If you think DLP is a tick-box for compliance, you need to think again. This powerful security technology can help organizations with a myriad of cybersecurity woes: data breach prevention, human error, employee training, and much more.
Here, we’ll explore the use cases of DLP in further detail, and explain why not all DLP solutions are created equal.
What is DLP?
Data loss prevention (DLP) is a technology-driven strategy designed to detect and protect sensitive information, such as intellectual property, personally identifiable information (PII), and financial data. By analyzing, inspecting, and encrypting data both at rest and in transit, DLP solutions help safeguard critical information.
DLP also enables organizations to monitor, respond to, and protect against unauthorized access attempts by employees or third parties. This is achieved through predefined policies, real-time alerts, and remediation tools. Here’s an overview of how DLP operates in real-time:
- Discovery: DLP utilizes automation and data classification to locate and monitor sensitive data within your network.
- Identification: Real-time monitoring enables DLP to identify potential threats to data security based on predefined policies.
- Alerting: When a threat is detected, the system alerts the security team, ensuring they have visibility into the issue.
- Remediation: Concurrently, DLP automatically encrypts the compromised data to prevent a breach.
- Reporting: To comply with standards and meet audit requirements, DLP provides comprehensive reporting functionalities, allowing organizations to demonstrate proper data usage and incident prevention.
Key DLP use cases
DLP has become a cornerstone of the modern enterprise’s cybersecurity strategy across sectors. Let’s take a look at some of the key use cases.
Financial services
The financial services industry is a prime target for malicious actors. Financial institutions handle a wealth of lucrative personal and financial data, including bank account numbers, social security numbers, credit card numbers, merger and acquisition (M&A) information, and intellectual property (IP). Essentially, if it’s valuable, banks are likely to have it.
In addition to external threats, financial institutions must also be vigilant about insider risks. Given that financial services is one of the most competitive sectors today, it is particularly susceptible to insider threats.
The vast amount of sensitive data processed by banks and the associated risks contribute to the industry’s stringent regulatory environment. Financial institutions must comply with a myriad of regulations, including GLBA, COPPA, FACTA, HIPAA, CCPA, and GDPR, among others.
In order to combat all of these challenges, a robust DLP tool is imperative. DLP can support everything from preventing data exfiltration to insider threat mitigation to compliance auditing.
Healthcare
The healthcare industry is under intense regulatory and auditing scrutiny due to the sensitive nature of patient data it handles. Regulators are unforgiving of mistakes or negligence in this area, which is reflected in the hefty fines imposed for violations.
Despite compliance mandates, healthcare organizations struggle–as all organizations do–with human error. Simply an employee sharing data with the wrong person can trigger a HIPAA fine.
On top of that, cybercriminals specifically target healthcare organizations, knowing that the critical nature of their services makes them more likely to comply with demands.
Compounding these challenges is the fact that 80% of healthcare data now passes through the cloud. The rise of tele-consultations, mobile applications, and internal collaboration tools, all cloud-based, improves patient experience and employee collaboration but also heightens the risk of data loss.
Native data security tools in applications like Slack and Google lack the granularity required for HIPAA compliance. Therefore, implementing a third-party DLP tool is essential. DLP can discover and protect PHI/PII across cloud environments, providing unparalleled visibility and control over data flow.
Compliance
Many compliance regulations mandate organizations to use DLP tools–and this will be checked during any auditing.
Of course, meeting compliance mandates like the GDPR and CCPA can be a manually-intensive burden. But with the right DLP tool, organizations can automate several aspects of compliance.
Firstly, DLP aids in the discovery of personal data by identifying its location within the organization, including in Software-as-a-Service (SaaS) applications. This proactive identification is essential for understanding where sensitive information resides and ensuring it is properly managed according to regulatory requirements.
Furthermore, DLP facilitates compliance by implementing measures such as deletion and encryption of sensitive data when necessary. These actions help organizations adhere to compliance mandates that require secure handling of personal and confidential information.
In addition to securing data, DLP prevents unauthorized access to sensitive information. By enforcing access controls and monitoring data access activities in real-time, DLP systems mitigate the risk of unauthorized users accessing or manipulating data, thereby reducing the likelihood of data breaches and tampering incidents.
Moreover, DLP solutions enforce compliance policies across the organization. They ensure that data security standards and regulatory requirements are consistently applied and maintained. This proactive enforcement not only strengthens data protection measures but also helps organizations avoid penalties and reputational damage associated with compliance violations.
Effective cybersecurity
Beyond compliance requirements, data loss prevention (DLP) solutions are essential for safeguarding organizations from both inadvertent data exposure and deliberate malicious activities.
These tools enhance security at the data-access level, ensuring that only authorized personnel have access to sensitive information. By enforcing strict access controls and monitoring data usage in real-time, DLP solutions prevent unauthorized users from accessing confidential data. This capability significantly mitigates the risk of data breaches originating from internal sources or compromised credentials.
Moreover, DLP solutions go further by ensuring that authorized users handle sensitive information responsibly and in accordance with legal and organizational policies. They monitor and restrict how data is utilized, preventing misuse or unauthorized dissemination that could lead to compliance violations or reputational damage.
Challenges and considerations in implementing DLP
For all its potential benefits, many organizations are hesitant of DLP. The reason? False positives.
These erroneous alerts, triggered mistakenly by a policy, have become all too frequent.
Each alert necessitates an investigation, leading to a significant drain on IT department productivity. This results in hours of labor-intensive work and the wastage of valuable resources.
Moreover, ‘alert fatigue’ is a genuine concern. Research indicates that when security teams are inundated with alerts, they are more prone to overlook legitimate security threats.
However, DLP systems shouldn’t be noisy. If they are, it’s likely because they rely on regular expressions for pattern recognition.
Regular expressions are search tools that use characters and symbols to help security teams identify specific patterns in text data. These patterns might include character counts, specific letters, or numbers. For example, a regular expression can be configured to locate social security or credit card numbers within text.
The primary issue with regular expression-based DLP is the high rate of false alarms. These tools do not account for variations in text, such as mistaking a reference code for a credit card number.
On top of that, regular expressions struggle with detecting sensitive information in unstructured formats. If the text doesn’t match the predefined patterns precisely, it often goes unnoticed.
The enormous data flow and subsequent noise make it difficult for information security and compliance teams to concentrate on actual risks. With so many false positives, preventing a breach becomes akin to finding a needle in a haystack.
Modern approaches to DLP
While security teams had to tolerate the false positives associated with DLP a decade ago, advancements in technology have brought significant improvements—enter natural language processing (NLP).
NLP, a rapidly evolving subset of artificial intelligence, enables computer systems to comprehend and analyze human language in both written and verbal forms. These advanced tools leverage neural networks to interpret human language, syntax, and grammar in real-time with remarkable speed.
Top-tier solutions in this field feature self-learning capabilities, allowing NLP models to evolve and improve based on new data without additional input from their developers.
By utilizing NLP for pattern recognition, DLP solutions become far more reliable and, crucially, much quieter. NLP-based systems offer exceptional accuracy, leading to reduced noise, improved compliance, and enhanced data protection.
DLP for cloud environments
Because NLP excels at discovering unstructured data, it also enables security teams to extend DLP to cloud applications like Slack, Microsoft Teams and Zendesk.
These tools are known as cloud or SaaS DLP. Cloud DLP leverages all of DLP’s original features and then some, with the aim of ensuring only verified and authorized users access sensitive information in the cloud–and that they do so in a compliant and secure manner in real-time.
The key benefit of cloud DLP is its ability to prevent data leakage and theft within SaaS applications. These solutions swiftly detect and protect sensitive information across mediums, including unstructured data found in chats, images, and PDF files.
Additionally, cloud DLP solutions facilitate compliance obligations by autonomously generating comprehensive security event inventories aligned with regulatory requirements. This feature streamlines the auditing process, ensuring organizations can easily demonstrate compliance during audits.
Furthermore, cloud DLP embraces the zero trust security model by employing contextual authentication factors. This method evaluates multiple variables such as user identity, the specific activity being conducted, the nature of the data involved, and the file’s location to make informed, risk-based decisions about user access.
By applying stringent access controls and continuously monitoring user behavior, cloud DLP tools effectively protect sensitive information from malicious actors and unauthorized access attempts.
Innovative DLP tools and technologies
Cloud DLP was a lifeline for security teams everywhere, helping them to reduce shadow IT and enhance compliance in the cloud.
Now, the rise of generative AI applications like ChatGPT and Bard have called for DLP to revolutionize once more. These tools need to extend data protection to ever-growing neural networks.
For many DLP providers, this is proving a challenge. After all, generative AI’s relies on both unstructured and semi-structured data sources.
Legacy DLP solutions were designed primarily to secure structured data within perimeter-based security frameworks. These solutions are ill-suited for the current era of cloud and collaboration, where data is stored, shared, and accessed across various cloud-based platforms like Slack and ChatGPT.
Many vendors have attempted to retrofit outdated DLP approaches with new technologies, but this often increases complexity and burdens IT departments with numerous false positives. These alerts, triggered by normal and safe activities, can overwhelm security teams, leading to wasted time and reduced productivity.
Moreover, traditional DLP systems fall short in supporting modern data-sharing practices, meeting stricter privacy requirements, and addressing new risks. They often lack the ability to grasp the contextual nuances of data risks, making it difficult to differentiate between legitimate collaboration and risky activities, or to adjust security responses accordingly.
This makes legacy DLP cumbersome, ineffective, and dangerous in the context of generative AI. These tools add stress and complexity for security teams while failing to prevent potential data leaks, which can lead to data breaches and compliance violations under regulations like GDPR and CCPA.
Why generative AI needs AI-based DLP
As generative AI becomes integral to the modern workplace, security teams need specialized data security solutions that are easy to deploy, minimize user friction, and enhance data protection. AI-based DLP solutions address these needs effectively.
Polymer DLP for AI, for example, allows organizations to leverage generative AI tools like ChatGPT while ensuring privacy, security, and compliance, and promoting responsible and ethical AI usage.
Unlike regular expressions, which struggle to identify unstructured data, our tool uses natural language processing (NLP) to intelligently and contextually redact in-motion PII across generative AI platforms and cloud apps like Slack, Teams, and Dropbox.
Our solution autonomously protects data, reducing the risks associated with generative AI data exposure without requiring user intervention. Instead of relying on agents or complex coding, our tool seamlessly integrates with the APIs used by ChatGPT and other platforms, offering a robust and user-friendly approach to data security in the age of AI.
Future trends in DLP
Best-in-breed DLP solutions are empowering organizations to protect data anywhere and everywhere.
Better still, leading providers are also enabling organizations to create a culture of security by embedding active learning into their tools.
Too often, employee cybersecurity training programs are irregular, infrequent tick-box exercises. Employees don’t remember–or care–about what they’ve learned. As a result, human error continues to be a major problem for organizations across sectors.
Active learning combats this. It integrates educational experiences directly into employees’ daily workflows, ensuring they learn about security practices in real-time rather than through isolated training sessions.
By embedding training prompts and nudges within familiar applications such as Slack, ChatGPT, and Microsoft Teams, active learning solutions ensure that security education is ongoing and seamlessly integrated into employees’ work routines.
These prompts are strategically timed to appear during relevant actions or triggers throughout the workday, fostering continuous learning.
Moreover, active learning solutions are designed to be contextually aware, delivering training prompts that are tailored to each user’s specific role and permissions. That way, training content remains relevant and applicable to the tasks and responsibilities of individual employees, thereby enhancing engagement and effectiveness.
Unlike traditional training methods that are often delivered in lengthy sessions, active learning presents information in bite-sized segments lasting less than a minute. For one, this approach accommodates employees’ busy schedules. For two, it enhances comprehension and retention of the training material by focusing on digestible chunks of information.
Unlock the power of NLP and active learning in DLP
Ready to embrace the potential of NLP-enhanced, active-learning-powered DLP today? Choose Polymer DLP. Our AI-enhanced engine protects data across SaaS and generative AI applications whilst building a culture of security.
