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  • DLP tools have evolved significantly but face challenges like false positives and limited efficacy against insider threats.
  • Next-gen solutions streamline deployment, improve threat detection, and secure cloud and SaaS apps effectively.
  • Look for a solution that harnesses NLP, active learning and offers protection in AI-driven applications.

Data loss prevention (DLP) tools have been part of the security stack for over 30 years. When this technology first arrived on the scene, it was considered the holy grail of security. However, in the last 10 years, DLP has fallen out of favor. 

It’s known for producing high false positives, barrages of alerts, and large inaccuracies. The thing is, though, DLP can be a silver bullet for preventing data breaches. You just need to know how to use it. 

With that in mind, here’s our guide on DLP best practices. 

What is data loss prevention?

DLP is not just a single tool; it’s a blend of multiple capabilities working together. Features like data classification, natural language processing, machine learning, and encryption all collaborate to discover, monitor, and protect sensitive data in real time.

The main goal of DLP is to ensure that only verified, authorized users access sensitive information, and that they use it in a compliant and secure manner.

Why is DLP crucial for organizations?

DLP is a key tool for ensuring data security across industries, especially in highly regulated sectors where compliance is crucial. Here are some of the benefits:

  • Securing sensitive data: Organizations that handle personally identifiable information (PII), protected health information (PHI), or payment card details (PCI) are required to implement DLP policies to comply with regulations like HIPAA and GDPR.
  • Safeguarding IP: For industries holding valuable trade secrets and intellectual property (IP), DLP is vital. It helps prevent unauthorized access, minimizing the risk of reputation and financial damage.
  • Combating insider threats: In the era of remote work, DLP is essential for addressing the increased risk of accidental data leaks from SaaS apps and Bring Your Own Device (BYOD) practices.
  • Boosting data visibility: DLP solutions offer comprehensive reporting and analysis tools, enabling organizations to track data usage and ensure transparency in data handling practices, which is crucial for demonstrating compliance with regulatory requirements.

How does DLP work?

Here’s how DLP works in practice. 

  • Discovery: DLP leverages automation and data classification techniques to locate and monitor sensitive data traversing your network. It continuously scans for valuable information, ensuring that nothing goes unnoticed.
  • Identification: Through ongoing real-time monitoring, DLP quickly identifies potential threats to data security by adhering to predefined policies. This allows for the rapid detection of unauthorized access or suspicious activities.
  • Alert notification: Upon detecting a threat, DLP promptly notifies the security team, ensuring they have immediate awareness of the situation and can respond quickly.
  • Remediation enforcement: At the same time, DLP automatically takes steps to mitigate the threat by encrypting the vulnerable data. This proactive approach helps prevent data breaches before any damage occurs.
  • Reporting: DLP provides comprehensive reporting capabilities, enabling organizations to demonstrate compliance with regulatory standards and audit requirements. This ensures that data is being handled properly and that incidents are effectively managed and prevented. Detailed reports also aid in refining security policies and enhancing overall data protection strategies.

Best practices for implementing DLP

DLP solutions like Polymer DLP work straight out of the box. But for them to produce maximum efficiency, security teams also need to do some groundwork. 

Break down organizational silos 

Organizational collaboration is essential for effective data loss prevention (DLP). At its core, DLP aims to safeguard sensitive data, but achieving this requires a clear understanding of what constitutes sensitive information and what doesn’t. This is where data classification plays a pivotal role—it serves as the foundation for successful DLP implementation.

However, challenges often arise because the team responsible for classifying data within an enterprise is typically distinct from the security team tasked with managing the DLP solution. This organizational divide can create a figurative barrier between those protecting data and those identifying which data requires protection.

Data itself is dynamic, constantly in flux within enterprises that generate, share, and store vast amounts daily. What may have been considered sensitive yesterday might not hold the same status today, and data that was once low-risk can quickly evolve into a high-risk asset.

To ensure the effectiveness of your DLP strategy, it’s imperative to have a robust classification framework in place. Tools like Polymer DLP offer autonomous, AI-driven classification capabilities, automating the identification of sensitive information. This approach shifts the burden away from manual efforts, enabling the DLP solution to proactively identify and protect sensitive data in real-time.

By breaking down organizational silos and integrating data classification seamlessly into your DLP strategy, you empower your organization to adapt swiftly to evolving data landscapes while maintaining robust security protocols. This alignment fosters a cohesive approach to data protection, enhancing overall compliance and mitigating the risks associated with data breaches.

Map your data

For DLP to be truly effective, it’s crucial to have a comprehensive understanding of all data paths and types, and to determine who has access to what information. This task is inherently time-consuming and must be continually updated due to the constant changes in employees and job roles. 

Moreover, modern communication rarely sticks to one channel. Employees and contractors frequently use various means, such as corporate email, collaboration tools, personal email accounts, and mobile phones. 

Keeping track of these ever-changing data paths manually is both overwhelming and unrealistic. Relying on manual mapping assumes that users have a perfect overview of all their data, which often leads to data gaps and inaccuracies. 

Instead, employing automated scan-based surveys can generate a more accurate and reliable data map.

Get employee buy-in

Legacy DLP solutions are often too restrictive, preventing employees from accessing, transferring, and receiving data essential to their work, leading to frustration and decreased productivity. Because of this, DLP may have a bad name in your enterprise. 

However, for DLP to succeed, it must have employee buy-in. When employees are frustrated by a solution, they are less likely to support it and follow its policies. This frustration can also drive employees to find ways to circumvent the DLP solution.

Employees need to see DLP as a facilitator rather than a hindrance to their work. Engaging employees in the DLP process and ensuring the solution is user-friendly can significantly enhance compliance and effectiveness.

Include unstructured data

Traditional DLP solutions often rely on pattern recognition within structured data. However, today, a significant portion of enterprise data—up to 80%—is unstructured and unregulated. This unstructured data includes a wide range of formats where ideas and intellectual property are continually generated, modified, and stored. 

To protect all sensitive information adequately, your DLP strategy must encompass unstructured data. This approach ensures comprehensive protection across all data types, safeguarding against potential breaches and unauthorized access.

Extend DLP to Collaboration Tools and the Cloud

Legacy DLP solutions were designed before the widespread adoption of smartphones, SaaS tools, and remote working. These solutions typically aim to prevent data from leaving a defined perimeter, but in modern work environments, there is no clear perimeter. 

Traditional DLP systems often lack the intelligence and breadth to monitor sensitive data as it moves through cloud applications, creating significant exit points for potential data breaches. 

According to IDC, 80% of companies experienced at least one cloud data breach in the past year and a half. To address these vulnerabilities, your DLP strategy must extend to collaboration tools and the cloud, ensuring robust protection in today’s diverse and dynamic digital landscape.

Evaluating DLP solutions: What to avoid 

Data Loss Prevention (DLP) tools have long been considered essential for bolstering security measures across various industries. Initially hailed as a game-changer, these tools now often appear as costly investments with minimal returns and significant drains on security teams’ time.

Many DLP solutions, particularly legacy and endpoint systems, struggle to effectively detect and respond to incidents in real-time, and they often fall short in conducting thorough post-incident analysis.

Given these challenges, here’s what to steer clear of when considering a DLP solution:

Deployment difficulties: Deploying a DLP solution can be challenging and resource-intensive, which poses significant hurdles for organizations. The initial setup often requires a substantial investment of time and resources, and ongoing maintenance adds to the overall cost burden. This complexity and high cost of deployment frequently lead to dissatisfaction among users, as highlighted in a survey by ComputerWeekly where expense emerged as a top challenge in adopting DLP solutions.

High false positive rate: False positives are another common issue plaguing existing DLP tools. These solutions often trigger unnecessary alarms, inundating security teams with alerts that turn out to be non-threatening. This flood of false positives not only wastes valuable time but also contributes to alert fatigue, diminishing the effectiveness of the security team.

Proxy-based: Proxies work by acting as a gateway between the end user and the cloud-based resource they are trying to access. They’re notorious for performance bugs, lags, and end user friction.  

Low sensitivity: DLP solutions can lack the sensitivity and adaptability needed to keep pace with evolving IT infrastructures and threats. They may fail to promptly detect breaches or adjust to changes within an organization, leaving vulnerabilities unaddressed. This inability to effectively monitor and respond to emerging threats can result in undetected breaches and heightened damage to organizational security.

Choosing the right DLP solution

To overcome these challenges, selecting a next-generation DLP solution is paramount. Here’s what to look for:

Utilizes natural language processing (NLP)

If your current DLP system is overwhelmed by false alarms, it likely relies on regular expressions for pattern recognition. Regular expressions serve as search tools using specific characters and symbols to identify patterns in text data, such as character counts, letter sequences, or numerical formats like social security or credit card numbers. 

However, their effectiveness is limited when dealing with unstructured data. Text that deviates from these predefined patterns may slip through undetected, contributing to gaps in security.

Furthermore, regular expression-based DLP systems often generate excessive false positives by not accommodating variations in textual content. For example, they might mistake a harmless reference code for a credit card number, leading to alert fatigue among security teams.

However, the advent of natural language processing (NLP) has revolutionized DLP capabilities. NLP, a dynamic subset of artificial intelligence, enables computer systems to comprehend and analyze human language in both written and spoken forms. Utilizing neural networks, NLP tools continuously analyze language syntax and grammar in real-time, significantly enhancing accuracy and operational efficiency.

Leading-edge NLP solutions are equipped with self-learning capabilities, allowing them to improve autonomously with new data without requiring manual updates. By leveraging NLP-driven pattern recognition, modern DLP systems deliver enhanced reliability, reduced noise from false positives, and improved adherence to compliance standards.

Extends security to the cloud 

Data Loss Prevention (DLP) solutions, once primarily associated with network security, are now evolving to meet the demands of safeguarding data in SaaS applications. This shift aligns closely with the widespread adoption of cloud services across enterprises globally.

As organizations increasingly transition from traditional on-premises setups to cloud-based infrastructures, evidenced by significant investments in cloud services, Cloud DLP has emerged as a crucial defense mechanism. It plays a pivotal role in securing sensitive information across popular cloud platforms such as Slack, Google Drive, and Microsoft Teams.

Cloud DLP operates through continuous monitoring, classification, and protection of sensitive data within cloud environments and collaboration tools. By implementing predefined policies, these solutions enforce real-time measures to prevent data loss, including redaction, encryption, and deletion. 

Leading Cloud DLP platforms leverage advanced AI capabilities to analyze user behavior patterns and detect trends in sensitive data usage, adapting policies dynamically to ensure optimal protection.

Low or no-code

In today’s fast-paced digital landscape, the need for rapid deployment of security solutions has become increasingly critical. Organizations are under pressure to secure their data quickly and effectively, without cumbersome implementation processes that could delay protection measures. 

This urgency has sparked the rise of low-code or no-code Data Loss Prevention (DLP) solutions, which offer a streamlined approach to deploying and managing data protection policies.

These modern DLP solutions are designed with ease of use in mind, leveraging intuitive interfaces and pre-built policy templates. This means that even organizations with limited technical expertise can initiate comprehensive data protection measures within minutes rather than weeks or months. 

By eliminating the need for extensive customization and coding, these solutions significantly reduce implementation timelines and operational overhead, allowing IT and security teams to allocate resources more strategically.

Active learning 

Embedded training within data loss prevention (DLP) solutions marks a departure from traditional compliance approaches. Historically, training has struggled with engagement and effectiveness, often failing to instill lasting behavioral changes among employees. This gap underscores the need for innovative methods that seamlessly integrate security awareness into daily workflows.

Modern DLP solutions address this challenge by incorporating active learning directly into employees’ everyday activities. These solutions exemplify best practices by embedding security nudges and reminders within the applications and platforms employees use regularly. 

This not only enhances the relevance of security training but also amplifies its impact by making it an ongoing and integral part of employees’ daily routines. 

Through timely reminders and nudges during work processes, employees are encouraged to adopt secure practices instinctively, reducing the likelihood of human error and fortifying overall organizational resilience against data breaches.

Moreover, embedded training within DLP solutions cultivates a culture of collective responsibility for data security throughout the organization. It encourages employees to actively contribute to safeguarding sensitive information, aligning individual behaviors with organizational security policies and regulatory requirements.

Integrates into generative AI apps

Generative AI tools have introduced a new data security concern: shadow AI, analogous to shadow IT, where employees use generative AI tools without approval, posing heightened cybersecurity risks. 

Firstly, the opacity surrounding data fed into generative AI applications presents a significant obstacle in preventing inadvertent data exposure. The rapid advancement of proprietary Language Model (LLM) technology further amplifies these risks, increasing the likelihood of training data exposure through sophisticated cyber attacks by threat actors or misconfigurations in security controls.

However, a good cloud DLP solution can solve the problem. They not only extend robust data protection to SaaS applications but also encompass generative AI applications like ChatGPT and Bard. This bidirectional protection helps mitigate the risk of data leakage in AI-driven applications by implementing proactive measures and real-time monitoring.

Embrace next-gen DLP now

Ready to unlock the benefits of AI-ready DLP? Find out more about Polymer DLP now.

Polymer is a human-centric data loss prevention (DLP) platform that holistically reduces the risk of data exposure in your SaaS apps and AI tools. In addition to automatically detecting and remediating violations, Polymer coaches your employees to become better data stewards. Try Polymer for free.


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