There are two types of businesses in the world today; those that run on data and those that will run on data. As a result, data security now sits at the top of nearly every organisation's priority list. But with such a high volume coming into most businesses every day, how can InfoSec professionals quickly identify which is the highest priority for protection? After all, security costs time and money, and not all types of data are as sensitive or vulnerable as others.
It's for this very reason that data discovery and classification techniques are making a significant resurgence. Indeed, global analyst houses Forrester and Gartner both claim data classification is now foundational to an effective data security strategy.
What is data classification?
Data classification is a process of consistently categorising data based on specific and pre-defined criteria so that it can be efficiently and effectively protected. In addition to simplifying security strategies, it can greatly assist companies in meeting governance, compliance or regulation mandates such as GDPR and PCI DSS, as well as protecting important intellectual property.
How can businesses implement an effective classification strategy?
Data classification is not a one size fits all approach. Every business has different needs to address, so a strategy must be tailored accordingly. However, the following five-point action plan can be used to create the foundation of an effective strategy for nearly any business:
1) Define a data classification policy
What are the goals, objectives and strategic intent? Make sure users are aware and understand why it's being put in place. An effective data policy must also balance the confidentiality and privacy of employees/users against the integrity and availability of the data being protected. A policy that's too stringent can alienate staff and impede their ability to carry out their jobs, but if it's too lax, the very data the business is trying to protect could be at risk.
2) Establish the scope
It's important to establish where the boundaries will be early on; otherwise it can quickly grow out of control. This is particularly important when considering partners and third parties. How far into their network, will/can/do you want to reach? Equally important is legacy and archived data. Where is it and how will it be protected? Finally, make sure to note anything that's out of scope and ensure this is evaluated and adjusted regularly.
3) ‘Discover' all sensitive data defined in the scope
Once data policy and scope have been established, the next task is to identify all the sensitive data that requires classification and protection within the business. Firstly, understand what data it is you are looking for. This could take many forms, ranging from personally identifiable information, payment card numbers and healthcare records through to business IP, source code, proprietary formulas etc. Next, focus on where this data is likely to be found, from endpoints and servers, to on-site databases and in the cloud. Remember that discovery is not a one time event, it should be continuously re-evaluated, taking into account data at rest, data in motion and data in use across all business platforms.
4) Evaluate appropriate solutions
When the time comes to identify an appropriate data classification solution, there are plenty to choose from. Many of the best solutions today are automated and classification can be context (file type, location etc) and/or content based (fingerprint, RegEx etc). This option can be expensive and require a high degree of fine tuning, but once up and running it is extremely fast and classification can be repeated as often as desired. An alternative to automated solutions is a manual approach, which allows users themselves to choose the classification of a file. This approach relies on a data expert to lead the classification process and can be time intensive, but in businesses where the classification process is intricate and/or subjective, a manual approach can often be preferable.
A final option is to outsource the classification process to a service provider or consulting firm. This approach is rarely the most efficient or cost effective, but can provide a one-time classification of data and give any business a good idea of where it stands in terms of compliance and risk.
5) Ensure feedback mechanisms are in place
The final stage is to ensure there are effective feedback mechanisms in place that allow swift reporting both up and down the business hierarchy. As part of this, data flow should be analysed regularly to ensure classified data isn't moving in unauthorised ways or resting in places it shouldn't be. Any issues or discrepancies should be immediately flagged for follow up.
With data now playing a pivotal role in nearly every business around the world, the ability to track, classify and protect it is no longer a luxury. An effective data classification strategy should form the corner stone of any modern security initiative, allowing businesses to quickly identify the data most valuable to them and ensure it is safe at all times.
Contributed by Thomas Fischer, threat researcher and global security advocate, Digital Guardian