Data classification is simple in theory. It involves tagging pieces of data by their levels of sensitivity and applying security measures based on those tags, making everything easier to locate and retrieve. Classifying data is a vital step enterprises must take to make sense of their vast amounts of data. Without sensitive data classification, organizations may end up treating all data the same, which shouldn’t be the case. Failing to classify data properly increases the risk of it being compromised. It also increases the possibility that you could be placing security controls on data that isn’t in fact sensitive, leading to loss of productivity and efficiency.
These issues often arise when data classification is handled manually, because the reality is, data classification is no easy feat. It’s a tedious process, which is why it often falls to the wayside. On top of this, manual classification can lead to inconsistent definitions for schema, tags and level of sensitivity, and it leaves sensitive data open to the ongoing risk of human error. This is why an automated classification tool is so essential to any organization’s information security infrastructure.
As you begin to fine-tune your own strategy, these seven data classification considerations will help shed a light on the importance of data classification and why automation is key for its successful execution.
1. Understand the need for sensitive data classification
It’s hard to dedicate time, money and personnel to something without knowing its value, so at a high level, here’s what enterprises can expect from sensitive data classification:
- Greater understanding of their data
- Specific, more secure protective measures for that data
- Sustained regulatory compliance
- Reduced risk of data breaches
Noncompliance and data breaches can be financially damaging and ruin your reputation with customers — which comes with its own financial repercussions — so taking preventative steps, such as data classification, to combat these from happening can benefit your organization in the long run.
2. Discover all the data your organization owns
In order to properly classify your data, you first need to discover it. With the amount of data moving throughout enterprises nowadays, manual discovery is virtually impossible and leaves sensitive data at risk of compromise. A data discovery tool is the way to go in order to ensure every instance of sensitive information, wherever it lives, is identified and eventually, accurately classified and protected.
3. Determine which data privacy regulations apply to your data
Once all the sensitive data your organization owns has been discovered, you’ll gain a clearer understanding of what data privacy laws you’re subject to comply with so you don’t inadvertently partake in noncompliance and risk fines or breaches. When classification enters the picture, it can help to maintain compliance as existing data moves through its lifecycle and new data is collected, because it tags data based on regulation-specific definitions of “sensitive” so the appropriate, compliant protections can be applied.
4. Assess the level of sensitivity and risk associated with your data
Because your data could be regulated by multiple laws, it’s important to understand what potential consequences your organization might face if certain data is compromised or isn’t protected properly.
HIPAA, for example, has three tiers of sensitivity levels: restricted, internal and public data. Restricted data requires the highest level of security and, if compromised, will result in the most severe penalties. Internal data might result in low-to-moderate damage if compromised, and it doesn’t need such strict security controls. Public data can be accessed by anyone, even unauthorized parties, but in order to avoid penalties, it needs to be protected against modification or destruction.
An automated classification tool would be able to categorize any personal health data an organization possesses into these three tiers and apply the corresponding regulatory protective measures. There’s no room for the subjectivity or inconsistencies that might result from manual categorization.
5. Define the categories and labels that will be applied to your data
In addition to those outlined by regulations, your organization should have its own policy for sensitive data use that guides its approach to categorizing and labeling. If privacy regulations assign sensitivity levels to certain information, your own data policy can determine things like the labeling of user roles permitted to access specific pieces of sensitive data and in what capacity.
For any unregulated data you collect, create a simple but effective categorization schema based on potential risks that might arise in the case of compromise. Take, for example, an e-commerce company that has shoppers sign into an account with a username and password to purchase products. In order to protect regulated data like payment card information, names and addresses, the e-commerce company should categorize password data — which is unregulated — in a way that ensures it’s thoroughly protected.
6. Shrink your sensitive data footprint
While the amount of data that enterprises collect is ever-expanding, their data footprints should not be. Luckily, data classification can help with this. In order to shrink your sensitive data footprint, duplicate, outdated and inaccurate information needs to be remediated or deleted in a manner that’s both legally compliant and won’t put your organization at risk. Classification is how enterprises can know exactly what data they have, how it was collected, how long it must be retained and how to properly dispose of it once its retainer window is up. There’s no space for sensitive data to be left vulnerable, which isn’t always the case with manual classification.
7. Implement automated, persistent classification
The need for data classification is abundantly clear, and the objections enterprises often have when it comes to implementing classification — it’s cumbersome, it’s inconsistent, it’s disruptive to business — make a strong case for an automated tool to tackle the task. It bridges the gap between all the sensitive information organizations collect and their ability to protect it by eliminating the risk of human error and streamlining the most time-consuming and complex data classification steps.
Get reliable classification automation with Spirion
Spirion’s Sensitive Data Platform is a revolutionary and modern approach to sensitive data classification. It automatically applies persistent labels and tags based on data collection methods and intended use. And, it’s designed to be intuitive for users of all skill sets and experiences, featuring automated playbooks that deliver fine-grained control of sensitive information in accordance with privacy regulations.
To learn more about how you can implement effective data classification through automation to bolster data security and compliance within your organization, contact us today.