Data loss prevention

Data loss prevention is a package of processes and tools designed to see that critical information is not accessed, misused, or lost by unauthorized users. Data loss prevention software, typically informed by regulatory compliance such as the General Data Protection Regulations (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), or the Payment Card Industry Data Security Standard (PCI-DSS), classifies data as regulated, confidential, or business critical and then defines policy violations defined by the organization itself and applicable guidance. Should it identify a violation, this kind of DLP data loss prevention software then enforces remediation, typically with protective actions such as encryption and alerts.

Data loss prevention (DLP) tools and software filter data streams on networks, control and monitor endpoint activities, and monitor data in the cloud. In this way, various DLP tools protect data in use, data in motion, and data at rest. Data loss prevention programs also feature reporting, which aids both in identifying anomalies and problems for forensic response and in meeting audit and routine compliance requirements.

There is a wide array of data loss prevention (DLP) solutions available today. This is primarily because of the many ways confidential data may exist. Information lives in many locations, such as databases, flash drives, file servers, mobile devices, PCs, physical servers, point-of-sale devices (POS), and virtual servers. Various network access points for data to move through also exist, including VPNs and wireless, so there are many ways to take on the issues of data leakage and loss.

• Data discovery and classification. Find and tag structured and unstructured sensitive data automatically (PII, PHI, PCI, IP).

• Content inspection and policy engine. Apply regex, ML classifiers, and context rules to detect sensitive content and intent.

• Endpoints and agent controls. Enforce policies on laptops, mobile, POS devices (block USB copy, limit printing, remote wipe).

• Network / edge DLP. Monitor data in motion across email, web, SaaS, and SSL/TLS channels.

• Storage/datacenter DLP. Scan data at rest in file servers, databases, and cloud stores and enforce access controls and encryption.

• Cloud and SaaS DLP. Protect data stored in cloud apps and collaboration platforms with API integrations and CASB-style monitoring.

• Behavioral analytics and anomaly detection. Find unusual access patterns and insider threats by correlating telemetry.

• Reporting, forensics, and compliance support. Keep auditable logs, incident data, and automated reports for GDPR, HIPAA, and PCI audits.

• Protect customer PII/PHI for compliance.

• Prevent exfiltration of intellectual property and trade secrets.

• Secure BYOD and remote work scenarios.

• Prevent data leakage from SaaS apps and cloud storage.

• Aid forensic investigations and breach response.

1. Encrypt data at rest and in transit. Ensure unauthorized copies are unusable.
2. Apply content inspection + contextual policies. Block actions that violate policy, alert admins, and educate users.
3. Enforce least-privilege access and RBAC. Limit who can copy, share, or export sensitive data.
4. Deploy endpoint agents and network DLP sensors. Monitor both local devices and traffic crossing your network.
5. Prioritize and classify critical data. Start with the data that would do the most harm if lost.
6. Test and measure with KPIs. Track policy hits, false positives, time to respond, and incident outcomes.

Prioritize data so that your data loss prevention implementation strategy starts with the information that is most sensitive or valuable if it is lost or stolen.

Classify the data based on context, such as the user who created the data, where the data is stored, or the source application. This also allows for data tracking through use of persistent classification tags. Content inspection for regular expressions, such as credit card information or keywords, often runs according to protocols for personal identifiable information and other regulatory standards.

Understand which data is at risk, when, by assessing risk at each point of data distribution. As information travels between customers, partners, and user devices along the supply chain, it is typically at greatest risk while in use on endpoints.

Monitor data in motion to comprehend how users deploy data and which behavior places data at risk to determine the scope of the data loss prevention strategy.

Provide guidance and training continuously to reduce the risk of negligent data loss by insiders. In addition to blocking risky activities, advanced data loss prevention products educate employees of risky and potentially violative data use.

Fundamental information security tools can protect against data loss and data leakage to some extent. For example, a firewall can stop unauthorized parties from accessing systems that store sensitive data. Antivirus and anti-malware software and intrusion detection systems (IDS) can also protect systems from attackers.

More mature or advanced security measures for detecting irregular data access may also be appropriate for some organizations, including data integrity controls, honeypots, network traffic analyzers, security machine learning, and user identity checks or activity-based verification.

However, especially for larger businesses, designated data loss prevention solutions may be best to safeguard your data. These tools are specifically designed to prevent attempts to transmit or copy sensitive data to unauthorized locations, whether intentional or not.

Network-based DLP solutions are installed at the perimeter of enterprise networks to protect data in motion. Their analysis engines monitor network traffic including email, instant messaging, SSL traffic, social media interactions, and web 2.0 applications, to detect violations of set information disclosure policies, such as the sending of sensitive data.

Data center or storage-based data loss prevention solutions protect data at rest within the company's data center infrastructure, such as databases, file servers, and collaboration tools like Microsoft 365 or Google Workspace. These data loss prevention tools locate confidential data and help users determine whether it's secure.

DLP for Endpoints monitors devices such as laptops, Point-of-Sale (POS), smart phones, and tablets for all data transferring actions such as printing, downloading, copying, or transferring to CD/DVD, social media, USB, or webmail. These data loss prevention tools may be configured to actively block specific activities, or configured only for passive monitoring.

Content-aware data loss prevention tools reduce the risk of accidental exposure and protect sensitive data outside authorized channels. These tools help prevent data leaks by monitoring, blocking, and remediating based on company policies that classify content.

Druva provides features to help IT administrators maintain control over sensitive data on hybrid and cloud workloads, SaaS applications, and endpoints, and respond quickly when data is compromised, lost, or accidentally deleted. Druva’s approach integrates classification and endpoint enforcement (remote wipe, encryption, agent-based controls) with cloud-centric visibility so organizations can detect anomalous data movement and remediate incidents. For data protection at scale, Druva pairs these features with cloud-native, immutable backups and continuous telemetry to ensure recoverability and prevent reinfection during incident recovery.

Features include:

• Endpoint controls: Agents for file/port control, remote wipe, and encryption.
• Cloud security and visibility: Discover and monitor sensitive files in cloud storage and collaboration apps.
• Policy engine and content inspection: Combine rules, regex, and ML for accurate classification and prevention.
• Integration with security telemetry and recovery: Threat detection signals plus backup telemetry accelerate investigation and safe recovery (avoid reinfection).

What is data loss prevention (DLP)?

DLP is a combination of policies and technologies that discover, classify, monitor, and prevent the unauthorized sharing of sensitive data. 

What should a DLP program include?

Discovery & classification, endpoint and network controls, content inspection, encryption, behavioral analytics, and audit/reporting. 

What types of data does DLP protect?

PII/PHI, PCI, intellectual property, financial records, and other regulated or business-critical information. 

How does DLP differ from backups?

DLP prevents or detects data exfiltration; backups ensure you can restore lost or corrupted data. The two are complementary — DLP reduces leakage risk while resilient backups ensure recoverability. 

Can DLP stop insider threats?

DLP helps detect and block both malicious insiders and accidental leaks by enforcing policies and alerting on anomalous behavior. 

How does Druva support DLP and recovery?

Druva offers endpoint controls, remote wipe/encryption, cloud DLP visibility, and integrates telemetry with immutable cloud backups and recovery workflows to contain incidents and restore clean data.

Visit the security posture page of the Druva site to learn more about the key security features. Explore Druva’s capabilities in a free product tour to see firsthand how we enhance defense against ransomware and other threats.

Now that you’ve learned about data loss prevention, brush up on these related terms with Druva’s glossary:

• Cyber resilience

• Data retention policy

• Ransomware