Data flows in and out of organizations via multiple channels. Within these flows, data can be lost or stolen. The financial impact of suffering a data breach is high for companies, and it can also damage their reputation.1
Data loss prevention can help organizations reduce the risk of sensitive data leakages and unauthorized data transfers. This article dives into data loss prevention, what are the challenges of implementing it within our organization, and how business leaders can overcome those challenges.
What is data loss prevention (DLP)?
Data loss prevention or DLP, also known as data leak prevention, is the practice of detecting and preventing data leaks and unauthorized users from gaining access to sensitive business data. It involves various DLP solutions, tools, and strategies to protect sensitive data, including data identification, data visibility, and access restrictions.
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 data and store their data in many different places. 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.
2. External threats
Malware and other cyber attacks, like data exfiltration attempts, are common causes of data losses. 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
Weak or reused passwords can be easily guessed by attackers. 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.
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.
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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:2
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 right 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.
- An AI-powered DLP tool that is designed to help with compliance can make this process significantly efficient. The tool should have features for encrypting data, generating detailed reports for auditing, and ensuring that confidential and critical information handling 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.
- You can implement strict access controls and divide responsibilities among employees, along with conducting thorough background checks of new hires.
- It is also important to enhance physical security measures, maintain a positive work environment, and establish clear procedures for both ongoing audits and the management of departing employees.
To learn more about data loss prevention best practices.
What are the types of data loss prevention?
This section discusses the types of data loss prevention that organizations use to safeguard their sensitive and critical data. These strategies can be broadly categorized into 3 types:
1. Network DLP
It monitors all incoming and outgoing data on the organization’s network, including data streams and access to company data. Network DLP is effective when devices are connected to the given network. It allows security teams to monitor the flow of information in company networks to detect anomalies. Administrators can establish DLP policies and security policies to reduce data loss risks by analyzing network data.
2. Storage/Data-at-Rest DLP
This strategy specifically focuses on safeguarding critical data that is stored rather than in transit. Its main purpose is to protect sensitive data stored on servers, cloud storage, databases, and other storage systems. It ensures that only authorized users access critical data and detects potentially risky storage or transmission practices.
3. Endpoint management
An endpoint DLP solution monitors endpoint devices such as desktops, laptops, and mobile phones that store business-critical information. This type of DLP is crucial for securing data on corporate networks and maintaining regulatory compliance.
In addition to these types, organizations should also consider DLP adoption in areas like data storage, compliance audits, and securing structured and unstructured data. DLP software monitors and antivirus software play a vital role in protecting data and ensuring data protection requirements are met. Encrypting data, data classification, and implementing a robust DLP policy are also key elements in a successful DLP strategy.
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