DLP

What Is Data Loss Prevention (DLP)?

DLP, an acronym for Data Loss Prevention, encompasses a suite of diverse technologies meticulously crafted to pinpoint and categorize content within data, spanning across various formats such as emails, files, packets, applications, and data repositories. This capability extends across data states, whether stationary, in use, or in transit. Moreover, DLP offers an array of functionalities including logging, marking, encryption, permission control, and blocking, all geared towards identifying and managing sensitive information in alignment with predefined policies.

The terms "data loss" and "data leakage" are inherently interconnected, with data loss prevention often interchangeably referred to as data leakage prevention. Consider a scenario where an unauthorized individual gains access to and opens a misplaced storage device containing confidential data; what initially manifests as a data loss event swiftly transforms into a data leakage incident. Conversely, data leakage can occur without the loss of the primary data source. For instance, confidential information might inadvertently leak through the transmission of a document via email.

Why Data Loss Prevention?

As information technologies advance rapidly, the indispensability of the Internet and computers for office work, communication, and collaboration has become apparent. Despite their efficiency-enhancing capabilities, these systems also impose heightened demands on data storage and transmission security.

The burgeoning volume of data necessitates enhanced protection measures.

From safeguarding personal information like accounts, phone numbers, and addresses to preserving intellectual property rights encompassing product designs and R&D blueprints, and safeguarding business secrets such as budgets, plans, and payrolls, the imperative for protection is evident. Any breach leading to data leakage could inflict immeasurable losses upon enterprises.

The regulatory landscape has become more stringent.

legislation such as GDPR and HIPAA mandates enterprises to safeguard data assets, curtailing the risk of sensitive information leakage. Incidents of data leakage expose enterprises to legal liabilities, amplifying the stakes.

Data breaches aren't solely the domain of hackers.

Instances of data leaks stemming from both intentional and inadvertent actions of internal employees are on the rise. Mishaps like accidental email transmissions, lost USB flash drives, and unauthorized printing of confidential files contribute to the vulnerability.

Conventional security measures fall short of combating data leakage comprehensively.

Utilizing security devices such as traditional firewalls, IPS devices, and storage encryption tools, traditional solutions offer passive, perimeter-based protection methods. However, these solutions impede data flow, lack control over authorized users leaking data, and fail to effectively identify and classify sensitive information for robust enterprise data management.

How does Data Loss Prevention Work?

DLP, with its predefined rules, discerns sensitive data and shields it accordingly. But how does this task execute?

Data Acquisition

The initial step involves acquiring data, irrespective of its storage, copying, or transmission location. Generally, DLP accesses enterprise intranet data through various means:

• Conducts scans on terminals and server storage devices.

• Monitors network protocol links and reconstructs transmitted files.

• Observes applications and drivers, extracting both transmitted and utilized data.

Thorough Content Analysis

Not all data warrants protection; DLP focuses solely on data containing sensitive information. Following data acquisition, DLP undertakes in-depth content analysis and detection to ascertain the presence of sensitive information, irrespective of its format, be it email, PDF, Word, or PPT.

Content analysis and detection technologies fall into two categories: common detection and advanced (key) detection technologies. Common detection methods encompass regular expression detection, keyword detection, and document attribute detection. Meanwhile, advanced detection technologies include:

• Indexed Document Matching (IDM): Matches content or documents with unstructured sample documents (e.g., Word, PPT, PDF, and various source program files) to identify similarities.

• Exact Data Matching (EDM): Precisely matches documents or content with structured data source tables (e.g., Excel and database tables) to verify if they originate from these tables.

• Computer vision technology: Extracts contour features from images for similarity matching against stored sample image features, regardless of manipulations like zooming, cropping, watermarking, or brightness alteration.

Data Governance

Once sensitive information is pinpointed, files containing such data must be managed according to predefined policies. Control measures encompass:

• Data encryption: Safeguards sensitive data by encrypting it, ensuring its confidentiality even in the event of data leakage.

• Permission identification and control: Assigns permission attributes to both data and users, restricting access solely to authorized users. Data is automatically decrypted during access, preserving user experience.

• Prevention of illicit activities: Blocks or raises alerts for unauthorized activities such as improper sending, copying, and printing.

• Visualized data representation: Illustrates the distribution of sensitive data across the network in a comprehensible manner.

• Data auditing: Offers comprehensive logs and reports covering the entire lifecycle (from creation to destruction) of sensitive information.

Data Loss Prevention Best Practices

DLP represents a comprehensive solution rather than a standalone product. Consider the following points for DLP implementation:

• Assess the necessity of DLP deployment: Evaluate whether enterprise operations involve handling sensitive personal data subject to regulations like GDPR and HIPAA. Also, determine if the enterprise's intellectual property or business decisions are at risk from competitors. Understanding the criticality of data protection needs can guide the decision to deploy DLP and garner support from business units.

• Prioritize data classification: Recognize that not all data holds equal importance. Different organizations may have varying definitions of critical data. Identify which data, if leaked, would pose the most significant challenges and what types of data are attractive to potential attackers. Focus initial protection efforts on safeguarding these types of data.

• Assess data risks: Analyze how key data is stored, used, and transmitted, identifying potential points of data leakage. Determine whether data breaches stem from external breaches or internal mishandling.

• Choose an appropriate solution: Select a DLP solution that aligns with factors such as the type of critical data, its format and structure, enterprise IT workflows, device types, and available budget.

• Establish communication and policy development: Engage in thorough discussions with data users (business units) to outline their roles and responsibilities. Craft distinct control policies for different data types and user roles.

• Provide employee training: Recognize that DLP implementation will alter employees' data handling practices. Therefore, it's crucial to provide training both before and after DLP deployment. Alongside DLP, fostering a culture of data awareness among employees is imperative for comprehensive data security.

• Conduct data audits and evaluate effectiveness: After deploying DLP, monitor how key data is distributed, transferred, and utilized within the enterprise. Address any identified issues and risks promptly to mitigate potential losses from data breaches.

• Adapt policies continuously：Understand that DLP deployment is not a one-time action. As enterprise services evolve and IT processes change, definitions of critical data may shift, and data formats and transfer methods may evolve accordingly. Long-term data auditing aids in uncovering ongoing security risks, necessitating real-time adjustments to DLP policies by administrators.