a need to create a strategy for risk management, which means assigning clear roles and responsibilities for different team members. For instance, who will be tasked with the day-to-day management and protection of said data, and who is responsible for investigating and stopping a data breach if one does occur?

Data policy roles and responsibilities should be consistent with organisational standards and distributed across team members based on their level of authority, expertise and accountability. At the same time, there’s a need for these roles and responsibilities to encourage communication, collaboration and co-ordination between different individuals and functions.

Examples of data policy roles and responsibilities include data protection officers, data owners, data stewards, a data privacy governance committee, data users and data subjects.

Once the data has been discovered, classified and policies have been created, organisations are ready to begin the next important step, which is building greater awareness of the data privacy framework and educating employees on best practices for data privacy.

One of the risks of managing sensitive data is that it has a way of spreading to unlikely places within an organisation, and it can do so very quickly. Anyone in your organisation who logs into a third-party platform to review information has the potential to leak their credentials. For these reasons, matters of data privacy should never be the sole responsibility of IT security teams.

Indeed, for data privacy to work, it must be a team endeavour that becomes ingrained within the company’s culture itself. What this means is that, irrespective of an employee’s role within the organisation, they should be familiar with the basic requirements and responsibilities they have when working with sensitive information. By thoroughly educating users to uphold certain standards, organisations can build a stronger, privacy-focused mindset that helps to reduce risk.

By carrying out real-time monitoring of sensitive data, along with regular reviews into how that data is being accessed, used and managed, companies can ensure they are proactive in complying with local data privacy regulations and requirements.

One of the best ways to do this is to automate the monitoring process at multiple endpoints, such as in on-premises systems, cloud and edge environments.

In addition, it’s often a good idea to maintain detailed logs of who accessed what repositories when, so that you can retrace your steps in the case of a breach.

By adopting a data privacy framework, organisations will ensure that they’re future-proofed against whatever threats emerge. The beauty of data privacy frameworks is that they’re not meant to be static. They’re designed to be flexible and act as a guide, based on certain core principles informed by the data privacy laws that must be adhered to. As such, the organisation will be ready to change and adapt its practices should those regulations evolve, or if new laws are implemented.

Organisations that have a strong data privacy framework in place are better positioned to anticipate and adapt to future regulatory changes. This enables organisations to take a more proactive stance and ensure they’re always one step ahead of malicious actors.