Data loss prevention (DLP) has a noise problem. Despite security teams carefully programming their solutions to catch sensitive data, most DLP tools alert them to benign instances—and lots of them.
The reason? Regular expressions. Once the cornerstone of DLP, regular expressions have become outdated for the modern, cloud-based world. They cause inconsistent, inconsequential, inordinate amount of alerts.
Luckily, there’s a better way forward: machine learning for DLP.
What is machine learning?
Machine learning, a dynamic subset of artificial intelligence (AI), empowers software applications to autonomously learn and improve from the data they process. Over time, as they are exposed to more data, these applications enhance their predictive capabilities without requiring human intervention.
When integrated with your data loss prevention (DLP) solution, machine learning can automatically identify and secure sensitive information, such as PII and PHI, across your cloud applications, APIs, and wider infrastructure.
Initially, you’ll configure the DLP solution with a set of rules to define what types of data to look for and how to handle them. Subsequently, the machine learning component of the DLP system allows it to continually learn and analyze new data patterns, facilitating the automatic redaction of sensitive information with exceptionally high fidelity.
While regular expressions might mistake a reference number for a social security number, DLP infused with machine learning is contextually aware. Thanks to its self-learning engine, it understands not just the data itself, but the situation surrounding it. As a result, it only redacts truly sensitive data–not just numeric patterns that resemble it.
On top of that, machine learning gives DLP an extra edge by monitoring user behavior, enabling the DLP solution to detect and react to suspicious or risky activities. This involves actions like redacting sensitive data or even preventing users from transmitting certain information—thereby enhancing the overall security and compliance of your organization.
Benefits of machine learning in DLP
In the realm of DLP, machine learning is a game-changer. Infused with AI and ML, modern DLP solutions are far superior and faster at detecting business-critical data compared to legacy systems.
Due to their self-learning capabilities, these advanced DLP solutions require significantly less intervention from IT teams, allowing these professionals to focus on higher-value tasks instead of constantly responding to false alarms generated by their DLP systems.
Here’s a deeper look at the benefits of machine learning-enabled DLP.
Empower your team
Research indicates that 83% of cybersecurity professionals feel overworked, with one of the main contributors to burnout being the overwhelming amount of false positives they must sift through. Incorporating AI and ML into DLP systems can alleviate some of this burden by enabling these solutions to make automatic decisions.
This shift allows security personnel to concentrate on more critical tasks and better prioritize their workload. It’s important to note that AI-enhanced DLP is not intended to replace security analysts but to augment their ability to respond to threats in real-time by handling more menial, time-intensive tasks such as data classification and redaction.
Accelerate DLP efficiency
Traditional DLP solutions often make data classification and redaction cumbersome and time-consuming, with policy-based rules needing frequent updates. This constant need for manual adjustments leaves security teams perpetually on the back foot and data continuously at risk.
AI-powered DLP, however, is self-learning and utilizes previous logs, rules, and pattern recognition to identify sensitive data, even in the absence of explicit policies. In the case of insider threats, AI-driven DLP’s user behavior analysis capabilities can prevent leaks or breaches in real-time, while simultaneously enhancing end-users’ awareness of data security.
Handle unstructured data effectively
AI and ML excel at analyzing vast amounts of data at incredible speeds, often with greater accuracy than human counterparts. For AI, more data equates to better learning, resulting in increasingly efficient and accurate solutions. This capability is particularly valuable when dealing with unstructured data, where the volume and variability can overwhelm traditional DLP systems.
Secure cloud applications
In an era dominated by Slack, Teams, and remote work, keeping data secure is a significant concern for security leaders. Especially given the potential repercussions of a data breach like compliance fines, downtime, reputational damage, and loss of brand equity, it’s crucial to protect your data.
Fortunately, with the right DLP solution, organizations can achieve robust protection even as their data moves through various cloud applications. AI-enhanced DLP ensures that data remains secure, providing peace of mind for security leaders and safeguarding the organization’s assets in a rapidly evolving digital landscape.
Challenges of implementing ML in DLP
It’s clear that machine learning is the next frontier of DLP. And yet, adoption has been slow across organizations.
There’s a few reasons for this. Depending on the DLP vendor you choose, you may come across a few–rather significant–obstacles. Let’s take a look at the challenges in further detail.
- Inadequate natural language processing (NLP) capabilities: Some DLP solutions often lack robust NLP capabilities, making them ineffective at accurately identifying and understanding sensitive information embedded in text. Without NLP, these solutions struggle to discern context and nuances, leading to missed detections or false positives.
- Absence of a machine learning feedback loop: Many DLP systems claim to use AI, but do not incorporate a machine learning feedback loop, preventing them from learning new patterns and adapting to emerging threats. This limitation results in a static, inflexible security posture that fails to keep pace with evolving data breach techniques.
- Lack of customizable feature-setting: Ineffective DLP solutions typically do not allow for customizable feature-setting that can integrate on-the-ground heuristics. This inflexibility hinders the ability to tailor the DLP system to the specific needs and unique data environments of different organizations.
- Dependency on large training data sets: Some ML-based DLP systems require extensive training data to function effectively. This dependency can be a significant drawback, as many organizations may not have the resources to compile large data sets, leaving them with a less effective security solution.
- Complex installation and configuration processes: DLP solutions often come with complex installation and configuration processes, requiring substantial time and technical expertise to deploy. This complexity can delay implementation and reduce overall system efficiency, making it harder for organizations to achieve timely data protection.
- Inability to separate metadata from real data: Inefficient DLP systems may fail to separate metadata from real data, limiting the accessibility of the dashboard to a broader audience. This constraint can restrict the usability of the DLP solution, making it difficult for various stakeholders to access and interpret important security information.
Overcoming the hurdles to machine learning in DLP
Keeping these shortcomings in mind, here are the features to look for when assessing ML-based DLP solutions on the market.
Harnesses machine learning through NLP
Natural language processing (NLP) is a sophisticated branch of machine learning that delves into the intricacies of human language, enabling systems to comprehend and interpret the nuances of textual communication. It excels in uncovering and identifying unstructured sensitive data within diverse formats such as documents, images, and web chats.
Powered by its self-learning engine, NLP continuously improves its ability to understand the context of human interactions, ensuring that the Data Loss Prevention (DLP) mechanisms are activated only when genuinely necessary.
For organizations aiming to bolster their data security measures, integrating a DLP tool equipped with NLP capabilities is crucial. Here are the key benefits:
- Enhanced detection of unstructured data:
- Traditional DLP systems often struggle with unstructured data due to its variability and complexity. NLP excels in parsing and analyzing such data, accurately identifying sensitive information that might otherwise be missed.
- Contextual understanding:
- The self-learning engine of NLP enables it to grasp the context in which data is used. This contextual understanding allows the DLP system to differentiate between benign and sensitive information, reducing false positives and ensuring that security measures are applied only when necessary.
- Automated classification and security:
- With NLP, the process of detecting, classifying, and securing sensitive data becomes largely automated. This automation reduces the need for manual intervention, allowing IT and security teams to focus on more strategic tasks while maintaining high accuracy in data protection.
- Adaptability and continuous improvement:
- NLP’s self-learning capabilities ensure that the DLP system continually adapts to new data patterns and emerging threats. This adaptability is essential in today’s rapidly evolving digital landscape, where new vulnerabilities can appear unexpectedly.
- High accuracy and efficiency:
- By leveraging NLP, organizations can achieve exceptional accuracy in data detection and classification. This precision not only enhances security but also improves operational efficiency by minimizing the time spent on manual data handling and reducing the incidence of false alarms.
Ready-to-go templates
Forget about spending weeks or even months configuring your new data loss prevention (DLP) tool. Best-in-class DLP solutions are designed to work out-of-the-box, providing instant benefits.
These low and no-code solutions come with pre-built compliance templates for mandates like HIPAA and GDPR, allowing you to start using your DLP solution within minutes.
- Rapid deployment:
- Top-tier DLP solutions eliminate the lengthy setup process. With ready-to-go templates, organizations can quickly deploy their DLP systems without the need for extensive configuration, ensuring immediate protection of sensitive data.
- Pre-built compliance templates:
- These solutions come equipped with templates tailored to specific compliance requirements such as HIPAA, GDPR, and other regulatory frameworks. This feature ensures that your organization meets mandatory compliance standards right from the start, without the hassle of manual configuration.
- Low and no-code solutions:
- The simplicity of low and no-code DLP solutions means they require minimal technical expertise. This accessibility allows organizations with limited IT resources to implement robust data protection measures efficiently.
- Ease of use for small IT teams:
- Even organizations with only one or two IT staff can initiate comprehensive data protection measures within minutes. The intuitive interface and automated features of these DLP solutions enable small teams to manage and maintain effective data security without being overwhelmed.
Features active learning
Leading DLP solutions incorporate active learning directly into employees’ everyday activities. Through security nudges and reminders within the applications employees use regularly, these tools help to build a culture of security.
- Enhanced relevance and impact:
- By integrating training into the tools and workflows that employees already use, the relevance of security training is significantly enhanced. This approach ensures that security awareness is not a separate, occasional task but an ongoing part of daily work routines.
- Ongoing engagement:
- Active learning transforms security training into a continuous process. Timely reminders and nudges during work processes encourage employees to adopt secure practices instinctively, reducing the likelihood of human error and fortifying organizational resilience against data breaches.
- Cultivation of a security culture:
- Embedded training within DLP solutions fosters a culture of collective responsibility for data security. Employees are encouraged to actively participate in safeguarding sensitive information, aligning their behaviors with organizational security policies and regulatory requirements.
- Behavioral change:
- Security nudges embedded in daily workflows lead to meaningful behavioral changes. As employees receive constant, contextually relevant reminders about security, they are more likely to internalize and adhere to best practices, resulting in a stronger overall security posture.
- Reduction in human error:
- By integrating security training into everyday tasks, the likelihood of human error is significantly reduced. Employees become more aware of potential risks and are better equipped to handle sensitive information securely, contributing to a lower incidence of data breaches.
Generative AI-ready
Generative AI tools have introduced a new dimension of data security concerns, particularly through the emergence of shadow AI—similar to shadow IT—where employees use generative AI tools without official approval, thereby heightening cybersecurity risks.
A robust cloud DLP solution can effectively address these challenges by extending comprehensive data protection to both SaaS applications and generative AI tools like ChatGPT and Bard. Here’s how:
- Bidirectional protection:
- Effective cloud DLP solutions provide bidirectional protection, ensuring that data flowing into and out of generative AI applications is monitored and secured. This proactive measure significantly reduces the risk of data leakage.
- Real-time monitoring:
- By implementing real-time monitoring, DLP solutions can detect and respond to potential security threats immediately. This capability is crucial in mitigating risks associated with the rapid processing and generation of data by AI tools.
- Proactive measures:
- DLP solutions take proactive measures to secure data, such as enforcing policies on data handling and usage within generative AI applications. These measures help prevent unauthorized access and data breaches.
- Integration with SaaS and AI applications:
- A good DLP solution integrates seamlessly with both SaaS and generative AI applications, providing a unified security approach. This integration ensures that all data, regardless of its source, is protected under a single, coherent policy framework.
Unleash the potential of machine learning in your SOC today
One of the most powerful aspects of ML is its capacity for independent learning. Like a human brain, ML neural networks grow and improve with each interaction.
The longer you use an AI-powered DLP tool, the more accurate and effective it becomes. It will develop contextual knowledge specific to your organization, enabling it to refine security policies, understand user behavior, and better uphold data security.
When searching for a DLP solution that can scale with your organization’s evolving security needs, especially if your employees rely on highly collaborative cloud applications, consider one that leverages MLI.
Features such as NLP-based data classification, flexible remediation, user training, and contextual enrichment will minimize false positives and automatically enhance your security posture.
Polymer DLP offers all these capabilities and more. Request a free demo today to see our solution in action and experience the benefits for yourself.
