DLP 101: The Best Data Loss Prevention Software for 2024

In a world where data breaches hit the headlines almost daily, organizations need effective data protection measures more than ever. Let's dive into the world of Data Loss Prevention (DLP) and discover how this powerful software can safeguard your organization's most valuable asset: your data.

What is data loss prevention, or DLP?

Data Loss Prevention, or DLP, refers to software that can stop the unauthorized access, transmission, or leakage of sensitive data. Think of DLP software as a digital bodyguard that protects data from loss, theft, or corruption by enforcing strict policies that control data usage and access.

Here are a few real-world examples of DLP in action:

1. Healthcare: Imagine a hospital using DLP software to maintain HIPAA compliance. This software can automatically detect and redact any health information that’s accidentally shared in a Slack message or unencrypted email. 
2. Finance: Picture a bank implementing DLP software to identify insider risks. This software can alert their security team when an employee attempts to share a file outside the organization that contains thousands of customer credit card numbers.
3. Tech: Envision a tech company using DLP software to protect their source code from data exfiltration. This software can monitor file shares and block any attempts to transfer code to external devices or cloud storage.

These scenarios highlight just a few ways organizations across various sectors rely on DLP software to protect their sensitive data. Now, let's explore the benefits of DLP in 2024.

Why is DLP software important in 2024?

In 2024, the data protection landscape is getting increasingly complex. Organizations face a myriad of new challenges, including:

1. Increased data volume: Data is proliferating across the cloud, which complicates monitoring and protection efforts. For example, a multinational corporation might struggle to track and secure terabytes of data spread across multiple cloud applications and on-premises servers.
2. Remote work: Many organizations have moved to remote or hybrid work, which expands their attack surfaces. For example, consider a marketing agency where employees access customer data from their home computers. DLP software ensures that this data remains secure even outside the office network.‍
3. Regulatory compliance: Regulations like GDPR, CCPA, and HIPAA demand rigorous data protection practices. For instance, an e-commerce company must use DLP software to detect and protect payment card information (PCI data) across all its systems to comply with PCI-DSS requirements.

What are the key components of DLP software?

If you're searching for DLP software, keep the following components in mind:

1. Data discovery: This involves identifying and classifying sensitive data. For example, DLP software might scan a company's cloud to find any credit card numbers or social security numbers that have been shared in places they shouldn’t be shared. 
2. Data classification: This step categorizes data based on sensitivity and importance. DLP software can automatically label files containing personal information as "Confidential" and restrict access accordingly.
3. Policy enforcement: This involves implementing rules that govern how users can access, share, and transmit data. For instance, a DLP policy might prevent employees from sending emails containing sensitive data to external recipients.
4. Monitoring and reporting: DLP software continuously monitors data usage and generates reports on potential threats or policy breaches. This could include tracking user activity, such as attempts to access restricted files or unusual data transfer patterns.‍
5. Data protection: DLP software responds to data breaches or leaks in real time. For example, if it detects an unauthorized attempt to download a large number of customer records, it could automatically block access to the file and alert the security team.

What data types does DLP software cover?

DLP software protects various types of sensitive information, each with specific compliance requirements:

1. Personally Identifiable Information (PII): This includes data like social security numbers and email addresses that can be used to identify an individual. Regulations like GDPR and CCPA protect PII. For example, DLP software might scan outgoing emails to redact any social security numbers before sending the email.
2. Payment Card Information (PCI): Financial details such as credit card numbers and CVV codes fall under this category. The Payment Card Industry Data Security Standard (PCI-DSS) governs PCI. A DLP solution could prevent employees from storing sensitive data, such as unencrypted credit card numbers, on their local machines.
3. Protected Health Information (PHI): Medical data, including patient records and health insurance information, falls under HIPAA, which mandates stringent protection measures. A hospital's DLP system might monitor file transfers to ensure only authorized personnel access patient records.

By adhering to these compliance standards, businesses can ensure the proper protection and management of sensitive data across all business-critical sectors.

What are the challenges of using DLP software?

DLP software can present several challenges, including:

1. False positives: Legacy DLP software often generates high volumes of false positives due to its reliance on predefined rules. This issue occurs when legitimate activities mistakenly set off alerts. For example, legacy DLP software might flag an employee sending a press release containing the company's name and address as a potential data leak.
2. Complex environments: It can be challenging to manage data across IT landscapes filled with diverse systems, platforms, and data types. For instance, a multinational enterprise might struggle to implement consistent DLP policies across its endpoints, networks, cloud services, and custom-built applications.‍
3. User resistance: Employees may perceive DLP policies as intrusive or restrictive, leading to resistance and potential workarounds. For example, workers might resort to using personal cloud storage to share files if they find the company's DLP-enforced file sharing process too cumbersome.

What are the best DLP software vendors?

Here's a rundown of some of the top DLP software vendors on the market today:

Nightfall AI

Nightfall is the comprehensive, AI-native data security platform that enables organizations to discover and protect sensitive data where end-users work in the modern enterprise: across SaaS and GenAI apps as well as email and endpoints. Whether you're looking to prevent secret sprawl, stop data exfiltration, or secure AI usage, Nightfall's industry-leading detection engine and flexible APIs cover a wide variety of use cases. Get a demo of Nightfall here.

Microsoft Purview

Microsoft Purview integrates smoothly with its suite of products, like Office 365 and Azure. It features strong data detection capabilities, making it easy to secure information within the Microsoft ecosystem.

Google Cloud DLP

Google's cloud-focused DLP product helps businesses locate, classify, and protect sensitive data within Google Cloud. It also offers advanced encryption and policy management tools.

Zscaler

Zscaler offers content inspection, policy enforcement, and incident management across multiple channels. Its global cloud architecture provides scalable performance for large enterprises. However, some users find Zscaler's DLP complex to configure and manage.

Code42

Code42 is known for its robust endpoint data protection and insider threat detection capabilities. However, it may not provide the same comprehensive coverage for cloud and SaaS environments as other leading DLP solutions.

Netskope

Netskope is known for its seamless integrations with SaaS, IaaS, and web environments. It offers in-depth visibility and data protection via granular policy controls and user behavior analytics. However, users often note that deploying and maintaining Netskope can prove difficult.

How can AI improve DLP software?

AI-powered DLP software elevates data protection by enhancing detection accuracy and automating responses to potential threats. Legacy DLP software relies on static rules, while AI-powered software can recognize patterns more effectively, making it adept at spotting threats.

Here's how AI improves DLP:

1. Better detection: AI can identify both structured and unstructured sensitive data. For example, AI-powered DLP software might be able to identify sensitive data in images as well as spreadsheets, PDFs, and more. 
2. Contextual understanding: AI-driven DLP software understands user activity and uses it to flag abnormal behavior without disrupting normal operations. For instance, it might learn that it's normal for the finance team to access large volumes of financial data at month-end, but flag similar access by a marketing employee as suspicious.
3. Faster responses: AI can automate incident response. For example, AI-powered DLP software might automatically quarantine a file that contains sensitive data before anyone can share it externally.‍
4. Continuous learning: AI systems learn from every incident, improving over time to stay ahead of evolving threats. This means that DLP software can become more effective at distinguishing between legitimate and suspicious activities over time.

What does the future of DLP software look like?

Following the rise of AI and the migration to the cloud, it’s necessary to implement DLP software that's equipped to address emerging risks. Future DLP software will heavily rely on ML and GenAI to provide real-time protection and secure data in cloud environments and beyond, especially for remote businesses.

Nightfall stands at the cutting edge of DLP innovation. Schedule a demo today to see how how AI-powered DLP software like Nightfall can help you to secure your business.

FAQs about DLP software

Which industries benefit most from DLP software?

Industries that handle sensitive information—like healthcare, finance, and tech—rely heavily on DLP software. Hospitals protect patient records, banks secure financial data, and tech safeguards IP. These sectors face strict regulations like HIPAA, PCI-DSS, and GDPR, which require constant data protection. However, schools, e-commerce retailers, and other businesses also utilize DLP to prevent data breaches and safeguard sensitive information.

How does DLP software protect cloud data?

Cloud-based DLP software monitors and secures data in services like Google Workspace and Microsoft 365. It tracks data movement through cloud apps and ensures that only authorized users have access to sensitive information. By integrating with major cloud platforms, this software automatically enforces security policies and keeps your data safe in real time.

What's the difference between network DLP, endpoint DLP, and cloud DLP?

Here's a brief overview of each type of DLP:

1. Network DLP software secures data as it moves across your systems; it monitors web traffic to ensure sensitive information doesn't leave your network.
2. Endpoint DLP software protects data on devices such as laptops and smartphones; it monitors actions like copying or transferring sensitive files to endpoint devices. 
3. Cloud DLP software secures data that's stored and processed in cloud applications like Google Drive, Slack, and Microsoft 365; it monitors for sensitive data sprawl in DMs, drives, files, and more. 

Companies often employ a combination of network DLP, endpoint DLP, and cloud DLP to safeguard data across all critical platforms.

Can DLP software assist with compliance?

Yes. DLP helps organizations to comply with data protection laws like HIPAA, PCI-DSS, and GDPR. It tracks sensitive data and enforces rules to ensure proper handling. DLP software also provides audit logs and reports to help businesses demonstrate compliance with data security requirements.