Every aspect of our daily lives, from shopping to healthcare delivery to communication to banking, is moving online. This transition to digital means that consumers’ personal information is worth more than the Crown Jewels of the British Empire.

Think about this, though: 

The Crown Jewels are guarded by more than 38 ex-military personnel. These security professionals, known as Beefeaters, live onsite and must have at least 22 years of military service behind them. 

But since the collection is the largest collection of royal regalia in the world and is worth an estimated $4 billion, the UK government doesn’t leave its protection entirely on the shoulders of humans, who are a notoriously and historically unreliable provider of security.

Instead, they’ve augmented their people power with a vast array of automated cameras, climate control monitors, smoke detectors, motion sensors, bombproof glass, and alarms using the principle of redundancy.

Redundancy in security means that structural, organizational, and technological resources are combined into a series of checkpoints. If one precaution (say, the Beefeaters) fails, the remaining measures (i.e., monitors, cameras, sensors) can maintain protection.

When it comes to data privacy, employees are far more likely to introduce human error than the Beefeaters are to let St. Edward’s Coronation Crown be stolen. But thanks to data automation tools, businesses can reduce some of the heavy lifting when it comes to privacy compliance.  

How data privacy automation came to be

Ecommerce, social media platforms, and widespread digitalization have continued to grow exponentially since their initial boom in the early 2000s. 

It didn’t take much for companies to realize that data collected from online users could profoundly impact the success of their marketing efforts. And for a long time they were collecting every piece of information they could—from everyone they could—even when they didn’t have a clear use for it.

These free-for-all data collection practices led to significant encroachments on consumer privacy. Even worse, bloated databases became the targets of hackers, who were intent on making their fortune by selling stolen data. After a series of massive, high-profile data breaches in the early 2010s, consumer privacy advocates finally successfully lobbied for the passage of strict data privacy laws.

Privacy laws and workflows: A match not made in heaven

Beginning with the European Union’s enactment of the General Data Protection Regulation (GDPR), governments worldwide have proposed and passed legislation regulating how personal data is collected, used, and stored.

These laws have serious civil, even criminal, penalties for companies that don’t meet their statutory obligations, but building agile, robust processes that can handle the nuances of these laws is complicated.

The amount of data most companies own makes manual data processing without errors can be challenging, especially without detailed processes and practices in place. Automating consent management, notice delivery, data processing and storage obligations, and access privileges is like adding motion detectors and alarms to the Tower of London. 

Crown Jewels analogy notwithstanding, it’s important to note that data privacy is not the same thing as data security. Privacy and security functions often overlap, but while data security is focused on blocking attacks from the outside, data privacy centers on internal policies governing how sensitive data is collected, shared, used, and stored.

Creating functional practices in your privacy program

This subtle difference means data privacy programs need to include a nuanced combination of human and technical resources that can handle big data while balancing consumer values, legal obligations, and organizational goals.

Off-the-shelf privacy automation products are a good starting point in building a powerful privacy program. But because these products don’t understand specific, unique use cases, they won’t be fully functional without input from an experienced privacy team.

For example, most standard data privacy automation programs can find your data, but they can’t tell you how to understand it—and that’s an important part of the equation. Complying with each individual privacy law doesn’t always work without careful interpretation. For example, the “sale of data” has a specific definition and requirements under the California Consumer Privacy Act (CCPA), but under GDPR? It’s a different issue and needs to be handled as such.

In short: a dedicated privacy professional can’t single-handedly manage your data systems, but they can spot gaps in a platform’s capabilities. 

That’s why you need both.

Six benefits of data privacy automation

Hopefully, we’ve established why data privacy automation is crucial to businesses of the future. Now let’s talk about what automation can do for you.

Build a data inventory

Mapping your structured and unstructured data will tell you where your data resides. It will also help you understand why you’re collecting each piece of information, what data processors are doing with it, and what your users have consented to. 

Automating your data inventory will help you find old, extraneous, and at-risk data while simultaneously streamlining your collection.

Consent management

Automating your consent program can help ensure the right cookie and privacy notices are fired at the right time every time. Since user consent is the foundation of all privacy laws, getting this right is crucial.

Manage and document audits

Automating your audits makes the data easier to pull in the first place if it gets requested. This is important because if issues arise, companies need to be able to prove to their regulator that they have:

  • Met their obligations
  • Complied with requests promptly
  • Established continual improvement processes to ensure their program is collecting and using data correctly

Redact unnecessary data

With the amount of data being collected, it’s not totally unexpected that some unnecessary information will filter into your system. 

Additionally, many privacy laws require personally identifiable information to be redacted, encrypted, or anonymized to protect users against fraud or identity theft. Automation allows the redaction process to be continually occurring in the background without locking up data needed for daily operations.

Conduct Data Protection Impact Assessments (DPIAs) and Privacy Impact Assessments (PIAs) 

DPIAs and PIAs are similar risk assessment requirements that belong to different consumer privacy laws. Anytime you introduce a new technology or change a process that involves high-risk data (precise location, biometric data, Social Security numbers, etc.), a DPIA/PIA must be completed. 

Automation can serve to significantly streamline processes surrounding DPIA/PIA. For example, if you use a tool to run your data inventory, that same information can feed into your DPIA/PIA, saving time and reducing the risk of errors. 

Process Data Subject Access Requests (DSARs)

Both data privacy laws and data privacy best practices call for giving individuals more control over how their data is collected and used. 

Under various privacy laws, consumers can file a Data Subject Access Request to learn what information a company has about them, as well as delete or change that information. This is a time-consuming job that demands high-touch communication with customers, so automating at least part of the workflow will ensure both compliance and quality service. 

If you have a high volume of requests, you can also automate the process, such as downloading the information and integrating it with other systems. To see this in action, you can download your data on LinkedIn, Starbucks, or Spotify.  

Improve your productivity, performance, and perception

If you need help understanding your data management program, legal obligations, or options for automation, schedule a consultation with the experts at Red Clover Advisors today. We excel at providing practical solutions that go beyond compliance so that our clients become leaders in consumer privacy.