Do you honestly think that the rules of traffic were the same in the early 1900s? Of course not! So why did they change?
There were plenty of reasons.
More people started driving; the cars got faster, and the types of vehicles participating in traffic became greater. The old rules were no longer good enough.
The same applies to data security.
Why is innovation so dangerous for data security and compliance? Well, the most obvious answer to this question would be that compliance has to be updated with every change.
Every time an analytical tool improves, there’s a potential that a whole new type of data is now useful and needs to be collected. There are more industries out there than ever. Also, the rules change with the changing landscape.
So here are reasons to ensure data security compliance in this age of innovation.
1. The evolution of compliance standards
The initial efforts to protect data predate the age of the internet (at least its massive use). The Privacy Act was passed in the US in 1974, while the Data Protection Act was passed in the UK in 1984.
These regulations were primarily focused on protecting personal information stored in computer systems that were still not on the network. The main concern was the misuse of data.
Then, in the 1990s, there was a new challenge. Companies were finally held accountable and had to explain their information-sharing practices and safeguard sensitive data.
The first major breaches didn’t start until the early 2000s and some of the biggest regulations in history started as a response to these breaches. Seeing as how these data breaches happened to some of the biggest names in the industry (like Yahoo and Target), which already had some forms of incident response plan and cyber protection, this was a clear and strong message. It showed everyone that just having protection isn’t enough. The protection had to be adequate, as well.
The next big step came in the form of comprehensive data protection formats (like GDPR). These compliances still remain in power, and they’re undergoing revisions and updates on a regular basis. This is where the principle of data minimization, purpose limitation, and actual accountability were introduced.
After this, we have come to the past few years with sector-specific data security standards (like for health data safety and payment card safety), as well as the data concerning the latest technology. Today, we live in an AI era, as well as the era of the IoT (Internet of Things), which comes along with its own set of unique challenges.
2. Automation of compliance management
Compliance management is the key to survival in the business world and to doing things legally right. This is why it’s so important to approach it from the right perspective, and automation is the way to go.
First of all, with a tool like a GDPR-compliant privacy policy generator, you drastically minimize the chance of human error. Seeing how these policy generators receive regular updates, if there’s an update in regulation, it will be automatically applied to all future privacy policies.
Most importantly, compliance management is a repetitive administrative process that will take a lot of time and energy. At the same time, it’s not a creative process, and in the age of AI-powered automation, there’s not a single reason why a human should do it.
Doing this via a tool simplifies the process, ensures uniformity, and limits the amount of human error. It also reduces the need for this to be done by humans (whom you’re paying by the hour). So, having them on these tasks that an automation tool can handle seems like a massive waste of money and potential.
Not only that, but repetitive administrative tasks have a negative impact on your team’s ability to focus, as well as their morale. Thus, automation can also be seen as a massive boost to your overall organization.
The biggest perk of automated compliance management is its scalability. Even if you could handle these things partially manually (which is almost never the case in 2024), what would happen when the number of these requests reaches a certain critical number? With automation, this won’t really be an issue.
3. New industries
Thanks to the IoT, more and more products are connected to the network and are actively collecting data. In the past, this was not something that you had to think about. After all, once you bought and paid for the product, the manufacturer no longer had any business inquiring about it or wondering what they were using it for.
Today, everything you do, to a certain degree, sends data into the network.
For instance, if you’re driving an electric car, it’s not just about the GPS in your car tracking your location. It’s also about your battery intelligence software collecting data about the battery use, condition, and efficiency.
For a lot of people, this is a reason for concern, potentially even a slight invasion of privacy, but it doesn’t have to be seen this way. First of all, keep in mind that this industry is heavily regulated, and the network has to remain compliant with all the latest laws and regulations.
Second, it’s important to understand that this is a growing industry but also a relatively new one. People have been driving gas cars for centuries, but they’ve only just started investing in electric vehicles en masse. Companies manufacturing these batteries and improving battery software need insight into how these software are used in practice. Without it, it just won’t work.
The bottom line is that this data needs to be collected in agreement with legally prescribed practices, and parties collecting this data need to be transparent about what and why they’re collecting.
4. The impact of AI
With the help of AI technology, the capacity of cybersecurity has drastically risen. Now, AI analytical tools have a far higher capacity to spot problems early, even in the stage where they would just appear as unusual patterns and anomalies.
You have to understand that regulations are also determined by what is possible. A government agency or a regulatory body cannot just randomly demand a safety measure that they know that organizations cannot deliver on. With the help of AI, what’s possible has drastically expanded, and this will soon have a profound impact on AI.
The speed of an automated incident response will also be a lot higher.
An AI has the potential to automate the response to detected threats. This improvement in data protection mechanisms definitely has to be taken into account when the future policy is written, and the current policy is rewritten.
The biggest concern here remains algorithmic accountability. For this to work, these AI algorithms need to be transparent and fair.
Cloud computing and data security compliance
We started this article (in the first section) by talking about how the first regulation was about data stored on devices. Today, almost all data is in the cloud, and cloud adoption rates are skyrocketing. Product like Microsoft Office and Windows are today fully in the cloud through Microsoft 365 and Windows 365. This means that potential data breaches of these cloud servers have the potential to cause a huge disaster.
Now, one of the biggest problems with cloud security is the jurisdiction. With physical devices, it was fairly easy - the location of the device could be used to determine the jurisdiction. With the cloud, the logical solution would be to focus on the server, but this is not the best way to do so. Cloud data is often stored across multiple geographic locations, which naturally raises these concerns.
The matter of cloud security, therefore, has to be taken very seriously.
Another thing worth mentioning is the importance of access controls and identity management. With traditional storage units, you had to be in actual possession of the drive in order to access data. This is no longer the case with cloud storage. This is why confirming your identity through various means has to be taken very seriously.
Modern data security regulations have to work extra hard to keep up with innovation
At the end of the day, technology is developing at an incredible pace, and it revolves around data collection and analysis. This is why businesses need more data than ever before, and data-collection tools are more sophisticated than ever before.
Thanks to modern AI analytical tools, even seemingly unimportant data can be used to get valuable insight. Moreover, new industries are rising, and they’re changing the playing field even further.
Because of all this, the rate at which these compliances have to evolve and the rate at which enterprises have to adapt to these compliances need to be drastically improved.