When most people think about data, they might imagine a mad dash of numbers swarming across the screen in a frenzy. Unfortunately, they don’t always think about what those ones and zeroes might really mean.
If you’re talking about protecting student data privacy, they mean a whole lot. But not all personal data is created equal – some are more sensitive than others. That’s why classifying data by risk is a pivotal part of data loss prevention. For example, consider the amount of data a single student is creating on a daily basis, compounded by every other student in each individual school. Your district is collecting the whole spectrum of sensitive data from test scores to addresses to social security numbers.
Data classification and data loss prevention (DLP) go hand-in-hand. To understand why you can’t effectively do the latter without the former, here’s a guide through the ins and outs of data classification.
What is data classification?
Imagine you’re putting away your groceries. Some fruits and vegetables might need to be protected in the refrigerator, others might need to be frozen, and a few are safe and sound on the kitchen counter. You wouldn’t put all your groceries in one place, just like you wouldn’t classify all your data the same way.
Data classification is essentially the same procedure. TechTarget defines data classification as the process of separating and organizing personal data into categories based on their shared characteristics. While certain types of data are low-risk, others might contain more sensitive information that needs to be tightly secured.
Why is classification important to data protection? Simply put, it’s what helps you allocate your data security resources most effectively. Think about it: If you don’t know your data, where it resides, or what it contains, how will you know where to focus your attention? That’s the type of insight data loss prevention depends on to easily locate, monitor, and apply the best protection.
How does classification work?
Data classification is all about sorting structured and unstructured data. But what’s the difference?
In short, structured data is quantitative. For your district, that likely means test scores, birth dates, Social Security numbers, credit card numbers, and other sensitive information that might be represented numerically. On the other hand, unstructured data is qualitative, such as personally identifiable information found in text and image content.
In either case, data discovery methods will locate the created data and classify it in three ways:
- High sensitivity: Confidential data such as financial records, intellectual property, personally identifiable information, and medical histories.
- Moderate sensitivity: Data that isn’t public but is used internally, such as academic records, class rosters, or emails and documents without confidential data.
- Low sensitivity: Public information that is easily accessible, such as web pages and blogs.
Bottom line: The greater the sensitivity, the bigger the risk to data security.
Data classification barriers, challenges, and consequences
Data classification and data loss prevention efforts are no simple task for many school districts. What should be a fast, simple, and automated process is often made more complicated than it needs to be.
There’s likely a number of barriers that challenge your ability to classify student data effectively and efficiently.
Let’s take a look at some challenges with which you might be familiar:
- Data classification can be a costly and difficult process
This is especially true for school districts still using manual classification methods. Classifying data by hand is not only time-consuming, cumbersome, and inefficient, but it’s also prone to human error. In addition, mishandling sensitive information could leave confidential data unprotected if sorted incorrectly.
- K-12 IT teams are stretched and understaffed
According to Edweek Research, K-12 districts need to allocate their cybersecurity resources better. In fact, only 20% of budgets are spent on cloud application security. Simply put, there’s not enough time to classify data by hand.
- Data silos can’t be easily monitored
Inadequate data classification can lead to information becoming siloed off in repositories where it isn’t meant to be. In other words, disparate information makes data protection exceedingly difficult for IT teams if they cannot locate data and, in turn, keep it secure.
- Policies are difficult to enforce
A data policy is what dictates how your confidential data can be classified, accessed, or used. Without proper enforcement, highly sensitive data could mistakenly be shared outside the district – opening a Pandora’s box of privacy law and compliance implications.
All told, any one of these challenges could spell trouble for your district’s data security. If left unclassified and therefore unprotected, sensitive data could fall into the hands of unauthorized third parties or cybercriminals. At that point, there’s no telling where that information might go or how it’ll be used.
The benefits of classifying data
When you consider the importance of organized data – or the dangers of unorganized data – it becomes clear that improving data classification should be one of your district’s top priorities. After all, an investment in data classification is an investment in your district’s safety.
And the good news? There’s plenty of benefits that make your investment worthwhile:
- Improved data security efforts
Most importantly, a more accurately classified database is a major advantage for protecting your confidential data. Locating and identifying your most sensitive data swiftly allows you to mitigate risks as they arise quickly – all made possible by classification.
- Simplified compliance
Meeting strict industry regulations is a major obstacle for any district, not to mention the rising standards of teachers and parents. Highly classified data eases this burden and accelerates data retrieval when it’s time to perform an audit.
- Enhanced data monitoring ability
Improving your ability to classify data will naturally break down data silos accurately. The result? More visibility into data use throughout the district allows you to identify DLP policy violations as quickly as possible.
- Greater access control and accountability
Likewise, data classification can help you ensure all users – students, staff members, and administrators – are managing their data appropriately. Accurately organized data enables better insight and access control over how personal information is shared throughout the district.
Data classification best practices
By now, you might be wondering: How can I start improving data classification today? To answer that question and help you realize the intended benefits, here are a few best practices:
- Perform a data risk assessment: Gain an understanding of your data environment by auditing and assessing your district’s regulated and unregulated data circulating.
- Create a data classification policy: A formal policy provides the governance your district needs to uphold accountability and keep data secure.
- Deploy an automated solution: Let’s face it: Protecting your district’s personal data is too big of a job for just a handful of staff members. Time is limited, but data is growing exponentially every day. Cloud DLP uses optical character recognition to automate data classification for both structured and unstructured data while providing an additional layer of security for your district.
Protecting your school district’s data might begin with data classification, but it doesn’t end there. ManagedMethods understands that effective data loss prevention is all about securing the entire data lifecycle. Our cloud security solution automates 24/7 data protection to help your team keep sensitive data out of harm’s way.
Ready to learn more? Check out our webinar for more information about protecting data privacy in education technology or request a free trial today.