Data loss prevention (DLP) involves a set of processes and technologies that ensure sensitive data is not lost, misused, or accessed by unauthorized users. As remote work, cloud computing, and other trends continue to move companies further into digital space, the need to build effective data loss prevention strategies becomes increasingly important.
Two core concepts lay the foundation for an effective approach to data loss prevention: sensitive data discovery and data classification. Unfortunately, many DLP data classification tools miss the mark on these key points. To create effective data loss prevention strategies within your organization, you need to understand how to incorporate these concepts into your approach.
Why discovery and classification are vital for protecting data
Sensitive data can have different definitions depending on who you ask. Creating standards for how your organization classifies sensitive data and then applying those classifications to data gives everyone the same guidelines to follow. Before you can classify data, however, your organization must be able to find data where it lives. Organizations cannot safeguard data without first being aware of what they have.
There’s more behind why these two core concepts are integral to effective DLP, and why your current DLP solution likely needs support. Here’s what you need to know.
What are data loss prevention best practices?
Getting a DLP tool or software is a great step towards meeting your data protection goals. However, since DLP is both a set of processes and technologies, it’s difficult to find one solution that effectively addresses all of your needs. As a result, many organizations rely on DLP data classification solutions that are not data-centric, leaving blindspots in their protection.
Depending on your organization’s objectives, you can tackle a DLP strategy in several ways. No matter how you approach DLP, however, these five core practices should play a role.
1. Locate sensitive data using data discovery: the essential first step
If the name of the game is protecting sensitive data from unwanted users, the first step needs to be locating that data. Cybersecurity and IT departments can’t protect what they can’t find.
To keep up with digital transformation, organizations often adopt various software solutions and programs designed to streamline business operations or to enhance the customer experience. Unfortunately, the integration of new technologies can also introduce vulnerabilities. As your organization onboards and trains employees about new third-party systems, it’s easy for information to get lost in the mix or travel where it wasn’t supposed to. It’s not uncommon for an organization to discover previously unknown data within their systems.
Having a sensitive data discovery tool in place that works in real-time is critical for ensuring an effective DLP strategy. According to Accenture, leaders are prioritizing cybersecurity tools that help them move fast, including how quickly a tool can help them detect a security breach or execute their response plan. Sensitive data discovery allows your organization to be proactive by finding sensitive data wherever it resides.
2. Data classification: a critical role
In order to protect the valuable data discovered, organizations also need to know how to classify it into data sensitivity categories.
By classifying data, you:
- Can shift focus towards data that needs your immediate attention
- Automate remediation tasks or workflows based on the data’s sensitivity level
- Monitor or quickly pull reports on data by sensitivity level
- Create standard policies that are easy for employees to understand and follow
Organizations can create any set of classifications that best address their objectives, but a common set of classifications are: public, internal, regulatory, and confidential. Those classification levels address the data’s level of sensitivity. Sometimes, an organization may choose to classify data based on other parameters, such as how much post-breach damage its loss could cause or by the regulations that it’s governed by — like HIPAA, PCI, or GDPR.
Regardless of how an organization decides to classify its data, creating a data classification policy that uses consistent automated tagging can significantly strengthen a DLP strategy. DLP and broader security strategies consist of multiple third-party solutions working together, so when standardized tagging and naming conventions are assigned during classification, it enhances cross-platform communication and further fortifies the security infrastructure.
3. Implement technical safeguards: best practices to follow
Along with the core fundamentals of discovery and classification, organizations should follow these technical best practices:
Backups
Backups help restore lost data, so creating automatic backups of important areas of your database, website, and systems are essential. It’s not uncommon for data to be accidentally lost due to human error. In events like this, having data backed up can save your team from major headaches.
Data encryption
Critical business data should be encrypted when it’s at rest or in transit. While authorized users can view or modify a data file, encryption prevents an unauthorized person from accessing or reading the data, which adds a layer of breach-prevention protection. Along with encryption of data files, software-based and hardware-based encryption can also be applied to endpoints like computers and portable devices. You can encrypt a device’s hard drive to avoid the loss of critical information even if hackers gain access to the device.
Automating data loss prevention
Manual DLP processes may work in a small business environment but are not sustainable for large or enterprise organizations. Manual processes are limited in scope and eat up valuable time and resources. The more DLP processes you can automate, the faster you can act on issues of concern. It also enables your cybersecurity team to focus on strategic planning, rather than repetitive manual work.
When organizations automate data discovery and classification, for example, they achieve a near real-time view of when new, sensitive data is created or introduced, and classification is practically immediate. Manual classification not only takes longer (in some cases, the time lag between creation, manual discovery, and classification can be months) but can be prone to human error and subjectivity.
Automating this process allows your organization to be agile, accurate, and better prepared. It also inherently allows for the Zero Trust model, a popular and highly effective cybersecurity approach, to be applied to sensitive data. Zero Trust requires users and applications alike to be continuously verified when attempting to access sensitive data. Access can also be limited so there’s no accidental or intentional changing of classification at the user level, which could of course lead to a data breach and noncompliance violations.
4. Create and test your data breach response plan: looking ahead
To help reduce the damage of a data breach, it’s important to plan ahead by creating a data breach response plan. Delegate specific responsibilities to your team and make sure to test your plan. It’s worthwhile to run through the plan you’ve created with casual exercises or even in a simulated environment on a routine basis. As the technology landscape evolves, you may discover gaps in your response plan that need to be filled or areas where you will need to pivot.
5. Educate your entire organization: a “security-first” culture
Now that you have created processes and implemented the technologies, it doesn’t stop there. To ensure strong data loss prevention, creating a “security-first” culture within your organization is critical.
Protecting sensitive data isn’t a task that should be left solely to your cybersecurity or IT teams. If your organization processes a large amount of sensitive data, then all departments should understand how it should be treated, along with the regulatory penalties, fines and other consequences that could come out of carelessness. Creating a culture of data privacy and security is an ongoing effort.
How do data discovery and data classification enhance DLP strategies?
Data discovery and classification are two core areas that truly set the foundation for a strong data loss prevention strategy. When organizations skip over these steps, they leave large areas of vulnerability in their DLP strategy—putting at risk reputation, revenue, and regulatory compliance.
In addition to protecting sensitive data and preventing data breaches, an effective DLP strategy should also help your organization achieve regulatory compliance. The penalties for noncompliance can be severe, but a DLP strategy that includes automated data discovery and classification can significantly reduce risk to your organization.
Choosing a data discovery and classification tool for a better DLP data classification strategy
When you choose a data discovery tool, there are a few key factors you should be looking for:
- Can sensitive data discovery be active 24/7?
- Can our team be alerted in real-time? Is there real-time monitoring?
- Can this tool find sensitive unstructured data—or does it only locate structured data?
- What is the tool’s accuracy rate?
- Is it possible to automate data classification? If so, how easy is it?
Strengthen your DLP strategy with Spirion’s data privacy solution
Spirion’s Sensitive Data Platform is a data privacy solution that excels in securing sensitive data at rest via automated data discovery, classification, and remediation. Our discovery tool searches locations such as PDFs, images, cloud repositories, databases, and even employee laptops for sensitive data. No matter where your data ends up — whether it’s in the cloud or on-premise, unstructured or structured — Spirion can find it. It’s easy to set up automated classification, so once data is found, it’s instantly and accurately classified. Organizations can also create workflows with specific trigger events or actions to streamline remediation. To see our platform in action, you can watch a free demo here.
