When data loss prevention comes to mind, it usually involves preventing data breaches or stopping accidental leakage of sensitive data. However, specific data loss prevention mechanisms—like the exact data match—are more granular, focusing on specific sensitive data types to reduce false positives in cybersecurity.
This article explains what constitutes exact data match, how it differs from traditional data loss prevention techniques, and how to use it to improve cybersecurity.
What Is an Exact Data Match (EDM), and How Does It Work?
Exact Data Match (EDM) is a data comparison method mainly used in data loss prevention (DLP) systems. It involves comparing two fields, character by character, in separate records, to determine whether they match precisely. This process usually results in a definite outcome—why exact data match is also called deterministic linkage.
EDM is typically used when the analyzed dataset contains uniquely identifiable attributes or where precision is required. For instance, it can be used to match customer data from two different databases to identify duplicate entries.
Here's how it generally works:
- Data Indexing: The first step is to create a secure index or fingerprint of the sensitive data, which usually involves choosing the relevant fields in a database and creating a hash of each record.
- Data Matching: Once the index is created, the DLP system scans through data in motion (data sent out of the organization) or at rest (stored data). It matches the hashed values of the sensitive data against the hashes in the index.
- Alert or Block: If the system finds an exact match, it triggers an action specified by the DLP policy. This could be an alert to the system administrator or blocking the transaction entirely.
- Review and Update: The results of the matches are reviewed, and the EDM index can be updated as needed to ensure continuous protection of sensitive data.
EDM is an effective technique for preventing sensitive data leakage as it can precisely identify and protect specific sensitive data values.
Why Exact Data Match Is Important for Data Security
There are several ways in which EDM is crucial for improving data security posture and efficiency:
Improving Precise Detection
Unlike pattern detection, EDM doesn't rely on identifying patterns or structures within data. Instead, it zeroes in on the precise values of sensitive information. This means that EDM solutions are programmed to recognize specific data points like social security numbers, credit card numbers, or even confidential company information.
Because EDM directly compares data against a predefined list of sensitive values, it can pinpoint exact matches accurately. This targeted approach offers a significant advantage in data security. Pattern detection methods, while valuable, can sometimes produce false positives due to their reliance on broader patterns.
However, with its focus on exact values, EDM minimizes the risk of false positives, ensuring alerts are triggered only when sensitive data is genuinely present.
As a result, EDM enhances the efficiency of data security measures by enabling organizations to respond quickly and decisively to data breaches or leaks, safeguarding sensitive information, and maintaining data integrity.
Reducing False Positives
EDM’s targeted approach offers several advantages over traditional data loss prevention methods that rely on pattern matching or regular expression. For instance, EDM significantly reduces the number of false positives generated by security systems.
This is because EDM’s ability to focus on precise data values minimizes the erroneous detection of non-sensitive information that may resemble sensitive data.
By targeting specific data values, EDM reduces false alarms that can burden IT departments and desensitize employees to real threats due to “alert fatigue.” Therefore, EDM helps maintain employee awareness and responsiveness to real threats by minimizing the number of irrelevant alerts, improving the efficiency of security systems.
Moreover, EDM’s targeted approach reduces the computational resources required to monitor and analyze data, allowing faster and more accurate detection of sensitive data exfiltration.
Its targeted approach also enables organizations to prioritize protecting their most critical data by monitoring specific data values, freeing security teams to focus their efforts on preventing the loss of sensitive information that could have the most significant impact on the organization.
Maintaining Regulatory Compliance
EDM is a powerful tool that can significantly enhance data security for businesses. EDM helps organizations comply with data protection regulations, such as GDPR, HIPAA, and PCI DSS, by ensuring that sensitive data is appropriately handled and protected.
Because EDM precisely identifies and matches sensitive data across various systems and databases, it boosts compliance measures in some very specific ways:
Enhanced Data Protection and Regulatory Compliance
One of the key benefits of EDM in bolstering data security is its ability to help businesses meet stringent data protection regulations and standards.
It accurately identifies and classifies sensitive data, such as Personally Identifiable Information (PII), including customer and financial data. This allows organizations to apply appropriate security controls and safeguards, ensuring vital information is adequately protected and compliant with relevant regulations.
Targeted Data Security Measures
EDM enables businesses to implement targeted data security measures based on the specific type and sensitivity of the protected data. For example, PII may require encryption and access controls, while financial data may necessitate additional security measures such as tokenization or masking.
Avoiding Costly Fines and Penalties
With its capacity to empower organizations to apply the appropriate level of protection to critical information, EDM facilitates a proactive approach to data protection. As a result, it helps businesses avoid costly fines and penalties while fostering trust and confidence among customers and stakeholders.
Data Breach Prevention and Response
EDM aids in forensic investigations and post-breach analysis, enabling organizations to identify the root cause of the breach and implement corrective measures to prevent future incidents. Therefore, by accurately identifying and tracking sensitive data, EDM can help organizations quickly detect and contain data breaches, minimizing the potential damage and impact.
Overall Data Security Posture
From meeting regulatory compliance to preventing data breaches, EDM plays a crucial role in strengthening an organization's overall data security posture and safeguarding its valuable assets and reputation.
Because EDM allows organizations to apply the appropriate level of protection, it ensures that critical information remains confidential and secure without hindering legitimate business operations.
Improving Data Quality and Security
EDM significantly enhances data quality by minimizing and rectifying both duplicate entries and errors within a dataset. This improvement in data quality not only streamlines operations and boosts overall efficiency but also plays a crucial role in bolstering data security.
EDM reduces vulnerabilities that unauthorized parties can exploit by ensuring that data is accurate, consistent, and free from redundancies. It also reduces the risk of data breaches and unauthorized access, safeguarding sensitive data and protecting individuals' privacy.
In addition, eliminating duplicate data through EDM helps organizations maintain compliance with data privacy regulations and industry standards, especially by minimizing the unnecessary collection and storage of personal information.
Prevention of Data Leaks
EDM is a data discovery technique that allows organizations to pinpoint sensitive information within their systems. By zeroing in on sensitive data, EDM helps prevent data leaks, protecting both company and customer information.
This is how EDM enhances data security and prevents data leaks:
- Incident Response: In the event of a data breach, EDM can be used to quickly identify the affected data and take appropriate action to contain the breach and minimize its impact.
- Data Protection: By locating sensitive data, EDM enables organizations to implement targeted data protection measures, such as encryption, access controls, and data masking. This helps prevent unauthorized access and data leaks.
- Risk Management: By identifying and mitigating data security risks, EDM helps organizations protect their reputation and avoid financial losses associated with data breaches.
EDM’s ability to enable organizations to discover, classify, and protect sensitive data plays a crucial role in enhancing data security.
Enhancing Data Protection
EDM significantly enhances data protection strategies by enabling organizations to pinpoint vulnerabilities within their data landscape. Through EDM’s ability to precisely identify sensitive data, security teams can implement appropriate safeguards, such as encryption or hashing, ensuring that critical information remains protected.
The advantage of this targeted approach to data security is minimizing the risk of unauthorized access and data breaches, protecting both the organization and its stakeholders.
Adaptability
EDM can adapt to changes in data by focusing on exact values, ensuring even minor data modifications or manipulations do not lead to breaches going unnoticed. Its high degree of precision and ability to focus on the exact data values means that even the slightest alteration or manipulation of data will not go unnoticed, preventing potential breaches.
This adaptability is particularly crucial in today's dynamic data landscape, where information is constantly updated and modified. Traditional data security methods may struggle to keep up with these changes, potentially leaving gaps that malicious actors can exploit. However, EDM's unwavering focus on exact values ensures that it remains effective even as data evolves.
For example, if a hacker attempts to change a Social Security or credit card number by even one digit, EDM will immediately flag this as a potential breach. This level of granularity is essential for protecting sensitive information and maintaining the integrity of data systems.
Overall, EDM is vital to a robust data security and governance strategy.
How Does EDM Differ From Other Data Classification Methods?
Precision: EDM is highly precise because it looks for an exact match of data values. This contrasts with other methods that often look for patterns or data types rather than exact values, leading to less precision and potentially more false positives.
False positive reduction: EDM can significantly reduce false positives compared to other methods due to its exact match approach. Other techniques that use pattern recognition or rules-based classification may pick up false positives due to coincidental matches or slightly overlapping data.
Suitability for sensitive data: EDM is ideal for matching sensitive data like credit card numbers, Social Security numbers, or other unique identifiers. Other classification methods may use broader categories or classifications, which may not be as suitable for sensitive or unique data.
Need for uniquely identifiable attributes: Unlike other classification methods, EDM requires your dataset to contain uniquely identifiable attributes. This is because it compares two fields, character by character, to see if they match.
Limited scalability: EDM works well for specific data values, but its performance may drop when applied to large datasets with varying data types. Therefore, rule-based or AI-based classification methods might perform better in such scenarios as they are designed to handle more diverse and complex data.
Handling variations: EDM may not be as equipped to handle variations or complexities in data as other methods. For example, a rule-based method could include rules for common misspellings or variations, which an EDM method may not be able to match accurately.
The Key Benefits of Implementing EDM in Data Protection Strategies
- Enhanced Accuracy: EDM can significantly increase the accuracy of identifying and protecting sensitive data by looking for specific data values instead of broader patterns or formats.
- Reduced False Positives: By targeting exact data, EDM minimizes the chance of false positives that could lead to unnecessary action or alarm.
- Improved Compliance: EDM can play an important role in helping organizations meet compliance requirements related to data protection, privacy, and storage.
- Prevention of Data Leakage: By identifying exact data values, EDM can help identify potential data leakage points and put strategies in place to prevent such breaches.
- Increase in Operational Efficiency: EDM can automate the process of identifying and protecting sensitive data, freeing up staff resources and increasing overall operational efficiency.
- Enhanced Data Security: EDM is crucial for ensuring data security and preventing breaches, as it allows for a more granular, precise understanding of where sensitive data resides and how it is being accessed and used.
- Reduction of Data Duplication: EDM can also help eliminate data duplication by providing the means to identify duplicate data entries precisely.
How Can EDM Help with Compliance and Data Privacy Regulations?
Exact Data Matching (EDM) can play a significant role in helping organizations comply with data privacy regulations in several ways:
- Identifying Sensitive Data: EDM helps identify sensitive data such as Personally Identifiable Information (PII) or Protected Health Information (PHI) that need protection under regulations like GDPR, HIPAA, and CCPA.
- Reducing Data Leaks: EDM helps to prevent unauthorized data sharing or leaks by comparing outbound data against sensitive internal records. The transfer can be blocked or flagged if a match is found, reducing the risk of non-compliance.
- Enforcing Data Accuracy: EDM ensures data accuracy, a key requirement in many regulations, by detecting and eliminating duplicates.
- Improving Audit Trails: EDM can provide detailed audit trails, which is crucial for demonstrating compliance with regulators. It records successful and attempted data matches, providing insight into how data is used and shared within the organization.
- Enhancing Data Minimization: EDM helps in data minimization, a principle of GDPR and other privacy laws, by matching data with exact records, thereby reducing the amount of data that needs to be processed and stored.
- Data Subject Access Requests (DSAR): EDM can expedite the fulfillment of DSARs. It can quickly and accurately locate specific personal data across systems, making it easier to respond to requests for data access, rectification, or erasure.
- Assists in Breach Notification: In light of a potential breach, EDM can help identify exactly what data may have been compromised via audit trails and matching logs. This aids in fulfilling breach notification mandates as per many privacy laws.
Therefore, EDM contributes to maintaining privacy, ensuring data accuracy, fulfilling DSARs, providing audit trails, and assisting in data breach notifications, all of which are critical components in maintaining compliance with data privacy compliance regulations.
The Best Practices For Maintaining an Accurate EDM Database
Maintaining an accurate Exact Data Match (EDM) database requires specific practices:
- Regular Data Updates: EDM databases should be regularly updated to ensure all changes and updates in data are captured.
- Data Verification: To prevent false matches, it is important to authenticate input data. This involves data validation techniques to ensure accuracy, completeness, and consistency.
- Data Deduplication: Duplicate data can lead to false positives, making deduplication necessary. EDM software often includes deduplication features.
- Using Reliable Data Sources: Always use reliable data sources that are regularly updated and consistent.
- Training: Team members involved in data management should be adequately trained in advanced data matching techniques and the software in use.
- Data Cleansing: Regularly review and cleanse the data in the EDM database to correct inaccuracies and eliminate outdated information.
- Standardization: Implementing data standardization practices helps maintain consistency and increases match accuracy.
- Sound Data Governance: Implement robust data governance policies that outline how data is collected, stored, updated, and used.
- Regular Audits: Regularly audit the EDM database to identify errors and help ensure the quality and accuracy of the data.
- Documentation: Proper documentation of data sources, methodologies, and criteria used in the process helps maintain clarity and transparency.
By following these practices, the accuracy and efficiency of an EDM database can be maintained, leading to better data-matching outcomes.