Is data loss prevention (DLP) relevant in 2025?

Data loss prevention (DLP) solutions were once heralded as the cornerstone of a robust security program—a surefire way to protect sensitive information from falling into the wrong hands. But, today, they often cause more frustration than they do good. Steep learning curves, sky-high costs and a barrage of alerts mean that DLP is often more a nuisance than it is an asset. 

And the data backs this up. In 2024, data breach costs reached an all time high—up a staggering 10% from the year before. Clearly, DLP isn’t protecting data like it’s supposed to. 

This begs the question: is DLP worth the investment in 2025? Let’s find out.

What is data loss prevention (DLP)?

Before we dive into the types, pros and cons of DLP, let’s first explore exactly how this technology works. In theory, DLP should be a game-changer for preventing data breaches. This technology is designed to protect sensitive information from compromise and exposure, while ensuring compliance with regulatory standards such as HIPAA, GDPR, and PCI-DSS. In essence, it’s supposed to act as a virtual lockbox, keeping your organization’s most critical assets safe.

DLP solutions achieve this by continuously analyzing, inspecting, and encrypting data—whether it’s stored in the cloud, sent through messaging apps, or downloaded to end-user devices. With predefined policies, real-time alerts, and automated remediation tools, DLP gives your security team control over who has access to this sensitive data and flags any unauthorized attempts to bypass the security measures.

Here’s a closer look at the DLP process in action:

• Identification: DLP uses real-time monitoring to spot potential threats, triggered by policies that align with your organization’s needs and regulatory requirements.
• Alert and visibility: When a potential breach is detected, the system immediately notifies the security team, offering visibility into the threat.
• Remediation: Simultaneously, DLP steps in to mitigate the risk, encrypting the data to prevent unauthorized access or leaks.
• Compliance reporting: To help organizations stay audit-ready, DLP generates detailed reports that demonstrate how incidents were handled and data was protected.

Do organizations need DLP? 

For many understaffed and overworked security teams, DLP has earned itself a bad reputation.  Here are some of the most common complaints we hear when people first speak to us: 

• Low ROI: DLP tools are time-consuming and expensive to deploy. They require significant setup and ongoing maintenance to function effectively. Even then, many DLP solutions fail to detect data breaches or introduce system glitches, leaving your data vulnerable. A poor-performing DLP can lead to costly leaks and missed opportunities.
• Lack of actionable insights: DLP tools often struggle to identify and separate relevant from irrelevant data, especially with unstructured data shared across communication platforms like Slack or Google Workspace. This leads to a lack of actionable insights and makes it harder for security teams to respond to threats effectively.
• High false positives: Due to the complexity of data usage patterns, DLPs generate many false positives, resulting in unnecessary alerts and wasted time for security teams. This “alert fatigue” drains productivity and can cause critical threats to be overlooked.
• Low efficacy: DLP tools often fail to stop insider threats. Even technical users can find ways to bypass these systems, rendering them ineffective against determined attackers. For instance, Microsoft’s endpoint DLP fails to prevent email leaks despite its claims.
• Low sensitivity: Many DLP solutions miss data breaches, allowing them to go undetected for longer and increasing potential damage. Additionally, they fail to adjust to changes in your organization’s IT environment, which can make previous security measures obsolete.

Given all these pain points, it’s no wonder so many security professionals think DLP is a deadend…

But they’d be wrong. 

The problem isn’t DLP in its entirety, but rather, the type of DLP organizations are investing in. 

The good and bad of DLP solutions

In the age of cloud working and generative AI, traditional DLP tools are no longer fit for purpose. By “traditional,” we mean network DLP tools that focus on securing data on-premises. Today, most employees work in cloud applications like Microsoft 365, Slack, and Google Workspace, but traditional DLP solutions fail to extend protection to these cloud-based environments.

The main issue lies in their reliance on regular expressions (REGEX), a search tool that uses specific characters and symbols to identify patterns in text. While REGEX works well with structured data, it struggles to detect sensitive information in unstructured formats. For example, it can identify credit card numbers if they follow a specific pattern, but if the data doesn’t match exactly, it goes unnoticed.

Beyond this accuracy issue, REGEX-based DLP tools are notorious for generating overwhelming false alarms. These false positives happen because they don’t account for variations in text, such as mistaking a reference code for a credit card number.

Regular expressions & the cloud

When data was primarily stored on-premises, REGEX-based DLP did a passable job. But with the rise of cloud computing and generative AI, the sheer volume and speed of unstructured data have rendered traditional DLP ineffective. The high volume of false positives makes it difficult for security teams to focus on real risks. In today’s cloud-driven environment, preventing breaches feels like searching for a needle in a haystack.

SaaS applications are decentralized and their data doesn’t fit neatly into the rigid structure required by regular expressions. This mismatch leads to inaccurate alerts and even more false positives, creating unnecessary noise that requires manual intervention to sort out.

The alternative: DLP with natural language processing (NLP)

The good news is that there’s an alternative: DLP infused with natural language processing (NLP). Unlike traditional DLP tools, NLP uses AI to analyze and understand human language in both written and verbal formats. NLP models can process text in real-time, learning from new data without requiring constant input from developers.

NLP-based DLP systems are much more reliable, with a higher level of accuracy and far fewer false alarms. This means enhanced compliance, better data protection, and, most importantly, less noise—allowing your security team to focus on actual threats, not false positives.

How next-gen DLP prevents data breaches

One of the reasons that data breaches are on the rise is because there are so many possible avenues for data leakage and theft. Below, we’ll explore the most common data security threats, and how a good DLP solution mitigates them. 

• Hacker intrusion: Hackers are constantly trying to breach systems, with attacks happening every 39 seconds. While multi-factor authentication (MFA) can help protect accounts, it’s not foolproof, as seen in breaches like Uber and Twilio. Next-gen DLP can step in here, preventing sensitive data theft even if an attacker compromises an employee’s account. Tools like Polymer DLP use AI to analyze user behavior in real-time and flag risky actions, such as accessing sensitive data at unusual times or from unfamiliar locations, to stop breaches before they happen.
• Unsecured endpoints: With remote work becoming the norm, unsecured endpoints are a major risk. Employees accessing corporate data on personal devices or connecting to unsecured Wi-Fi put data at risk. Cloud-based DLP solutions offer protection no matter where employees log in from or what devices they use. They work across all your SaaS apps, ensuring continuous, agentless data protection for a truly flexible workforce.
• Negligent data exposure: Mistakes happen, but in the case of data security, they can be costly. Research shows 90% of data breaches are caused by carelessness, such as sending confidential emails to the wrong recipient or leaving databases unprotected. Cloud DLP tools help reduce these risks by combining AI with human risk management. They monitor data usage and provide real-time alerts and corrective actions, helping employees improve their security practices while keeping your data safe.
• Insider threat: Not all threats come from the outside. Malicious insiders—whether current or former employees—can intentionally steal company data. With next-gen DLP tools, monitoring employee behavior and triggering alerts when suspicious actions are detected can reduce the risk of insider threats. By making employees aware that their actions are being monitored, you discourage malicious behavior.
• Cloud-based vulnerabilities: Data stored across multiple environments—public cloud, private cloud, and on-premises—creates additional vulnerabilities. These types of breaches are often costly and harder to detect. Cloud DLP solutions are specifically designed to address cloud security risks, such as misconfigured storage, improper access controls, and misuse of employee credentials. With the right cloud DLP, you can ensure your organization meets its security responsibilities and prevent expensive data leaks before they happen.

What to look for in a next-gen DLP tool 

Investing in the right DLP tool can transform your organization’s security posture, dramatically reducing the chance of data leakage and theft in 2025. Here’s what to look for in a next-gen DLP tool.

NLP

​​A modern DLP tool should combine the power of natural language processing (NLP) with regular expressions (RegEx) to ensure accurate identification of both structured and unstructured data. While RegEx works well for detecting specific patterns, such as credit card numbers or social security numbers, it often struggles with the nuanced, complex nature of unstructured data.

NLP, on the other hand, understands context, allowing it to differentiate between similar data types—like a phone number and a credit card number—by interpreting their intended use in context. This advanced capability helps reduce the frequency of false alerts, improving the accuracy of the DLP system. By minimizing these unnecessary notifications, security teams can focus on real threats, while employees can continue working productively without disruption.

Compliance 

Organizations across various sectors face different compliance mandates, such as HIPAA, GDPR, GLBA, CCPA, and others. Configuring a DLP tool to meet these requirements can often be time-consuming and prone to errors, especially when compliance rules are complex and constantly evolving. To streamline this process, look for a DLP solution that comes with built-in compliance templates tailored to these specific regulations.

These templates allow you to take a low-touch approach to compliance, automating the enforcement of compliance policies. With contextual awareness, the tool can automatically apply the necessary controls to protect sensitive data and ensure you’re adhering to legal standards. Additionally, the best DLP tools will generate audit-ready reports for you, saving time and reducing the risk of human error while ensuring your organization is always compliant.

Automation and AI

While we’ve already touched on automation, it’s worth emphasizing that it’s arguably the most critical component of modern DLP solutions. Automation and AI empower DLP tools to act as an invisible, highly efficient member of your Security Operations Center (SOC), managing tasks related to data protection and compliance without constant human oversight.

This allows your security team to shift focus to more strategic initiatives, rather than getting bogged down by routine monitoring and data protection duties. For this to be effective, the solution must have high fidelity, meaning it accurately detects threats and compliance issues without generating false positives or missing critical risks.

Human risk management 

Human risk management (HRM) is the process of discovering and remediating user-based risks, and delivering timely education to mitigate future threats.  It’s the next frontier in the world of cybersecurity awareness, and will replace traditional training mechanisms in the next five years. HRM and DLP naturally work together.

If a user tries to bypass a policy, DLP immediately blocks the action, while HRM steps in to educate them on what went wrong, helping them avoid future mistakes. With that in mind, your DLP tool should combine data protection with HRM to deliver security whilst building a culture of security. 

Low-code 

Complex and lengthy DLP deployments can drain your security team’s time and reduce the overall effectiveness of the solution, especially if integration issues arise.

To avoid these headaches, it’s smarter to invest in a low-code or no-code solution that can be up and running in just minutes—not weeks. With a quicker deployment, you can start reaping the benefits of enhanced data protection right away, without the risk of integration errors or delays.

Cloud-focused 

With most employees spending their workdays in cloud applications like Slack, Microsoft Teams, and Google Workspace, it’s essential to invest in a DLP solution that extends data protection to these SaaS platforms. The ideal solution should be agentless, ensuring robust protection for your cloud-based data no matter where your employees log in from or what device they’re using. 

Generative AI ready 

The rise of generative AI tools like ChatGPT and Bard has been one of the most significant shifts in the past year, with nearly all Gen Z workers admitting to using these platforms. However, 81% of them report they haven’t been trained on how to use these tools securely.

This creates a major risk—generative AI’s data leakage potential is high. Anything inputted into these tools could be inadvertently shared with another user, potentially exposing sensitive company data to unauthorized parties. To mitigate this risk, it’s crucial to select a DLP solution that extends data protection to generative AI applications, ensuring sensitive information isn’t input, shared, or exposed at any point. 

Wrapping up 

In answer to the question we posed in the introduction of this article, DLP is indeed worth investing in 2025. However, the success of your DLP rollout relies on the type of solution you invest in. Network and endpoint solutions simply aren’t fit for purpose in the cloud-based, gen-AI evolving world.

Instead you need a tool that is agile and intelligent, using a combination of AI, automation, and NLP to overcome the common pain points of DLP and deliver timely, reliable data protection that doesn’t forsake employee productivity. 

Ready to get started? Request a free Polymer DLP demo today.