What is data loss prevention (DLP)?
Data Loss Prevention (DLP) is a strategy put in place by security organizations that prevents the leaking and potentially malicious exfiltration of secure data. According to the Information Systems Audit and Control Association (ISACA), implementing a robust DLP solution is crucial for detecting and preventing unauthorized data leakage and sharing, thus safeguarding sensitive information.
The organization goes on to say that it’s important to know locations where data exists, along with an indication of the functional areas of where to implement or enhance applicable security and privacy controls.
Types of Data Loss Prevention
DLP solutions generally fall into three main categories based on where data is monitored and protected.
Endpoint DLP
Focuses primarily on monitoring network endpoint devices. It enables security teams to specify data that they may consider sensitive and therefore enact policies that bar that particular data from leaving the endpoint.
Network DLP
Zooms out and looks at data traveling over internal and external, cloud-based networks. When putting a network DLP strategy in place, it’s imperative to understand network protocols at a deeper level so as to avoid potential misconfiguration.
Cloud DLP
Monitors data going to and from the cloud, as it is in an especially precarious position for malicious exfiltration, once an attacker has breached a network. A security operations center (SOC) would be wise to automate much of the data leakage discovery as well as reactive DLP protocols to a potential breach.
Why is data loss prevention important?
Implementing a Data Loss Prevention (DLP) strategy is a core element of an organization's broader data security efforts—helping protect sensitive data, maintain customer trust, avoid financial loss, and meet regulatory obligations. DLP helps organizations detect when and where data is leaving and entering their networks—enabling faster, more effective protection against accidental leaks and targeted attacks.
Internal data leakage
While some internal data leaks are malicious, most result from human error. Employees may unknowingly expose data by falling for phishing attacks, using weak or reused passwords, or sending sensitive files over unsecured channels like email or messaging apps. Even granting network access to supply chain partners or third-party vendors can open up vulnerabilities if not tightly controlled.
These unintentional actions can have serious consequences, especially when they involve personally identifiable information (PII) or intellectual property. A well-configured DLP solution can help detect these risky behaviors in real time and apply automated protections—preserving data integrity and aligning with zero trust security principles by assuming no user or device is inherently trustworthy without continuous validation.
External threats
Malicious external actors actively target sensitive data for financial gain, often through ransomware attacks or data theft for resale on the dark web. These attackers may exploit network vulnerabilities, phishing tactics, or previously undetected malware to gain access to internal systems.
In such cases, DLP is a valuable line of defense—monitoring data movement, flagging unusual access patterns, and blocking exfiltration attempts. Preventing unauthorized access not only protects your data but also reduces the likelihood of reputational damage or costly recovery efforts.
Cloud communication risks
Modern businesses rely heavily on cloud-based tools and workflows, increasing the risk of data leakage across cloud environments. Sensitive data is transmitted between users, services, and platforms multiple times daily—often without full visibility from IT or security teams.
For example, a finance department might unknowingly share customer or financial records through unsecured messaging platforms or misconfigured SaaS tools. Without DLP in place, this data could leave your organization unmonitored and unprotected. A cloud-aware DLP solution helps monitor data in motion, enforce encryption or blocking policies, and enhance your cloud security posture by reducing the risk of exposure in hybrid or multi-cloud environments.
For these key reasons, it’s critical a DLP solution is able to detect when and where data is leaving and entering networks and help analysts prioritize protecting data that may be more sensitive than other data—contributing to stronger exposure management by identifying and reducing the highest-risk data flows.
Causes of data leakage
Let's take a look at some of the top reasons data at rest or in transit might "leak" off of endpoints, systems, and networks and into the hands of bad actors.
Human error and honest mistakes
As we referenced above, company employees can also be offenders, unknowingly leaving data vulnerable in one way or another, and ultimately allowing it to leak into the hands of attackers. This could be the result of becoming an unwitting victim of a phishing campaign, reusing passwords or using unsophisticated passwords, or granting internal network access to supply chain partners or outside vendors.
Malware and ransomware
Attackers could have delivered malware designed to exploit a network vulnerability months ago – and had the luxury of not being discovered. In this scenario, they have the time to cherry-pick the data they wish to exfiltrate, and deliver a ransom demand for that data. And keep in mind that it might not end there; increasingly attackers are dipping into double-extortion strategies so they can try to extract the most money possible for their efforts.
Retaining outdated or archived data
Whether intentional or not – and if not, archived data should be stored as offline backups – maintaining data that has aged out of its usefulness can be a potential source of data leakage and a big time vulnerability. Even if the data is no longer useful to the security organization or company, it can still be very useful to bad actors. If an attacker manages to gain access to an endpoint, system, or network, archived data – such as old credentials or past emails containing sensitive information – could be exactly what they need to carry out an attack.
Cloud misconfigurations
This can also be attributable to human error, but if critical operations are, well, operating on misconfigured – and therefore inherently flawed – cloud infrastructure, then that data is exposed and therefore potentially “leaking” into multiple places like the public-facing internet or third-party servers.
Benefits of implementing a DLP solution
The benefits of a DLP solution are clear and add up to the ability to better secure data from inadvertent exposure and theft. Let's break down a few key benefits and how they specifically affect a network.
Improve visibility across devices and networks
The ability to monitor network endpoint devices and analyze traffic and interactions for suspicious activity will accelerate visibility of an overall environment and improve security posture. Monitoring a network for data loss can also help to eliminate previously unseen blindspots – internally and among devices connecting to a network – that were just waiting to be exploited.
Strengthen access controls with IAM
Identity and access management (IAM) is critical for a DLP solution and network security in general. IAM helps to ensure the right people are accessing the right endpoints or network systems. By instituting IAM policies on critical systems and endpoints, the network perimeter becomes harder to breach, which in turn can help the business remain in compliance with both internal and external regulatory standards.
Streamline data classification and labeling
Data classification should be as simple and straightforward as possible. Let's look at a tiered-structure example:
- Level 1: This is data for public consumption and that may be freely disclosed.
- Level 2: This is internal data not for public disclosure.
- Level 3: This is sensitive internal data that – if disclosed – could affect the company in a negative way.
- Level 4: This is highly sensitive corporate, employee, and customer data.
Based on this classification, it’s clear that storing the wrong data at the wrong level, or classification, could have potentially disastrous effects. If there is a situation where data of different classification levels must reside on the same server, intermixed data should be labeled and classified using the highest classification rating and thus protected accordingly. Automating this process will also help to ensure it occurs with efficiency and speed.
Data loss prevention best practices
Implementing best practices for a DLP solution will help to calibrate it to a specific environment. According to ISACA, there are many best practices that will help to ensure a DLP strategy is deployed successfully:
People
- Do not leave sensitive data unattended.
- Do not permit copying of sensitive data onto removable media.
- Provide view-only access to sensitive information.
Management
- Implement a data management life cycle to organize data and manage storage and use.
- Regularly update data risk profiles to be aware of new threats.
- Standardize the endpoints to make deployment more manageable.
Deployment
- Deploy DLP in prioritized waves for quick-wins.
- Start with a minimal base to handle false-positives, help identify the critical or sensitive data, and fine-tune DLP policies.
- Test implementation in a small, controlled unit before going full scale.
IT-restrictive controls
- Do not allow unauthorized devices in the network.
- Block files containing personally identifiable information (PII).
- Perform DLP discovery scanning at a desired frequency (or on demand) to audit and maintain awareness of the security status.
Product selection
- Check the DLP product to see if it supports the enterprise's data formats.
- Scan data stores for sensitive information and, if necessary, take remedial action.
- Use the DLP tool to automatically find unencrypted sensitive data, encrypt the information (Data Encryption), and remove the information or perform another remediation according to the enterprise's policies.