Data Loss Prevention: What It Is, How It Works, and Why It’s Crucial Today

According to IBM, the global average cost of a data breach is an eye-watering $4.35 million. In the United States, where the cost of a data breach is the highest in the world, the toll soars to $9.44 million.

That doesn’t include the damage to a company’s reputation or fallout from increased government scrutiny. Often, it may lead to the resignation of top executives. A National Cyber Security Alliance survey found that 10% of companies that suffered a data breach went out of business. Of those that didn’t, 25% filed for bankruptcy.

What stands between your company and a multimillion-dollar crisis is your first line of defense: data loss prevention.

What is Data Loss Prevention (DLP)?

Data loss prevention is a comprehensive set of processes, tools, and best practices designed to protect sensitive data. Data loss prevention aims to prevent the accidental or illicit transfer of data outside of an organization. It also helps mitigate the unintentional deletion of sensitive data.

DLP prevents unauthorized users from accessing or moving data. By ensuring that sensitive or critical data isn’t shared or sent outside the protective boundaries of the network, DLP mitigates the threats of data loss and data leakage incidents.

It also prevents authorized users from accidentally sharing data that could put the organization at risk. For example, an employee might attempt to upload a sensitive business document to an unregulated cloud storage service such as Dropbox. Data loss prevention protocol would deny permission for the upload. As a result, sensitive information remains protected.

DLP automatically classifies different content types, applies protective policies, and uses encryption where appropriate. These defenses can include other protective actions, such as issuing alerts when detecting suspicious activity.

Data loss prevention or data leakage prevention are sometimes referred to by other terms, such as information loss prevention or exfiltration prevention. No matter what it’s called, the end purpose stays the same: to prevent the misuse, loss, or dissemination of important information.

How does Data Loss Prevention work?

Sensitive data is constantly at risk of leakage or loss from internal and external hazards. An effective DLP solution is designed to protect your data against the three most common types of threats:

1. Internal threats. This threat may originate from an actor who has gained access to a compromised user account with substantial privileges. They may attempt to transfer sensitive data outside the organization, resulting in a data breach. But this type of threat could also take the form of an actor inside the organization, such as an employee with malicious intent.
2. External threats. Many cyber attacks use techniques such as malware or phishing to penetrate an organization’s security perimeter. They exploit security vulnerabilities and gain access to sensitive data, resulting in a breach.
3. Accidental threats. Not all data loss is a result of malicious intent. Sometimes, sensitive data can be leaked as a result of an accident. For example, an employee may lose a device containing sensitive data in a public place. Or they might unintentionally upload sensitive information to the internet. Or they may forget to follow specific security policies, inadvertently causing a breach.

6 Requirements of Data Loss Prevention systems:

It’s important to remember that DLP is not one specific tool but rather an overall process. It combines various technologies and strategies that all work together to mitigate risks. A complete and reliable DLP solution should protect your data at every point of vulnerability:

• Identifying sensitive data. Different kinds of data require different levels of protection. DLP can only safeguard sensitive information if it knows how to identify what’s important and what’s not. It is possible to apply rules and metadata manually, but that is more likely to lead to avoidable mistakes and security gaps. Identifying sensitive data using an automated data discovery classification tool is more efficient and effective.
• Protecting data in motion. DLP analyzes incoming and outgoing traffic at the edge of the network. If it detects an occurrence of sensitive data sent in violation of a security policy, it can intervene. DLP helps stop external data breaches by preventing the data from being routed someplace it doesn’t belong.
• Protecting data in use. This DLP technique involves monitoring users’ activities with data, either intentionally or unintentionally. Unauthorized activities can be stopped or flagged to alert network security staff immediately.
• Protecting data at rest. Data requires protection not only when it’s moved around but also while it is stored. Data must be secured everywhere it resides. That includes databases, cloud storage, individual computers, mobile devices, apps, and other types of storage. DLP manages encryption, controls access, and administers data retention policies to safeguard sensitive information.
• Protecting endpoints. Individual endpoints such as computers, mobile phones, and tablets need to be secured by DLP. Data transfer between legitimate users, groups, or external parties can be protected by encryption. The system also blocks transfers to illegitimate users in real-time.
• Detecting data leaks. This type of DLP functionality involves first establishing a normal activity baseline. Then it stays vigilant for anomalies. It may immediately stop unusual or suspicious data transfers and alert network security staff to the possibility of a data leak.

Why is Data Loss Prevention important?

Data loss can damage a company’s reputation and lead to fines or even criminal penalties. Considering that one in four companies declare bankruptcy after a data breach and a significant number go out of business, the importance of data loss prevention cannot be overstated. Today more than ever, your organization must make every effort to stay in control of its data.

Data loss prevention is key to protecting sensitive data, complying with regulations, and serving as an essential tool in detecting security weaknesses.

Benefits of Data Loss Prevention

• Protects the Personally Identifiable Information (PII) on file to keep the organization in compliance with government regulations.
• Secures valuable Intellectual Property that is critical to the organization’s business interests.
• Monitors remote cloud storage servers to mitigate risks and keep data secure.
• Enforces security in BYOD (Bring Your Own Device) environments and keeps the mobile workforce secured.
• Provides data visibility across large organizations, which helps break down silos so that teams can achieve more.

The more seamlessly your technology and processes work together, the better they can safeguard your organization’s data. To achieve the best possible prevention, following these data loss prevention best practices is essential.

Top 10 Data Loss Prevention best practices

More than 35% of data loss prevention implementations fail, according to a Gartner study. Inconsistent policies, undefined objectives, and lack of clarity about responsibilities can all hamper DLP efforts. To avoid costly missteps and downtime, follow these best practices for deploying a DLP solution.

1. Define your objectives. Are you primarily focused on protecting PII (Personally Identifiable Information), meeting regulatory compliance, or gaining more visibility into your data? Creating a smooth deployment process begins with determining your main objective.
2. Specify your security requirements. Your cybersecurity standards and compliance requirements should inform how your data needs to be monitored and protected. That dictates the way your DLP solution will be deployed.
3. Establish roles and responsibilities. Determine who will be responsible for what in your organization’s DLP initiative. Define what their duties will be. This provides valuable checks and balances to keep everything running smoothly.
4. Inventory and assess your data. It’s only possible to protect your data if you know exactly what it is and where it is. That requires a comprehensive data inventory and assessment of your infrastructure. Start with an inspection of your most sensitive or valuable data, which is likely to be a target.
5. Classify your data. Different kinds of data require various levels of protection. PCI, PII, financial data, regulatory data, intellectual property, and other types of sensitive data have different requirements. This necessitates a data classification framework for both structured and unstructured data.
6. Define consistent DLP policies. Create policies for handling data, starting with data regulated by strict regulations—for example, GDPR in Europe and CCPA in California.
7. Train your team. Accidental data breaches from inside your organization can be just as damaging as external threats. Educate your employees on how their actions can result in data loss. Consistent training reduces the risk of accidents.
8. Aim for a quick win. Instead of trying to fix everything all at once, focus your initial efforts. Start with a simpler, less ambitious rollout that is more likely to achieve highly visible results. This pilot program can lay the groundwork for future expansion.
9. Document your process. Ensure clear communication across your organization. Document changes to the environment, new procedures to follow, what to review and when, and so on. This documentation has multiple benefits. It helps with onboarding, keeps staff updated, and minimizes mistakes that could weaken your DLP efforts.
10. Define your metrics for success. What will be the key performance indicators you use to measure the success of your DLP program? How will you determine areas that need improvement? Establish these metrics, monitor them, and share the results with leaders across your organization.
    In addition to those top 10 best practices for data loss prevention, there’s one additional step your organization should take to ensure the security of your data:
11. Regularly test your defenses. The difference between having an incident response plan in place and actually testing it can be significant. According to an IBM report, companies that tested their techniques before suffering a data breach saved an average of $2.66 million compared to companies that didn’t test.