Abraham Lincoln once said, “If I had eight hours to chop down a tree, I'd spend the first six of them sharpening my ax.” So what has that to do with automated data compliance?
$575 Million (and potentially up to $700) in fines and compensations! That is what Equifax Inc. has agreed to pay as part of a global settlement after failing to take reasonable steps to secure its network, leading to the 2017 data breach. In 2019, Capital One bank had to pay $80 million in fines due to security compliance failure. These are not rare occurrences; actually, a 2021 IBM report concluded that the cost of a data breach incident had hit a record high of $4.24 million per incident.
Maintaining strong, up-to-date security compliance comprises countless routine, repetitive, time-consuming tasks. We as humans are poorly suited for that, handling dozens if not hundreds of daily/monthly/yearly installations, updates, testing, monitoring, and checking of every system component.
Therefore, Abraham Lincoln's ax sharpening is a good analogy for automated data compliance. It automatically makes sure you follow all compliance issues, thus excluding the human error factor, but it also helps save your resources in terms of time and money.
This blog post will uncover the best practices for automated data compliance and how to implement them using the best tools available.
Automated data compliance: the basics
Before diving into the subject, let's discuss some of the basics.
What is automated data compliance?
The term data compliance refers to security standards, rules, and regulations designed to make sure businesses use the best possible applications, practices, and protocols to protect sensitive data. Data compliance (i.e., following these standards, rules and, regulations) is not voluntary but mandatory. For example, one is not allowed to process debit or credit cards without complying with DSS PCI - Payment Card Industry Data Security Standard.
In the past, data compliance was done manually, including regularly updating antivirus apps, installing security patches, ensuring proper security configurations, frequent vulnerabilities testing, and constantly auditing and enforcing the adherence to all security stipulations.
Therefore, automated data compliance is the use of technological tools to remove this manual work and automate the data compliance process.
How automation strengthens your compliance
Whether you are a CISCO, DevSecOps, Infosec, or Compliance Officer, automation can dramatically strengthen your enterprise compliance while having many other added values:
- Reduce human error: security work is extremely prone to errors. The Gartner Inc 2019 report states that: “Nearly all successful attacks on cloud services are the result of customer misconfiguration, mismanagement, and mistakes.” Data compliance is no different. By removing the human factor, data compliance automation can dramatically reduce errors within all compliance processes.
- Improve the handling of multiple, constantly changing compliance regulations: there are many data security standards, rules, and regulations. As the complexity of modern cloud infrastructure increases, so do the threats and vulnerabilities. In turn, regulations keep evolving to make sure these threats are mitigated (at an ever-growing rate). One of the largest challenges of data compliance is to keep up with these frequent changes in many different sets of standards concerning new and old technologies. Automation is crucial as it allows you to constantly follow compliance updates whenever they occur and immediately apply them all across your system, ensuring it is always compliant.
- Facilitate the use of advanced cloud technology: going into the cloud and adopting approaches like microservices gives you countless advantages and increases your capabilities. However, as your system becomes more flexible and decentralized, it becomes more difficult (sometimes impossible) to follow all your data while ensuring security compliance. In many cases, this task has become so extensive that automation is not only an option but a must.
- Boost compliance/security teams' efficiency: manual compliance can be very time-consuming. Detecting and fixing every issue can be exhausting. Automation dramatically reduces this workload, allowing teams to do the same work faster, more effectively, and better their work experience.
- Optimize resource utilization: automation dramatically reduces workload and allows you to transfer resources to where they are most needed. This is another good example where compliance automation not only strengthens your data compliance but actually provides added value.
- Enhance reports’ accuracy: compliance means regular audit reports. As discussed above, these reports can be error-prone when done manually. Automation can help produce better and more accurate reports (not to mention that it makes the work of the compliance officer much easier, as he is able to extract all relevant, up-to-date data quickly and easily).
- Transparency: one of the major advantages of automated data compliance is complete and absolute transparency. It derives from allowing immediate clear access to all updated compliance data by all authorized personnel (particularly beneficial in large multinational enterprises). This has another benefit, as it allows for smooth interfacing with new vendors or customer systems in terms of data compliance.
5 best practices for automated data compliance
Now that we have discussed the meaning of automated data compliance and its importance, let's talk about best practices for implementation.
1. Automate data flow mapping
You can't protect data if you don’t know where it is, can’t see it, or don’t even know it exists. Accordingly, the first crucial step in protecting data is mapping its flow: where is it created? Where is it stored? And how does it flow? The same goes for data compliance. Mapping your data flow is crucial for ensuring compliance.
Automating data flow mapping means using software to review your system’s data automatically. This review includes constantly logging the data type, where it was found, where it is flowing to, and where it is stored. Once all the information is gathered, it can be labeled and classified.
In today's world, we have become more dependent on decentralized cloud technology. Huge amounts of data are constantly created knowingly or unknowingly (for example, shadow data) all over your system (sometimes literally, all over the world). In this situation, manual tools are no longer relevant, meaning that automation has become the only option.
