Data isn’t just a collection of 1s and 0s — it’s the backbone of a healthy business, guiding decision-making, competitive strategy, and growth. Yet companies across the globe grapple with increasingly sophisticated cyberattacks, data leaks, ransomware attacks, and insider threats, making data loss prevention (DLP) efforts indispensable.

This article delves into what data loss prevention is, how it functions, DLP software solutions, and how to create a DLP strategy for stronger data security.

What is data loss prevention (DLP)?

Data loss prevention is all about protecting sensitive information from loss, corruption, misuse, or unauthorized access. This includes confidential information like customer data, financial statements, intellectual property, employee records, and other proprietary company information.

The benefits of data protection span legal, ethical, and business concerns. Here are several reasons for organizations to prioritize data loss prevention:

1. Improve data visibility: With the adoption of cloud computing and rise of big data, it’s growing more difficult to track confidential data. Specifically, Cloud DLP solutions give companies better visibility into how data is managed, stored, accessed, and used across the organization. 

2. Secure data in remote/BYOD environments: DLP software can enforce security policies and access controls for remote and BYOD workers, as well as ensure consistency in security practices across teams, departments, and locations. 

3. Protect Intellectual Property: Businesses rely on trade secrets, proprietary processes and technologies, and competitive data to innovate and maintain a competitive advantage. Having a DLP strategy in place can protect critical assets.

4. Promote Brand Reputation: Indirect costs of data breaches include a loss of customer trust, which can be incredibly difficult to restore. According to the Thales Consumer Digital Trust Index, over 20% of consumers stop using companies that have suffered a data breach. 

5. Prove Regulatory Compliance: Data privacy laws and regulations such as GDPR, CCPA, and HIPAA require organizations to protect personal data, and failure to comply can result in significant fines and legal action. DLP solutions include reporting features that can simplify the process of proving compliance.

6. Prevent Cyberattacks and Data Breaches: According to the IBM Cost of a Data Breach Report 2023, the global average cost of a data breach in 2023 is $4.45 million. Direct costs include fines, compensation, and the expense of rectifying the breach. DLP solutions can enforce data security best practices and quickly alert or remediate vulnerabilities, helping security teams prevent and respond to incidents.

How to implement a DLP strategy in 5 steps

A successful DLP strategy requires a holistic approach that integrates technology, policies, and people. You’ll need to understand what data you have, how it's used across your organization, and the risks it faces.

Here’s a step-by-step guide on how to get started building a data loss prevention strategy.

Step 1. Establish goals and success metrics

Is your primary goal to increase data visibility? Enable strategic decision-making? Reduce the number of security incidents? Simplify regulatory compliance?

Identifying a primary goal for your DLP strategy is the first step in gaining buy-in from key stakeholders and measuring the success of your efforts.

Step 2. Classify and prioritize data

What type of data would cause the biggest problems if it were lost, compromised, or stolen? Identifying critical data and its associated risks helps focus your DLP efforts on the most impactful areas.

Here are some typical data classification types:

• Personal Identifiable Information (PII): Data that can identify individuals, such as social security numbers, addresses, and birthdates.
• Protected Health Information (PHI): Health-related data that falls under regulations like HIPAA, including Medical record numbers, biometric data, health plan beneficiary numbers, etc.
• Payment Card Information (PCI): Payment card details including credit card numbers, names, and addresses.
• Intellectual Property: This includes patents, trade secrets, and research data.

Data is also typically classified in one of four categories based on sensitivity: 

• Public: Information that can be openly shared without any additional security protocols. Examples include company websites, promotional materials, and information about the company’s products or services. 
• Internal: While this data doesn’t include sensitive information that could harm the company if shared externally, it should not be shared outside the company. Examples include internal memos and communications and the employee handbook. 
• Confidential: Disclosure of this information could have a negative impact on the organization. Examples include vendor contracts, employee data and performance reviews, pricing policies, etc. 
• Restricted: Access to this data is even more tightly controlled than confidential information, and includes trade secrets, personally identifiable information, financial data, etc. Data access is typically granted only on a need-to-know basis or via NDA to minimize legal and compliance risk. 

Step 3. Monitor data and identify risks

Track and monitor how data flows through your networks, systems, and devices to understand when and where your data is most vulnerable.

With this knowledge, you’ll be able to understand any existing behavior that puts data at risk and develop effective policies and processes to mitigate data loss. You’ll also be able to pinpoint areas where unauthorized data access or data exfiltration is most likely to occur to inform your policies and controls.

Step 4. Develop policies and implement controls

Work with key stakeholders in your organization such as company leadership and department managers to develop policies and controls that mitigate specific risks. Be proactive about requesting feedback and monitoring controls so you can gauge effectiveness and improve your tactics.

Step 5. Train employees on DLP best practices

Human error accounts for 33% of all data loss incidents. Yet employees often don’t recognize or understand their role in accidental data loss.

Regularly training personnel on data loss prevention will arm your team with the knowledge and awareness they need to adhere to best practices and company policies. They’ll know how to handle sensitive data and recognize potential threats.

All training should include a review of your organization’s incident response plan. By establishing and clearly communicating procedures for reporting data security incidents, you’ll be able to address them faster and more effectively.

What is DLP software and how does it work?

Data loss prevention software is designed to automate and improve an organization’s DLP efforts. DLP solutions classify business data based on sensitivity and criticality and automatically detect violations against company policies and regulations such as GDPR, HIPAA, and PCI DSS. DLP software can alert organizations of any issues and automate encryption and other remediation actions to prevent end users from accidentally or intentionally sharing data or introducing risk to the organization. 

DLP tools also monitor and control endpoint devices and activities, such as copying data to USB drives, printing, or screen captures. They filter data streams on corporate networks and monitor cloud tools to protect data at rest, in use, and in motion. Reports can also help companies meet or prove compliance requirements and identify any gaps in their information security posture. 

There are three main types of data loss prevention tools:

• Endpoint DLP: Endpoint data loss prevention solutions monitor endpoint devices such as servers, computers, laptops, and mobile devices where data is stored and accessed. 
• Cloud DLP: Cloud data loss prevention solutions scan and audit data to automatically detect and encrypt sensitive information before it is admitted to and stored in the cloud. 
• Network DLP: Analyzes corporate network traffic to detect sensitive data in motion and ensure it’s not being sent in violation of information security policies or being routed somewhere it shouldn’t be. 

As businesses migrate to the cloud, cybersecurity becomes more complex and resource-intensive. Cloud DLP solutions help by:

• Extending on-premises protocols: Cloud DLP seamlessly extends the protection policies of on-premises data to cloud storage and tools. Companies can set specific rules for various data types, making security policies and processes highly tailored.
• Streamlining compliance: Cloud DLP solutions often come with built-in templates that align with global regulations, simplifying compliance.
• Real-time monitoring and remediation: As data is moved or accessed in the cloud, cloud DLP continuously monitors and secures it. DLP solutions provide real-time visibility and control over data at rest, data in use, and data in transit. It also tracks access to ensure proper permissions and prevent unauthorized users from accessing confidential data.
• Incident response and analysis: In the event of a cyberattack or data breach, DLP tools can trace leakages to inform investigations, shorten time to resolution, and improve mitigation tactics.
• Education and best practices: By flagging sensitive data transmissions, employees become more aware of data security policies and best practices.

Tips for choosing a cloud DLP solution

As with any software solution, it’s important to evaluate multiple vendors to find the right fit for your organization’s specific challenges and needs. Here's a list of key features and capabilities to look for when selecting DLP software providers:

• Automated data classification: Classifying data is the first step in deciding how to protect it. While some DLP software requires data to be pre-classified before it can enforce policies, modern solutions automatically classify data as it’s used and created across an organization. 
• Artificial intelligence and machine learning capabilities: AI algorithms and machine learning models can ingest and analyze massive amounts of data to identify user behavior trends and flag deviations, giving organizations an edge in identifying unauthorized access attempts and potential security threats.  
• Both pre-built and customizable policy packs: Out-of-the-box policy packs can simplify compliance with regulations and security frameworks, while customizable options allow you to tailor the tool to your unique needs. 
• Customizable policy enforcement: Look for a DLP solution that can automatically enforce your organization’s policies. For example, encrypting all sensitive data before it’s transmitted, or preventing users from transferring sensitive data onto USB drives. Also look for the ability to apply different policies to different user types and access roles. 
• Reporting capabilities: The best DLP solutions provide actionable insights that help you improve the way your organization protects its information assets. Reporting capabilities should include end-user policy violations, data access logs, and incident reporting. 
• Employee training: Incident-based employee training can provide end users with in-the-moment guidance and instruction around company policies and best practices. Look for a DLP tool that will flag violations and inform users about how they are breaking policies to improve awareness and data handling practices. 
• Ease of use and simple onboarding: Avoid overly complex and unintuitive tools. By choosing a solution that can be easily and quickly adopted across your organization, you’ll be able to roll it out quickly and start protecting your sensitive data faster. And because simple tools are more likely to be used by your personnel,  the software will be that much more effective. 

Our trusted cloud DLP partner Nightfall AI integrates with your cloud stack to automatically identify and remediate data exposure risks, without needing to install agents or proxies. 

Nightfall’s solution allows you to discover, classify, and protect payment card information, personally identifiable information, protected health information, secrets, credentials, and more—all from a single dashboard. Artificial intelligence-based detection tools like Nightfall can scan hundreds of file types for sensitive data while detecting and remediating that data in near-real time to improve your overall security posture and hygiene.

Paired with Secureframe, you’ll be able to: 

• Assess, identify, and remediate risk across your organization
• Simplify compliance with in-demand frameworks and regulations including SOC 2, ISO 27001, PCI DSS, HIPAA, and GDPR
• Harness the power of artificial intelligence and machine learning to automate your GRC and data loss prevention practices
• Educate employees on security policies and best practices
• Monitor your security and compliance posture in real time

Learn more about our partnership with Nightfall AI on our partners page, or schedule a demo to speak with a Secureframe product expert.