Data Loss Prevention (DLP): Types & 5 Challenges
The increased mobility introduces risks of data loss or theft, which can lead to severe financial losses and reputational damage for companies. Effective Data loss prevention (DLP) software needs to prevent the unauthorized movement of private data and personally identifiable information (PII) to limit reputational and financial risk.
Explore DLP fundamentals, challenges organizations face when implementing DLP solutions, and actionable strategies to overcome these hurdles.1
If you are already aware of data loss prevention and want to leverage an automated tool, here is a guide and a list of the top DLP software.
What is data loss prevention (DLP)?
Data loss prevention (DLP) refers to strategies, tools, and practices aimed at detecting and preventing unauthorized access, transfer, or exposure of sensitive business data. DLP solutions help organizations detect and prevent data breaches, exfiltration, or unwanted destruction of sensitive data. Organizations need to protect their sensitive data and maintain compliance with regulatory requirements.
Key elements of DLP include:
- Data Identification: Classifying and tagging sensitive data.
- Data Visibility: Monitoring data access and movement across systems.
- Access Control: Restricting data access based on user roles and permissions.
Types of DLP
Data loss prevention has three types based on the different business environments that the solutions and practices are targeted towards.
- Endpoint DLP: Protects data on end-user devices such as laptops, smartphones, and desktops by monitoring and controlling activities that could lead to data breaches. Example: Blocking unauthorized file transfers from a company laptop to an external drive.
- Network DLP: Monitors and secures data in transit across the network, preventing unauthorized data transfers and ensuring sensitive information does not leave the organization’s network, such as protecting email communications, instant messaging, and file transfers. Example: Restricting sensitive email attachments to external recipients.
- Cloud DLP: Safeguards data stored in cloud services by applying security policies and controls to prevent unauthorized access and data leakage from cloud-based environments, such as Google Drive, Dropbox, and AWS. Example: Preventing unauthorized downloads of sensitive files from a shared cloud folder.
What are the causes of data leaks?
Data leaks in organizations can occur due to a variety of reasons, often involving both technical vulnerabilities and human factors. This section highlights some of the major causes of data leaks and breaches in organizations.
1. Human errors
One of the most common causes of data leaks is human error. This can include accidental sharing of sensitive data, misconfiguring databases, sending sensitive data to the wrong recipient, or even losing devices containing sensitive data.
This can also happen via the various communication channels employees use, including mobile devices, to send and store data in multiple locations. If they don’t follow the organization’s data loss prevention and data usage policies, unauthorized parties can gain access to sensitive business data, leading to data leaks and breaches.
Case Study: CodeStream Technologies
Challenge: Employees working from home were using personal devices and unsecured networks, creating data security gaps. 2
Solution Implemented:
- Set up VPN-integrated network DLP monitoring.
- Deployed cloud-native DLP solution.
- Integrated with collaboration tools (Slack, Zoom, Google Workspace).
- Implemented endpoint DLP for BYOD devices.
2. External threats
Malware and other cyber attacks, like data exfiltration attempts, are common causes of data loss. For example, opening suspicious emails or accessing untrusted websites can lead to data breaches.
2.1. Phishing attacks
Cybercriminals often use phishing attacks to trick employees into revealing confidential or sensitive data, such as login credentials. Once these credentials are compromised, attackers can gain unauthorized access to the organization’s systems and data.
2.2. Weak or compromised passwords
Attackers can easily guess weak or reused passwords. Additionally, if an employee uses the same password across multiple services, a breach in one can lead to a compromise in another, including the organization’s systems.
Case Study: Precision Auto Components Inc.
Challenge: Engineering drawings and proprietary manufacturing processes were at risk of theft by competitors and foreign entities.3
Solution Implemented:
- Integrated with existing access control systems.
- Implemented comprehensive endpoint DLP across engineering workstations.
- Deployed network DLP to monitor CAD file transfers.
- Set up content classification for technical drawings and specifications.
3. Insider threats
Giving access permission to sensitive data can enable a malicious insider to copy or steal your business data, including proprietary data and confidential information.
Case Study: Sterling Capital Advisors
Challenge: A departing financial advisor attempted to steal client contact lists and investment portfolios for a competitor.
Solution Implemented:
- Deployed network DLP to monitor file transfers and email attachments.
- Enhanced endpoint DLP monitoring on high-risk user devices.
- Implemented user behavior analytics (UBA) integration.
- Set up real-time alerts for large data transfers.
4. Outdated or unpatched software
Vulnerabilities in software can be exploited by attackers if they are not promptly patched. Organizations that fail to keep their software and systems updated are at a higher risk of data breaches.
Why is data loss prevention important?
Stats from the IBM Cost of a Data Breach Report:4
Data loss can also damage companies’ productivity, reputation, and revenue. For these reasons, a detailed data loss prevention strategy is crucial to secure companies’ confidential or sensitive data. A comprehensive data loss prevention solution can reduce the data loss risk by monitoring endpoint activities and filtering data streams, and by using machine learning for better detection and prevention.
What are the top DLP challenges and how to overcome them?
Implementing effective Data Loss Prevention is essential for organizations to protect data, especially sensitive information like personally identifiable information (PII) and financial data. However, there are several challenges in achieving this. Here are the top 5 DLP challenges and strategies to overcome them:
1. Identifying sensitive data
Challenge: One of the biggest hurdles is accurately identifying sensitive data, such as PII, business-critical data, and financial information, which need protection.
Recommendations: You can implement automated DLP tools that utilize machine learning to analyze and classify data. These tools can be trained to recognize various forms of sensitive data, enhancing data visibility and ensuring that the correct data is protected.
2. Balancing data access and security
Challenge: Ensuring employees have the necessary access to company data while preventing unauthorized users from accessing sensitive information.
Recommendations: Some DLP solutions offer granular data access controls. You can implement role-based access policies and regularly audit access logs to ensure that only authorized personnel have access to sensitive data. This can lead to balanced operational efficiency and security.
3. Monitoring data across diverse environments
Challenge: With data spread across cloud repositories, consumer cloud storage services, and on-premises servers, tracking data movement and storage becomes complex.
Recommendations: Consider deploying a DLP software that offers comprehensive coverage across all platforms where data is stored or processed. You should also ensure that these tools can monitor data transfer and storage in real time. They should also provide visibility into where data is stored, how it’s being used, and who is accessing it.
4. Compliance and auditing requirements
Challenge: Keeping up with various regulatory compliance standards like the General Data Protection Regulation (GDPR) requires strict control over how data is handled.
Recommendations:
- An AI-powered DLP tool designed to aid in compliance can significantly enhance this process. The tool should have features for encrypting data, generating detailed reports for auditing, and ensuring that handling of confidential and critical information aligns with regulatory requirements.
- It is also important to regularly train your staff on compliance requirements and the importance of data protection. This ensures that everyone understands their role in maintaining compliance.
5. Protecting against insider threats:
Challenge: Insider threats, where employees or associates misuse access to sensitive data, pose a significant risk.
Recommendations:
- You can implement strict access controls and divide responsibilities among employees, along with conducting thorough background checks of new hires.
- It is also essential to enhance physical security measures, maintain a positive work environment, and establish clear procedures for both ongoing audits and the management of departing employees.
HIPAA Compliance and DLP
HIPAA places extensive data security requirements on businesses that have access to, process, and store protected health information. DLP is vital for organizations that need to comply with HIPAA.
DLP solutions can help organizations identify, classify, and tag data that is covered by regulations.
Case Study: Riverside Regional Medical Center Challenge: Medical staff were inadvertently sharing patient files via personal email and cloud storage services, creating potential HIPAA violations.5
Solution Implemented:
- Set up cloud DLP integration with Office 365.
- Deployed endpoint DLP on all workstations and mobile devices.
- Configured content inspection rules to identify PHI (Social Security numbers, medical record numbers, patient names).
- Implemented email DLP to scan outbound communications.
Further reading
- Top 10 Network Security Policy Management Solutions (NSPM)
- Top 10 Firewall Audit Software & Review-based Analysis
If you have questions, we would like to help:
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Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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