Quick answer: What is data loss prevention (DLP)? #
Data Loss Prevention (DLP) refers to a set of strategies, tools, and processes designed to detect and prevent the unauthorized access, transmission, or exposure of sensitive data. The goal is to safeguard data across endpoints, cloud platforms, and networks.
To implement DLP effectively, organizations must:
- Discover and classify sensitive data
- Monitor data in motion, at rest, and in use
- Enforce policies via encryption, masking, or blocking
- Set up role-based access and usage rules
- Integrate DLP with data governance and compliance workflows
- Maintain logs and reports for audits and investigations
Up next, explore the different types of DLP, top challenges in implementation, and how a metadata control plane like Atlan can enhance prevention strategies with real-time visibility and policy enforcement.
Table of contents #
- Data loss prevention explained
- Why does data loss prevention matter?
- What are the key regulatory violations that data loss prevention helps avoid?
- What are the core types of data loss prevention (DLP)?
- What are the key components of a modern data loss prevention (DLP) strategy?
- What are the biggest challenges in implementing data loss prevention?
- How a metadata control plane like Atlan strengthens data loss prevention
- Data loss prevention (DLP): Final thoughts
- Data loss prevention: Frequently asked questions (FAQs)
Data loss prevention explained #
Data loss prevention (DLP) is the practice of identifying, monitoring, and protecting sensitive data to prevent it from being leaked, misused, or accessed by unauthorized parties.
This includes structured and unstructured data—like credit card numbers, personal health records, or trade secrets—across endpoints, cloud environments, databases, and communication channels.
DLP sits at the intersection of data security, privacy, and regulatory compliance. Without clear DLP policies and enforcement, your organization risks violating laws like GDPR, HIPAA, or GLBA, leading to fines, reputational damage, and operational disruption.
In practice, effective DLP means:
- Identifying sensitive data and where it resides
- Tracking who’s accessing it, when, and how
- Defining rules for how it should (and shouldn’t) be used
- Automatically enforcing those rules in real time
Why does data loss prevention matter? #
In today’s data-driven world, organizations operate with sprawling data estates distributed across multiple clouds, teams, and third-party tools. Sensitive data isn’t confined to a single warehouse or application anymore, it constantly flows across environments, often invisibly, introducing serious risks of exposure, misuse, and non-compliance.
Imagine a typical modern data team workflow:
- Transactional data is stored in a cloud data warehouse like Snowflake.
- AWS S3 handles raw data ingestion and long-term storage.
- dbt transforms this raw data into clean, analytics-ready models.
- Business and product teams explore and make decisions using dashboards built in Looker.
- Soda runs automated data quality checks, flagging issues like missing values or unexpected anomalies.
- When issues are detected, Jira automatically creates tickets for the relevant teams to investigate.
- Meanwhile, conversations about these anomalies, ownership, and next steps happen in Slack, often involving data engineers, analysts, and product stakeholders.
Now imagine sensitive financial or customer data flowing through this pipeline. Without visibility into where that data is, who has access, or how it’s being handled across each touchpoint, you’re at risk of a breach or violation.
Data loss happens more often than many realize, and the costs are substantial. According to IBM’s 2023 Cost of a Data Breach Report, the average cost of a breach in 2024 is $4.88 million. This includes direct financial loss, remediation, legal expenses, and reputational damage.
This is exactly why data loss prevention matters. It lays the foundation for responsible data operations in modern, decentralized environments. Let’s quickly recap one of the most destructive and costly cyber-attacks in history.
NotPetya: When poor DLP costs hundreds of millions #
The NotPetya malware attack in 2017 began through a compromised software update from a Ukrainian accounting firm that lacked strong security and compliance controls.
In 2017, the NotPetya malware attack devastated global companies like Maersk, FedEx (TNT Express), and Merck. The malware spread through a compromised update from a Ukrainian accounting firm with lax security. It exploited the SMBv1 “EternalBlue” vulnerability—a known flaw that Microsoft had patched months earlier via security update MS17-010.
In the aftermath of the attack:
- Maersk had to reinstall 4,000 servers and 45,000 PCs, resulting in over $300 million in losses.
- FedEx (TNT Express) reported $400 million in damages due to service disruptions.
- Merck suffered extensive downtime, costing hundreds of millions of dollars.
NotPetya was a cyberattack, but at its core, it exposed a compliance failure—specifically, the absence of robust third-party risk controls and failure to enforce known security patches.
Strong DLP and data governance measures, involving vetting software vendors for patch management, data handling policies, and access controls, could have helped mitigate the level of damage suffered.
What are the key regulatory violations that data loss prevention helps avoid? #
Data loss prevention (DLP) plays a crucial role in avoiding costly regulatory violations by detecting, monitoring, and preventing the unauthorized access or transmission of sensitive data.
Here are some of the most important regulations where DLP helps mitigate compliance risks:
- General Data Protection Regulation (GDPR): EU law requiring protection of EU citizens’ data. DLP helps by flagging and blocking PII leaks and alerting compliance teams to potential breaches.
- Health Insurance Portability and Accountability Act (HIPAA): U.S. law for safeguarding health information (PHI). DLP can help identify PHI in unsecure channels, block external sharing, and log data access for auditability.
- Payment Card Industry Data Security Standard (PCI DSS): Governs the protection of cardholder data. DLP can help detect credit card numbers in unauthorized locations (e.g., Slack, email) and enforce encryption requirements.
- Gramm-Leach-Bliley Act (GLBA): Requires financial institutions to protect consumer data. DLP can help monitor for the presence of sensitive data across databases and file shares, and restrict unauthorized access or downloads.
- Federal Information Security Management Act (FISMA): U.S. law for protecting government systems and data. DLP can help monitor data usage and flag violations of agency-specific data protection policies.
- California Consumer Privacy Act (CCPA): Requires companies to protect consumer data and give users control over it. DLP can help block unauthorized sharing and support deletion workflows.
What are the core types of data loss prevention (DLP)? #
Data loss prevention (DLP) typically comes in three core types, each targeting different stages of the data lifecycle and risk surface:
- Network DLP: Monitors and protects data in motion—data being sent over the network (e.g., email, file transfers, web traffic). It prevents sensitive data from leaking through outbound communications or being intercepted during transmission.
- Endpoint DLP: Enforces DLP policies on devices like laptops, desktops, and mobile phones. It stops data loss from removable media (e.g., USB drives), screenshots, or unauthorized application usage.
- Storage or Data-at-Rest DLP: Scans and secures sensitive data stored in databases, cloud storage, file servers, and data warehouses. It helps you discover where sensitive data lives and whether it’s properly protected.
It’s vital to note that no single type of DLP is enough. To protect sensitive data end-to-end, especially in modern decentralized environments, you need all three—working together, driven by unified policy and visibility.
What are the key components of a modern data loss prevention (DLP) strategy? #
A modern data loss prevention (DLP) strategy embeds security into how data is discovered, classified, governed, and used. Core components include:
- Data discovery and classification: Automatically detect and classify sensitive data (like PII, PHI, financial records) across platforms—Snowflake, Databricks, Redshift, BigQuery, etc.–and apply appropriate tags or labels.
- Policy definition and enforcement: Create rules for how different data types should be accessed, shared, or restricted across tools. Additionally, ensure bi-directional policy sync with warehouses like Snowflake, so policies are enforced uniformly.
- Automated, column-level lineage mapping: Understand how sensitive data flows through your ecosystem. Visualize end-to-end lineage from ingestion (e.g., AWS S3) → transformation (e.g., dbt) → consumption (e.g., Looker/Tableau). Trace how fields are transformed, where sensitive data is used downstream, and what will break if something changes.
- Real-time monitoring and alerts: Track data activity in motion, at rest, and in use—triggering alerts for unusual behavior or violations.
- Access control, ownership and stewardship: Limit access based on roles or purpose, and monitor usage patterns for risky activity. Know who’s responsible for every dataset, dashboard, or column. Assign clear ownership so flagged issues don’t fall through the cracks. When risk is detected, notify the right steward or owner for immediate resolution.
- Data masking and encryption: Automatically protect sensitive data at query time or in storage using masking, tokenization, or encryption.
- Auditability and reporting: Maintain logs and generate reports that demonstrate compliance and support investigations.
- Metadata-driven automation: Use metadata to drive consistent classification, lineage, ownership tracking, and policy enforcement at scale.
- Human-centric security: A mature DLP strategy recognizes that people—not just systems—play a critical role in protecting data. In the recent Coinbase incident, attackers offered cash to a few insiders in exchange for copying data from customer support tools, impacting less than 1% of monthly users.
Up next, let’s explore the most common challenges in implementing DLP in complex, cloud-native data environments.
What are the biggest challenges in implementing data loss prevention? #
Despite its importance, DLP implementation can be difficult—especially for data leaders managing hybrid environments, legacy systems, and cross-functional data teams. Common roadblocks include:
- Data sprawl across cloud and on-prem systems: Sensitive data is scattered across decentralized data environments, making discovery and control inconsistent.
- Lack of unified visibility and metadata context: Without metadata-driven classification and lineage, it’s hard to track sensitive data movement.
- Inconsistent or missing classification: Without automated discovery and classification, sensitive data often goes unidentified or misclassified, exposing you to risk.
- False positives and alert fatigue: Overly rigid rules can trigger frequent alerts, overwhelming security teams and eroding trust in the system.
- Inconsistent policy enforcement: Policies that work in one system (e.g., Salesforce) may not apply cleanly in others (e.g., Snowflake or Slack), leaving gaps.
- Limited integration with the modern data stack: Traditional DLP solutions struggle to keep up with tools like dbt, Snowflake, Looker, and Slack—missing real-world data workflows.
- Shadow IT and third-party risk: Sensitive data often flows to SaaS tools or vendors without centralized control or oversight, increasing the risk of leaks.
These challenges make a strong case for embedding DLP into your broader data governance strategy—powered by active metadata and unified visibility.Next, we’ll explore how a metadata control plane like Atlan can help.
How a metadata control plane like Atlan strengthens data loss prevention #
Traditional DLP tools often operate in silos—focused on specific platforms, files, or networks—without understanding the broader data context. That’s where a metadata control plane comes in. It acts as connective tissue across your data ecosystem, surfacing deep context and enabling real-time, policy-driven controls.
Here’s how Atlan helps modern data teams scale DLP:
- Discover and classify sensitive data automatically: Atlan uses automated scanners, pattern matchers, and AI-powered classification to identify PII, PHI, financial data, and more—across structured and unstructured sources (Snowflake, BigQuery, data lakes, dbt, BI tools, etc.).
- Track end-to-end data lineage: Understand exactly where sensitive data originates, how it moves, and how it’s transformed—essential for tracing leaks and enforcing controls.
- Set dynamic, tag-based access policies: Define rules using business-friendly tags and apply them automatically across platforms like Snowflake, BigQuery, or Databricks, without writing custom code.
- Auto-ingest and sync metadata bi-directionally across the data stack: Enable consistent tagging and masking policies across all data tools while keeping business glossaries and metadata catalogs in sync.
- Map data ownership and usage: Link data assets to responsible users or teams, making it easier to assign accountability, control access, and meet audit requirements.
- Enable real-time impact analysis: Before a policy is changed or a field is shared externally, Atlan helps you evaluate the downstream impact—preventing accidental exposure.
- Integrate with your existing data workflows: Surface DLP context directly within Jira, Slack, Looker, or your BI tools—bringing compliance into daily decision-making without adding friction.
- Generate audit-ready documentation automatically: From policy lineage to usage logs, Atlan automates the creation of documentation (powered by AI) needed for compliance reviews and incident response.
- Centralize policy definition and monitoring in a policy center that ensures end-to-end coverage and real-time enforcement visibility across multiple platforms and teams.
- Maintain comprehensive audit trails and logs for compliance reporting and faster incident response, giving security, compliance, and data teams a single pane of glass.
Together, these capabilities turn passive DLP into an active, context-aware process embedded into your data fabric.
Case in point: How Austin Capital Bank simplified DLP with a metadata control plane #
Austin Capital Bank wanted to scale data access tracking, policy enforcement, and preparing for audits for its Snowflake assets.
“Inherent to Financial Services offerings is a large amount of sensitive customer data, including addresses, names, birth dates, social security numbers, and account numbers, all valuable to bad actors, and requiring careful stewardship and strict access controls.” - Ian Bass, Head of Data & Analytics
They wished to build an interface on top of Snowflake to easily track who has access to what. The bank adopted a unified metadata control plane to offer self-service to its data consumers without sacrificing strong access and masking policies.
By centralizing metadata and automating tag-based access controls, Austin Capital Bank turned what was once a manual, error-prone process into a scalable, audit-ready program that enables effective data loss prevention.
Data loss prevention (DLP): Final thoughts #
With regulations tightening, cyber threats increasing, and data environments growing more complex, Data Loss Prevention isn’t just a nice-to-have—it’s essential. While DLP is fundamentally a cybersecurity practice, it plays a crucial role in supporting data governance and privacy efforts.
For data leaders, the challenge is to balance protection with enabling teams to use data confidently and compliantly. The right tools can help by integrating DLP into everyday workflows, making it less about adding more steps and more about simplifying how sensitive data is managed across the organization.
Data loss prevention: Frequently asked questions (FAQs) #
1. What is the main goal of data loss prevention (DLP)? #
The primary goal of DLP is to prevent unauthorized access, transmission, or exposure of sensitive data—whether it’s in motion, at rest, or in use—while ensuring regulatory compliance and minimizing risk.
2. What kinds of data should DLP protect? #
DLP should cover personally identifiable information (PII), personal health information (PHI), payment card data, financial records, intellectual property, trade secrets, and any other sensitive or regulated data.
3. What laws and regulations require DLP or benefit from it? #
DLP helps meet requirements for regulations such as GDPR, HIPAA, PCI DSS, GLBA, FISMA, CCPA, and others that mandate control over sensitive data and the ability to detect and prevent unauthorized disclosures.
4. What are common signs that an organization’s DLP program is failing or insufficient? #
Red flags include:
- Frequent access exceptions or data sharing outside policy
- Inconsistent classification across platforms
- Manual audit processes
- Delayed incident detection
- Lack of integration between security, data, and compliance teams
These point to a need for more automated, holistic, and scalable DLP controls.
5. How does DLP support third-party and vendor risk management in data-sharing environments? #
Effective DLP would include the enforcement of granular access controls and auditability across shared data environments. By tagging sensitive fields and restricting access based on roles or trust levels, organizations can safely collaborate with external partners without compromising compliance.
6. How does metadata improve DLP effectiveness? #
Metadata adds critical context—like data ownership, sensitivity, usage patterns, and lineage—that enables smarter policy enforcement, faster incident response, and better audit readiness.
7. What role does data lineage play in strengthening DLP? #
Data lineage allows organizations to trace sensitive data across its full lifecycle—from ingestion to transformation to consumption. This helps identify where exposure risks exist downstream, enforce policies at every step, and ensure consistent protection.