In this guide to data loss prevention (DLP) software, we’ll help you understand the current DLP landscape, offering actionable advice on what DLP is, the different types, and what to look for in an effective solution.
Introduction to DLP
Data Loss Prevention (DLP) serves as a technology-driven approach to identifying and safeguarding sensitive data, including intellectual property, personally identifiable information, and financial data. It encompasses the analysis, inspection, and encryption of data both at rest and in transit.
These solutions play a vital role in monitoring, responding to, and defending against unauthorized access attempts by employees or external entities. They achieve this through predefined policies, real-time alerts, and remediation capabilities.
Let’s delve into how DLP operates in practice:
- Discovery: DLP employs automation and data classification techniques to discover and monitor sensitive data traversing your network.
- Identification: Through continuous real-time monitoring, DLP swiftly identifies potential threats to data security following predefined policies.
- Alert notification: Upon detecting a threat, the solution promptly alerts the security team, ensuring visibility into the situation.
- Remediation enforcement: Simultaneously, DLP automatically takes action to mitigate the threat by encrypting the at-risk data, thereby thwarting any potential data breaches.
- Reporting: DLP offers robust reporting functionality, enabling organizations to demonstrate compliance with regulatory standards and audit requirements. This ensures that data is being utilized appropriately and that incidents are effectively managed and prevented.
Benefits of DLP software
DLP is a fundamental tool for ensuring data security across all sorts of industries, especially in highly regulated sectors where compliance is mandatory. Here’s some of the benefits:
- Protecting personal information: Organizations handling 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 intellectual property: DLP is crucial for industries holding valuable trade secrets and intellectual property (IP) as it helps prevent unauthorized access, minimizing the risk of reputational and financial damage.
- Mitigating insider threats: In the era of remote work, DLP is essential for addressing the increased risk of accidental data leaks stemming from SaaS apps and Bring Your Own Device (BYOD) practices.
- Enhancing data visibility: DLP solutions offer comprehensive reporting and analysis tools, enabling organizations to demonstrate compliance with regulatory requirements by tracking data usage and ensuring transparency in data handling practices.
The various types of DLP software solutions
DLP has undergone several evolutions since it was first created. Here are the main types of solutions on the market today.
- Network DLP: Network, or legacy, DLP solutions focus solely on safeguarding data within the confines of the traditional network perimeter. However, they lack the capability to protect data in cloud applications and often rely on regular expressions, which are prone to generating false positives.
- Endpoint DLP: Endpoint solutions operate using proxies, either through installing agents on end-user devices for data monitoring or deploying agents in the cloud to oversee data in transit between cloud services and end users. Nevertheless, as remote work becomes more prevalent, these solutions face challenges in monitoring data accessed via unmanaged devices, collaboration tools, and unmonitored services, leading to blind spots in the CASB framework. For instance, traditional CASB solutions may not effectively monitor the sharing of sensitive data in collaboration platforms like Slack.
- Cloud DLP: Cloud DLP represents a comprehensive approach that integrates various capabilities. It incorporates features such as data classification, natural language processing, machine learning, and encryption, operating seamlessly within cloud applications to detect, monitor, and safeguard sensitive data in real-time. The overarching objective of cloud DLP is to ensure that only authenticated, authorized users access sensitive information and do so in a compliant and secure manner.
Understanding the DLP market: common challenges and solutions in DLP implementation
Data Loss Prevention (DLP) products were once hailed as the ultimate solution for bolstering security measures. However, they now often represent a costly investment with minimal return on investment (ROI) and a significant drain on the time of security teams who find themselves underutilizing them.
Unfortunately, many DLP solutions, particularly legacy and endpoint solutions, fall short in effectively detecting and responding to incidents in real-time and post-incident analysis. This is because of:
- Low ROI and deployment challenges: Implementing DLP solutions can be labor-intensive and time-consuming, resulting in a substantial upfront investment of resources. Moreover, ongoing maintenance adds to the overall cost burden. The high cost coupled with deployment challenges often leads to dissatisfaction among users, as highlighted in a ComputerWeekly survey citing expense as a top challenge in adopting DLP.
- Lack of actionable insights: Traditional DLP tools struggle to accurately distinguish between relevant and irrelevant data, leading to a lack of actionable insights for security analysts. This limitation is exacerbated by the inability of these tools to monitor and interpret data exchanged over modern communication platforms such as Slack or HipChat.
- High false positives: Existing DLP solutions frequently generate false alarms, inundating security teams with unnecessary alerts and contributing to alert fatigue. These false positives not only waste valuable time but also divert attention from genuine threats.
- Limited efficacy against insider threats: DLPs often fail to effectively prevent data breaches instigated by insiders, as these individuals may find ways to circumvent security measures. Even widely used solutions like Microsoft’s endpoint DLP have been unable to fully mitigate email leaks caused by insiders, highlighting a significant gap in effectiveness.
- Low sensitivity and failure to adapt: Despite their intended purpose of detecting every instance of data loss, existing DLP solutions often fall short in promptly identifying breaches and adapting to changes in organizational IT infrastructure. This failure to effectively monitor and respond to evolving threats can result in undetected breaches and increased damage.
Key features of modern DLP software
The shortcomings above can be eradicated by choosing a next-generation cloud DLP solution. Here’s how cloud DLP overcomes the flaws of legacy DLP solutions:
Utilizes natural language processing (NLP)
If you’re grappling with a DLP system inundated with false alarms, chances are it relies on regular expressions for pattern recognition. Regular expressions function as search tools, utilizing characters and symbols to identify specific patterns within text data. These patterns may include character counts, letter sequences, or numerical formats, such as social security or credit card numbers. However, the drawback lies in their limited efficacy with unstructured data. If the text deviates from the predefined patterns outlined by the regular expression, it’s likely to go undetected.
Moreover, regular expression-based DLP systems often generate excessive false positives, failing to consider variations in textual content. For instance, they may mistake a reference code for a credit card number, contributing to alert fatigue. However, advancements in Natural Language Processing (NLP) have revolutionized DLP capabilities.
NLP, a rapidly evolving subset of artificial intelligence, empowers computer systems to comprehend and analyze human language in both written and verbal forms.
Equipped with neural networks, NLP tools analyze language syntax and grammar in real-time, enhancing accuracy and efficiency. Cutting-edge NLP solutions feature self-learning capabilities, enabling continuous improvement based on new data, without manual intervention. With NLP-driven pattern recognition, DLP systems deliver heightened reliability, minimal noise, and improved compliance.
Extends data protection to cloud applications
Traditionally confined to network security, DLP solutions are now pivoting towards safeguarding data in Software as a Service (SaaS) applications, aligning with the increasing adoption of cloud services.
Enterprises worldwide are transitioning from on-premises servers to cloud-based infrastructure, as evidenced by the substantial investments in cloud services. Cloud DLP has emerged as a critical tool for securing sensitive information across cloud applications like Slack, Google Drive, and Teams.
Cloud DLP operates by monitoring, classifying, and protecting sensitive data within cloud environments and collaboration platforms. Through predefined policies, these solutions enforce real-time data loss prevention measures, including redaction, encryption, and deletion. Leading cloud DLP solutions leverage AI to discern user behavior patterns and sensitive data trends, dynamically adjusting policies for optimal protection. This alleviates the burden on IT teams, streamlining policy management and ensuring robust data security in the cloud environment.
Low or No Code
In today’s fast-paced landscape, rapid deployment is paramount. Low-code or no-code DLP solutions equipped with pre-built policy templates enable organizations to initiate data protection measures within minutes, minimizing implementation timelines and operational overhead.
Embedded Training
Traditional compliance training methods often lack engagement and efficacy, failing to instill lasting behavioral changes among employees. Conversely, automated feedback loops integrated into daily workflows offer a more effective approach to cultivating a security-conscious culture. Best-in-class DLP solutions incorporate security nudges and reminders into employees’ daily activities, reinforcing security awareness and fostering a proactive security culture over time.
Generative AI ready
The rise of generative AI tools has brought about a new concern: shadow AI. Similar to the concept of shadow IT, shadow AI refers to employees using generative AI tools without approval from the cybersecurity department, heightening cybersecurity risks twofold.
Firstly, the opacity surrounding the data fed into generative AI applications presents a significant challenge in preventing data exposure. Additionally, the rapid development of proprietary Language Model (LLM) technology increases the likelihood of training data exposure, either through sophisticated cyber attacks orchestrated by threat actors or due to misconfigurations in security controls.
Fortunately, next-generation cloud DLP solutions not only extend data protection to Software as a Service (SaaS) applications but also encompass generative AI applications such as ChatGPT and Bard. This bidirectional protection helps mitigate the risk of data leakage in AI-driven applications.
Selection criteria for DLP tools
The DLP landscape has witnessed significant disruption in recent times, with traditional email and network DLP providers falling short in addressing modern security challenges. In response, cloud-based DLP vendors have emerged as frontrunners, focusing on safeguarding data where it faces the highest risk: in the cloud.
When evaluating cloud-based DLP providers, consider the following questions:
- Deployment ease: How straightforward is the deployment process? A user-friendly deployment experience can expedite implementation and minimize disruptions to operations.
- Data discovery: Does the solution effectively identify both unstructured and structured data within cloud environments? Comprehensive data discovery capabilities are essential for ensuring comprehensive protection.
- Central administration interface: Is the central administration interface intuitive and easy to navigate? A user-friendly interface streamlines management tasks and enhances operational efficiency.
- Reporting and auditing: Can the solution automatically generate reports to meet reporting and auditing requirements? Automated reporting capabilities simplify compliance efforts and facilitate regulatory adherence.
- Automation and AI: Does the solution leverage automation and artificial intelligence to enhance DLP capabilities? Automation accelerates threat detection and response, transforming DLP into a low-intervention task.
- Preventive action: Can the solution proactively alert users to risky behavior and encourage learning from mistakes? Preventive measures help mitigate security incidents and foster a culture of security awareness.
- Proxy vs. APIs: Does the solution utilize proxies or APIs for data integration? APIs offer greater flexibility and scalability, facilitating seamless integration with diverse cloud environments.
The future of DLP: What about SASE?
You may remember that, in 2019, Gartner introduced the concept of ‘SASE’—Secure Access Service Edge—a solution that has garnered significant attention from security providers, touted as the next evolution beyond DLP.
SASE isn’t a novel security technology but rather a convergence of Wide Area Networking (WAN) with existing next-generation security solutions like Data Loss Prevention (DLP), Firewalls as a Service (FWaaS), Secure Web Gateways (SWG), and the zero trust model.
Collectively, these components form SASE: a streamlined, cloud-delivered security service that protects data at the edge while enhancing end-user experience with high-speed functionality. The overarching aim of SASE is to enable borderless cloud security and efficient user functionality.
While SASE holds promise in theory, its practical implementation is still in its infancy, often resulting in underwhelming and error-prone deployments for adopting organizations. Here’s why:
- Over-hyped: Current SASE solutions in the market are in their nascent stages, featuring complex deployment processes and limited interoperability with existing networking and security solutions. This complexity translates to significant investments with uncertain returns.
- Complexity: SASE merges networking and security disciplines into a singular framework, demanding IT teams to possess comprehensive knowledge in both domains for successful deployment and operation.
- Points of presence (PoPs): SASE effectiveness relies on a network of cloud gateways (PoPs) distributed strategically for optimal coverage and performance. Establishing and maintaining such a network can be costly and challenging for smaller enterprises.
- Integration: Successful SASE deployment requires a seamless transition from traditional network security solutions to the SASE model. Organizations must carefully manage this transition, ensuring compatibility and alignment among various technologies and endpoint agents.
Despite these challenges, SASE represents a vision for the future of cloud-based security. While widespread adoption may be some time away, organizations can embrace foundational principles of SASE by prioritizing agile, cloud-based, and data-centric security solutions, thereby safeguarding sensitive data even as users operate at the edge.
Introducing Polymer DLP
Polymer DLP is a low-code, plug-and-play tool that harnesses the capabilities of natural language processing to seamlessly uncover, categorize, and fortify sensitive data across your generative AI and SaaS applications.
Here’s how Polymer is transforming data security:
- Enhanced accuracy: Unlike traditional DLP solutions, Polymer significantly reduces false positives, ensuring a high true positive ratio by amalgamating natural language processing with regular expressions.
- Automated remediation: With its self-learning engine, Polymer autonomously addresses potential data exposure instances, eliminating the need for manual intervention. This empowers your security team to focus on strategic initiatives rather than constantly reacting to alerts.
- Zero trust integration: Polymer DLP incorporates dynamic, contextual authentication measures to authenticate users in real-time as they seek access to sensitive data, thereby embedding zero trust principles into your generative AI tools.
- Measurable impact: Demonstrating the value of security investments becomes effortless with Polymer’s data exposure risk score. This metric quantifies the presence of sensitive data both within and outside the organization, facilitating precise ROI calculations and bolstering data loss prevention endeavors.
- Promoting a security culture: Polymer DLP fosters a culture of security by providing real-time guidance to users who inadvertently breach security protocols. This proactive approach has led to a remarkable reduction of repeat violations by over 40% within mere days. DLP also helps you comply with frameworks such as NIST.
Ready to find out more? Try a free risk scan to discover what sensitive data is lurking unprotected in your cloud apps.
FAQs
- What are the three types of data loss prevention? The major types of DLP solution are network, endpoint and cloud. The most advanced and mature of the three is cloud DLP, which offers holistic protection against data leakage and theft.
- What is the best way to prevent data loss? The best way to prevent data loss is through deploying a specialist data loss prevention solution that uses natural language processing for enhanced accuracy.
- What is the future of DLP? Best-in-breed DLP providers use NLP and AI to bring enhanced accuracy and precision to their solutions, as well as extending DLP for AI applications like ChatGPT and Bard.
- Is DLP required for GDPR compliance? Yes, deploying DLP is essential to achieving GDPR compliance.
- Why do DLP projects fail? Oftentimes, DLP projects fail because companies go for legacy solutions that rely on clunky regular expressions for pattern recognition. These solutions also tend to be complex to deploy, while hindering employee productivity due to overly rigid permissions.
