Data Loss Prevention (DLP) Tools: The Essential Guide

What is Data Loss Prevention (DLP)?

DLP can detect and prevent the unauthorized use, access, or transmission of sensitive data, enforcing policies based on data sensitivity levels. All DLP performs some level of contextual analysis as a core function– correlating information like data type, attempted actions, and policies to determine what actions are allowed or disallowed. However, the degree of success with which they do this varies based on technology type, detection accuracy, and security features.

Importance of DLP for Organizations

In today's threat landscape, it's unthinkable to not protect your sensitive data everywhere it lives–cloud platforms, endpoints, SaaS, and cloud storage. Data loss incidents can be extremely expensive. U.S.-specific breaches average twice the global average, coming in at $9.36 million per breach. In an age where funding is becoming harder and harder to secure–especially for tech startups, a data breach during growth phase is not likely to inspire investors. Customer trust is hard to earn, but it can be lost very quickly, making DLP an essential part of every security program.

Types of DLP Solutions

Network DLP

Network DLP is a collection of technologies used to monitor data governance, user activity, and data transmissions in your network traffic to identify data handling and access policy violations. This can include on-premises, cloud, and hybrid networks. Examples of network DLP would include CASB or SASE solutions that include DLP functionality. Netskope is an example of a legacy network DLP solution.

Endpoint DLP

Endpoint DLP capabilities tend to help mitigate risks associated with employee behavior, correlating data handling and access policies with actions taken by end users. One advantage of endpoint DLP that takes a data centric approach is that it can (or should be able to) perform ongoing scans of the endpoint to identify valuable assets like sensitive files or data sets, checking them against policies. Then, this form of DLP can monitor future actions taken with that data, like uploading to noncompliant locations or sharing with unauthorized users. Blocking actions is much easier and faster at the endpoint level, as well. Another advantage is that endpoint DLP is inherently SaaS and URL agnostic–expanding your monitoring scope to any location accessible from that machine.

Cloud DLP

By definition, cloud DLP is simply a DLP tool designed to prevent data loss in your cloud environments, specifically. Some Cloud DLP solutions focus on one workspace, like Microsoft Purview or Google DLP. Others may provide coverage to prevent unauthorized access, unintentional data leaks, and malicious activities across your whole mission critical cloud ecosystem, covering your workspaces and SaaS. These cloud-native solutions often include both inline and out of band DLP capabilities.

Native DLP

Native DLP tools are specific to the cloud apps they are built for, like Slack DLP or Atlassian Guard. If you are just getting started with DLP and simply want to reduce your risk landscape one location at a time, this can be a good option. Unfortunately, these solutions are fairly limited in detection capabilities, lack flexible response options, and don't help you secure cloud data that lives outside of that particular app. But they are a good place to start for security teams that need a quick fix. One other drawback about native DLP is that due to its limited scope, it creates additional data silos, vendor management burdens, and disparate reporting. More universal tools tend to provide greater visibility and more granular control.

Core Features of DLP Tools

Data classification and governance

Detection engines are key to a DLP solution's ability to find, classify, and govern sensitive data. Deciding what actions can be taken in which locations, and by whom all depends on your confidence level in knowing exactly where all your data lives–including errant files, posts, messages, and user endpoints. A good DLP solution will allow you to automatically classify and apply sensitivity labels to files across your entire environment.

You will also need customizable configuration of classification policies and protective measures to ensure your DLP-enforced data governance aligns with your organization's nuanced compliance regulations and corporate policies. User compliance plays a critical role in corporate compliance with regulations.

Access control

Role based access control is an essential component of DLP. Tracking user identities and user roles against granular policies enables organizations to take a proactive approach to keeping threat actors out of your sensitive digital assets. Equally as important, granular control of access helps you prevent insider threats like noncompliant file transfers.

Exfiltration Prevention

One of the most important functions of your DLP is to mitigate potential risks of data theft and unintentional leaks with exfiltration prevention. Inline scanning is required for this function, as the action must be blocked before it occurs. Applications like GDrive make it easy for employees to collaborate and share with other teams or third parties outside your organization. However, its ease of use makes Google Drive sharing an additional risk to organizations' risk posture. Preventing often overlooked internal security vulnerabilities–like the likelihood of experiencing leaks by employees–can help you reduce your number of potential attack vectors.

Inline DLP and the Uses of Inline Scanning

There are two key approaches to scanning data. Inline DLP is the process of scanning and evaluating content in real time, as it's being typed or submitted. Acting as middleware, this aspect of DLP has pros and cons. On the up side, inline DLP allows you to prevent data handling errors right away. On the down side, it can create latency issues. For this reason, modern DLP tools tend to reserve inline scanning for instances where a "send" or "post" action can be immediately catastrophic. Sending protected health information in a plain text email, for example, can result in regulatory compliance violations of HIPAA requirements. So, inline DLP would be used to prompt end users to encrypt their emails or simply block them. Another example would be posting an active API key or other proprietary secrets in GitHub. This is a case where a slight delay to achieve real-time data visibility is worth the outcome of protecting your organization from a major security incident.

Out of band DLP

Out of band DLP is the act of scanning data after it has been transmitted or stored. This is ideal for instances like employees who accidentally share sensitive data like credit card information or login credentials in Slack or Teams. In those cases, attackers are unlikely to find this sensitive data immediately. So, a one or two minute delay in alerting and remediation and a positive user experience supersedes delaying the employee's productivity by making them wait as a message is scanned.

Configurable policies in a strong DLP tool should allow you to give users a chance to remediate their own mistakes first, with a backstop in place to automate remediation if that doesn't happen within a specified time limit. Some security teams allow hours, while others might set thresholds at one or two days. In both cases, the error is going to be corrected to prevent data sprawl in cloud applications, but this can be done in the way that works best for each organization's business needs. The key is balancing work productivity with absolute data security, so employees are not hindered in their work and take part in the company's culture of security–without putting the company in harm's way.

Historical Scanning

Not only is it valuable to scan in near real time with out of band methods, but a fine-tuned approach to monitoring your data is getting started with finding and classifying data that may be lurking in locations you hadn't expected. Performing a historical scan of SaaS apps often surprises infosec teams, turning up instances of Social Security Numbers in random text files, or active API keys and credentials to highly sensitive assets that have been shared with other employees for expediency.

If you don't go back and find, then remediate errant data everywhere your teams work, even the smallest cyber attacks can bring business operations to a halt. For example, one set of leaked credentials that allows threat actors to get into a collaboration and messaging app can result in massive consequences. They could use an active API key to launch ransomware across your PaaS environment, change security configurations, or exfiltrate all your customer data. So, historical scanning is a critical component of a strong DLP strategy.

What are key capabilities to look for when shopping for a data loss prevention solution?

When evaluating DLP solutions, look out for the following features:

Data discovery and classification: Scan and classify sensitive data across various repositories, as well as multiple data types and formats.
Real-time monitoring and alerts: Monitor data movement with instant alerts for any suspicious activity.
Policy enforcement across multiple channels: Manage and enforce security policies across business-critical channels like SaaS apps, AI tools, email, and endpoints.
Integration with existing infrastructure: Integrate seamlessly with your existing tech stacks.
Automated incident response: Automate security workflows to handle incidents quickly.‍
Comprehensive reporting and analytics: Create custom dashboards, detailed audit logs, and advanced analytics for compliance reporting.

How can you choose the right data loss prevention solution for you?

There are dozens of DLP vendors out there. Here are a few things you should consider to zero in on the right one:

Business size and industry: Consider DLP solutions that cater to your business’ size, data volume, and industry sector. That means they will (hopefully) have some ready-made policies that align with common industry compliance requirements, so you can roll out a tool and see day-one impact.
Data detection capabilities: Tools that rely on regular expressions (regex), character matching, and key word proximity are likely to return a slew of time-consuming false positives, while missing harder-to-detect file types and data sets. If your organization stores or processes unstructured sensitive data–from secrets, to PII, to PHI, you'll need a tool that leverages artificial intelligence as part of its engine.
Specific data protection requirements: Identify DLP solutions that address your unique data types, compliance needs, and risk profile.
Existing IT infrastructure and security stack: Look for DLP solutions that integrate seamlessly with your current systems. Effective DLP will give you complete visibility and reduce security data silos, both of which are important for monitoring and securing your cloud-based systems properly.
Budget and resource constraints: Evaluate both initial costs and long-term expenses, including licensing, implementation, and ongoing management costs. The more you can minimize time-consuming false positives and automate security controls, the less dedicated staff you will need to manage your DLP solution(s), which in turn reduces total cost of ownership.
Scalability and growth potential: Choose a DLP solution that can adapt to your business’ future needs. A number of the leading AI-powered providers are newer in the market, but talking to them about scalability in the context of their product roadmap can improve your likelihood of having a true security partner–not just a vendor.‍
Ease of use and management: Prioritize solutions with user-friendly interfaces to reduce operational overhead.
Reporting Features: Most DLP tools focus reporting on reactive information, like how many violations occurred within a period of time. This is valuable information, but if you find a solution that also tracks data hygiene improvement over time and metrics for TTR (time to remediate), pay attention. The ability to demonstrate ROI on security investments is a key feature that will engender executive support for your program and future budget requests.

Evolving Landscape of DLP Technologies

There are a number of data security provider types that take very different approaches to DLP. They all have different origins, corporate foundations, and methodologies to solving challenges in data security. What they all have in common is data loss prevention.

CASB and SASE Vendor DLP: Solutions like Netskope and Zscaler offer network functionality beyond DLP, but they tack this capability on as an act of convenience for clients who don't want to deal with more vendors. The challenge here is that when a platform is designed to do one thing well–like protect the edges of your network from malicious external threats and risky user behaviors, functions like DLP don't receive the same kind of care and attention from product teams that they do in dedicated DLP platforms. AI teams are busy building other models, for example, instead of focusing all their efforts on a detection engine. The result is typically more noise and less accurate signaling, causing clients to eventually wander away in search of something better.

Email and Gateway DLP: Organizations like Proofpoint and Mimecast built their solutions on the foundation of mitigating data security risks and external threats in email. Both are very good at what they do within email, though their detection engines are not as strong as more modern providers. With a channel-specific approach, these solutions user tend to struggle with with inflexible, overly restrictive policies, cumbersome maintenance, length of time to deploy, and negative impacts on productivity.

Insider Risk Management Endpoint DLP: Solutions like Code42, DTEX, and Teramind are looking at employee behaviors for signs of data security policy violations. This user-centric approach tends to over-collect personal activity data, cause employees to feel like they live in a state of surveillance, and doesn't focus on collaborating to protect corporate data. "Negligent employees" are often well-meaning team members who just need proactive, positive security awareness training in the context of their daily work. Being disciplined, blocked, and surveilled is likely to create a sense of resentment that feeds mistrust between employee and employer. Taking a data centric approach still addresses human behavior, but does so collaboratively and with far better results for your security posture improvement over time.

Data Security Posture Management: Organizations like Cyera and Normalyze fall into the DSPM category. While many modern DLP tools have this capability in the contexts where it really counts, like cloud workspaces, DSPM-dedicated tools tend to focus on this functionality as the central means to protect your data. The limitation of a DSPM focused solution is that its performance is likely to come up short in other, vital components of a holistic DLP strategy.

App Native DLP: We have covered Native DLP above, including uses, benefits, and drawbacks. Check out stronger, alternative solutions listed in locations like the Slack Marketplace.

API-based DLP: These providers are lighter solutions, don't over-collect data, and support broader scope of coverage across your cloud ecosystem. Organizations in this space include Metomic, Polymer, Strac, and Nightfall. The way to differentiate solutions that are all promising similar features (flexible policy creation, AI-powered detection, numerous integrations, and broad coverage) is to test their power of detection against your hardest datasets. Nightfall AI offers a place where anyone can test its engine. The Nightfall Playground allows you to anonymously put us to the test in real time. Try it today!

Overview of Nightfall DLP

Nightfall AI is the market leader in Next-Gen DLP. From a single pane of glass, Nightfall gives you visibility into sensitive data in all your mission-critical SaaS apps, cloud workspaces, GenAI tools, and now endpoints. With Nightfall, you can put remediation tasks on autopilot across cloud data loss prevention. No portal switching, no lengthy manual processes, just comprehensive coverage.

With powerful reporting tools and advanced threat detection for your data, Nightfall AI is the perfect tool for a layered approach to data security.

Benefits of Nightfall:

Automatically finds and remediates data sprawl that can put you at risk of experiencing severe data breaches or violating regulatory requirements
Advanced AI-powered detection goes far beyond just credit card data or social security numbers (SSN)
Easily identifies even the most complex, unstructured data sets, including personal data, protected health information, and even custom fields in your SaaS apps
Universal coverage for Google Cloud Platform, Gmail, and all your mission critical SaaS (Jira, Slack, GitHub, ZenDesk, and much more)
Provides robust DLP at the endpoint level, as well
Use templates or create custom security policies to ensure compliance with regulations and a strong data security posture

In a sea of DLP tools, Nightfall AI stands out for its innovative detection engine and automated security workflows. Ready to see how Nightfall can revolutionize your data protection strategy? Get in touch for a free demo of Nightfall's industry-leading data protection solutions.

How does AI enhance DLP solutions?

AI and ML can enhance DLP solutions by:

Improving accuracy in data classification
Reducing false positives through context-aware detection
Adapting to new patterns and threats automatically
Providing more sophisticated user behavior analytics
Automating policy recommendations and enforcement

Are there any open-source DLP solutions available?

Yes, there are certainly open-source DLP solutions available. Open-source DLP solutions may be good options for businesses with a limited budget. However, these solutions may also demand higher operational costs due to ongoing maintenance, fine-tuning, and more.