What Is Data Loss Prevention?
Data Loss Prevention (DLP) refers to the processes, technologies, and policies by which an organization detects and prevents the exfiltration, leakage, and destruction of sensitive data. DLP is predicated on visibility into the usage and flow of data across an organization’s ecosystem, including its supply chain.
One important note is that DLP is often used interchangeably to mean both Data Loss Prevention and Data Leakage Prevention. Though there are a few notable differences, most DLP solutions span both use cases.
Data Loss Prevention Policy
A DLP policy is precisely what it sounds like—it defines the scope and nature of your DLP strategy, including:
- What data needs to be protected, and why
- The risks that data faces
- Regulatory compliance measures, if relevant
- The tools you’ll use to protect that data
- Policies and processes
- Roles and responsibilities
- Incident response planning
Data Loss Prevention Policy Best Practices
To develop an effective DLP policy:
Get Leadership on Board: As with any initiative, the first and arguably most important step is to get buy-in from organizational leadership. Each department head should have a say in how your DLP strategy takes shape and how it applies to their data.
Assess Everything: This includes your infrastructure, internal resources, hardware inventory, data stores, and more. Good data hygiene is crucial, so tear down any data silos.
Identify and Prioritize: Determine the different types and classifications of data present within your organization. Assign a priority value to each based on its criticality — and how much damage it would cause if it were lost or stolen.
Establish a Classification Framework: This will allow your DLP solution to orchestrate and categorize your organization’s data accurately.
Define and Document: Hash out your policies and processes immediately, and make sure the documentation is accessible and understandable to everyone who needs it. Make sure to establish the following:
- A DLP vendor evaluation framework
- An acceptable use policy for employees
- The specific rules and requirements that govern the flow of data
- Success metrics
- Data usage controls
- Employee training materials
Implement DLP Technology: Once you’ve chosen and deployed your DLP solution, integrate it with your cybersecurity tools. DLP and cybersecurity go hand-in-hand, and both are crucial to business continuity. On the topic of your DLP technology, ensure you have the following use cases covered:
- Data backups
- Access and usage controls
- Audit trails
- Secure storage
- Data orchestration
Maintain a Continuous Dialogue. Provide employees with regular training and guidance on your DLP policies and solutions. Make sure that whatever you implement doesn’t interrupt or impede workflows.
Data Loss Prevention Solutions
Data Loss Prevention Solution Features
The features of a DLP solution include the following:
- Scanning and analyzing traffic throughout the network to protect data in motion
- Scanning and analyzing cloud traffic
- Controlling the transfer of data between users, groups, and systems
- Providing feedback to users when a data transfer has been blocked
- Enforcing access control, data retention, and encryption policies
- Automatically identifying and orchestrating data
- Identifying and flagging or preventing potentially suspicious data transfers
- Detecting and analyzing incoming and outgoing emails
- Searching for and discovering new data on the network
- Setting automated rules for data usage
Benefits of Data Loss Prevention
More DLP Benefits
- Additional protection for your most sensitive data
- Complete visibility into how data is being used and shared by your employees
- More effective insider threat prevention
- More effective protections for intellectual property
How Data Loss Prevention Works
Typically, DLP combines contextual content matching and exact string matches to identify and filter out potential threats. A DLP solution generally requires an organization to establish and program its ruleset well ahead of time. More advanced DLP solutions augment this functionalist with artificial intelligence and machine learning.
A DLP solution might detect policy violations through any or all of the following techniques:
- Rule-based expression matching: The DLP tool scans for any content that fits specific predefined rules or criteria.
- Exact data matching: Leverages a database to look for exact matches to files or structured data.
- Checksum analysis: Examines the hashes of files to determine the presence of duplicate or modified content.
- Partial matching: Analyzes files to match specific data strings or sections.
- Lexicon matching: Combines dictionaries and predefined rules to analyze unstructured data for sensitive information.
- Advanced analysis: Uses either machine learning or analytical techniques such as Bayesian analysis to detect policy violations in scenarios where other methods have failed.
- Data categories: Classifies and controls data based on pre-built rules or categories, such as Protected Health Information (PHI) or credit card numbers.
Data Loss Prevention and Data Leak Prevention are often viewed as interchangeable. In many cases, they are—any solution designed to prevent data leakage will also typically stop data loss. With that said, there are a few minor differences between the two.
In situations involving data loss, the data in question has been rendered completely inaccessible. This may be due to hardware failure, malware/ransomware, a natural disaster, or an intentional act of sabotage. Protecting against data loss typically requires a combination of business continuity planning and cybersecurity.
On the other hand, data leakage refers to the unauthorized transmission of data outside an organization. This can be intentional (a threat actor exfiltrating a company’s intellectual property) or unintentional (an employee forwarding a sensitive attachment to the wrong email). Preventing data leakage is typically more complex than preventing data loss, as it requires a thorough understanding of data flows and controls on data sharing.