DLP is a data security strategy that allows the monitoring, management, and enforcement of data protection policies to ensure data security and compliance. As one of the core components of a holistic cybersecurity strategy, DLP helps organizations prevent unintended data loss, destruction, exposure, and misuse.
In this blog, we will discuss in detail what DLP is, the critical role it plays in the broader data protection landscape, and the best practices to effectively leverage it for enhanced data security.
What is Data Loss Prevention (DLP)?
DLP, which stands for Data Loss Prevention, is a critical tool in a cybersecurity team’s tech stack. It safeguards their sensitive data against unauthorized access, data leakage, or loss, which could occur due to a number of reasons. For instance, human error is one of the most common causes of unintended data exposure, usually resulting from cybersecurity incidents like phishing attacks or social engineering.
In the words of Gartner, DLP is “a technical control designed to prevent data loss in order to comply with personal data regulations, prevent unintended disclosure, minimize insider risk, and ensure that sensitive data is not overly accessible.”
A typical DLP solution is built to track and protect data in endpoints and networks across on-premise environments. However, modern DLP solutions go beyond the on-prem networks to protect structured and unstructured data in cloud data storage as well as SaaS environments.
Why is Data Loss Prevention Important?
The fear of the next big data breach keeps 77% of CISOs awake at night, worried about the security of their job. Their concerns are justified since the cost of a data breach now clocks at a whopping $4.44 million, as reported in IBM’s Cost of a Data Breach Report 2025.
Safeguarding data has become twice as challenging as it was a decade ago. For starters, data has completely outgrown the traditional on-prem environment. Now, it exists in cloud data storage, data lakes, data warehouses, and SaaS environments. Studies reveal that 31% of the enterprises globally use four or more cloud infrastructures for their operational needs.
The concerning aspect of the cloud infrastructures is that they are often just as vulnerable as traditional, on-premise environments. In fact, IBM’s 2024 report reveals that 82% of the data breaches involved cloud data storage.
If that wasn’t enough, the accelerated adoption of generative AIs, or large language models (LLMs), has introduced a completely unique set of risks and challenges. For instance, as LLMs require volumes of data for training and fine-tuning, they have become a prime target for threat actors seeking to exploit vulnerabilities like model poisoning, sensitive data exposure, unauthorized access, etc.
Check Out Infographics: The OWASP Top 10 Risks for LLMs
Beyond the risks and challenges discussed above, often the real damage comes down to the type of data that a breach affects. Personally identifiable information (PII), in particular, remains the most valuable type of data that is mostly targeted in data breaches. In fact, 53% of all the data breaches target customer PII. The damages of a breached PII echo beyond the data security sphere, exposing enterprises to serious compliance risks.
Data loss prevention tools are designed to address the data security risks enterprises face across all stages of data, i.e., at rest, in use, and in motion. DLP gives enterprises the much-needed visibility into where the data is located, how it moves from endpoint to endpoint or networks, and how it is accessed. With appropriate DLP policies and controls, enterprises can effectively prevent data breaches and thus data loss.
What Are the Benefits of DLP?
Let’s now explore some of the critical benefits that organizations can reap by implementing DLP solutions.
Enhanced Data Awareness for Teams
Having deep visibility of data is a foundational step for effective data management, governance, security, and compliance. Teams need to know where their data is located across endpoints, how it moves via networks, and its access usage. DLP provides that visibility, allowing teams to create effective policies and implement the right controls.
Automated Data Classification
Data classification plays a critical role in the overall data security strategy. A DLP software or a solution helps automate data classification, categorizing data based on a number of aspects. For instance, data can be classified according to its usage context, such as for general business purposes, as intellectual property, or for financial transactions. More importantly, classification is based on the sensitivity level of the data, its regulatory context, and business value.
Organizations use these insights to understand the type of data they deal with and thus implement effective controls to prevent unauthorized access or transfer.
Data Exfiltration Detection & Prevention
DLP can further enable organizations to identify and block data exfiltration that could lead to data loss or misuse. DLP helps monitor the flow of data to endpoints and networks, allowing security teams to encrypt or block unauthorized transfer of data, such as emails, USB transfers, etc.
Compliance Assurance
Data protection laws, such as the CPRA, GDPR, SOX Act, or HIPAA, require data controllers, such as enterprises, to ensure appropriate controls are in place to prevent unauthorized access, data destruction, and misuse. Non-compliance with any such laws may result in hundreds of thousands of dollars in fines.
DLP helps both the security and privacy teams in any organization to not only ensure data protection but also the appropriate handling of data according to global data privacy and protection laws. The ability to keep track of all the data, monitor its flow, and report on data helps streamline audit and compliance.
Types of DLP Technologies
There are different types of DLP solutions for different environments. Each solution focuses on a different method, depending on where the data lives or is transmitted. Typically, DLP tools are available for the following deployments.
DLP For Networks
Network DLP solutions monitor and control data movement across internal systems as well as data going to external networks. Policies and controls are enforced to keep sensitive data from being transferred to unauthorized systems, such as via emails, file transfers, etc.
DLP For Endpoints
DLP for endpoints monitors and protects data that resides in user devices on a network. Endpoint devices are the most vulnerable when it comes to sensitive data protection since most users interact with data on their workstations. DLP for endpoints can block the copying, pasting, or transferring of files, preventing sensitive data exposure.
DLP For Cloud
Cloud DLP solutions track and protect data residing in cloud environments, such as cloud data storage, data lakes, or SaaS applications. The solution tracks data transfers and usage across the cloud resources, preventing unauthorized access.
Main Causes of Data Leakage
Data loss or data leakage can occur due to a number of factors. For instance, a complicated ransomware attack could also lead to data loss. Regardless, here are some of the most common causes of data loss that organizations need to be aware of.
Phishing or Social Engineering
Phishing or social engineering attacks hit the weakest link in the cybersecurity ecosystem: humans. Lack of cybersecurity awareness or simple negligence can cost enterprises their valuable data as well as their business reputation. In fact, phishing remains one of the most common and costliest attacks, causing enterprises an average loss of $4.8 million per breach.
Insider Threats
Insider threat is yet another common attack vector that hits enterprises globally every year. To put things into perspective, it is the top-most expensive cyber threat that costs companies an average of $4.9 million per breach. These attacks can occur due to a simple mistake by an employee, contractor, or stakeholder or malicious intent.
Malware & Security Misconfigurations
There’s a long list of cyberattacks a threat actor could use to breach an enterprise’s cyber defenses and breach data. As discussed above, what they need is a small gap in the overall cybersecurity. For instance, ransomware could render a company unable to access its data. If the enterprise lacks any backup of such data, it would either have to pay the ransom to recover the data or lose it altogether. Similarly, vulnerabilities like cloud security misconfigurations, like exposed public buckets, could also lead to sensitive data exposure and loss.
Supply Chain Attacks
Enterprises lacking robust supply chain security controls can still suffer sensitive data loss if third-party vendors are hit by cyberattacks. A vulnerability in the software code or a breach could cost an enterprise not only data loss but also downtime. In 2024, Cybersecurity Venture predicted the global annual cost of supply chain attacks to reach a whopping $138 billion by 2031.
5 Strategies & Best Practices For Effective Data Loss Prevention
Deploying DLP just isn’t enough to protect data against unintended leakage, loss, or exposure. In fact, enterprises need to keep improving their DLP policies and practices to keep up with evolving data security threats and regulatory requirements.
Define a Strong DLP Policy Foundation
Organizations first need to have complete awareness of their data. Keep that as a starting point, and move from there to formulating the DLP policy around sensitive data access. Define who can access what data, determine policies for the transfer of data to authorized and unauthorized destinations, and create remediation policies when a violation occurs.
Identify Your Most Valuable Data
To formulate a robust DLP policy framework, first identify all your structured and unstructured data. Identify the type of data your enterprise collects, such as PHI, financial data, customer data, or intellectual property data. Categorize the data based on its sensitivity. This will allow teams to prioritize their efforts to protect the data that is valuable to the enterprise.
Monitor Who Accesses Your Data
Data access monitoring is one of the core provisions of many critical data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and the Payment Card Industry Data Security Standard (PCI DSS). It not only helps keep track of who accessed the data but also how the data is accessed and used. There are a number of ways enterprises can monitor data access. For instance, security teams can review the access logs to investigate how many access attempts have been made and how often. Teams can further enhance the principle of least privilege (PoLP) by reviewing how often a dataset is accessed and revoking users or identities that haven’t accessed it for a long time.
Implement Access Security Controls
Access controls can comprise a number of tools and techniques that allow security teams to restrict access to only authorized users- people who can do their job without interruption. Access controls can be as simple as setting up password-protected accounts or requiring multi-factor authentication, and they can be as complex as restricting certain users or job roles, or dynamically masking data so that unauthorized users can’t see sensitive data.
Maximize the Impact of DLP
It is critically important to see DLP not as a standalone solution but as a core part of the wider data security framework in any organization. For instance, combining DLP with a data security posture management (DSPM) solution can significantly enhance the capabilities of a DLP solution. For instance, a DSPM solution can potentially reduce the false positives or negatives that often flood security teams, rendering them unable to effectively prioritize security.
Learn More About: The Risks of Legacy DLP
Strengthening Data Protection with Continuous DLP
Understanding what DLP is and its benefits is important. However, what matters most is how an enterprise implements it into its broader data security framework and refines it continuously. Enterprises must embrace the multicloud realities or how they can integrate related technologies like DSPM to reduce the alert fatigue that often overwhelms security teams, affecting their decision-making and the ability to effectively prevent data breaches.
