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June 20, 2024
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 min read

Improving DLP Accuracy: Reducing False Positives

Learn the importance of reducing DLP false positives, their causes, impacts on workflow and security, and effective strategies for minimizing them.

Improving DLP Accuracy: Reducing False Positives
Calendar Icon White
June 20, 2024
Clock Icon
 min read

Improving DLP Accuracy: Reducing False Positives

Learn the importance of reducing DLP false positives, their causes, impacts on workflow and security, and effective strategies for minimizing them.


  • DLP false positives occur when legitimate actions are mistaken for data leaks, leading to workflow disruptions and delayed identification of real threats.
  • False positives decrease productivity, cause frustration, erode trust in DLP systems, and divert resources from genuine security threats.
  • Common causes include legacy DLP systems, rigid rules, manual intervention, user errors, incorrect data classification, misconfigured policies, and fraudulent data dumps.
  • Strategies to reduce false positives include Fine-tuning policies, data classification, advanced detection methods, user education, and integrating DLP with other security tools 
  • Strac's DLP solutions for SaaS, endpoints, and AI protect sensitive data across various platforms, offer remediation actions and ensure compliance with regulations while minimizing false positives.

When it comes to protecting sensitive data from exposure, organizations must take prompt and effective measures. Failure to do so can result in serious repercussions, including financial setbacks, reputational damage, and legal issues. 

Organizations often rely on Data Loss Prevention (DLP) solutions to protect their sensitive data. These systems monitor, detect, and prevent unauthorized data transfers and exposures. However, DLP systems can sometimes mistakenlyflag legitimate data transfers as potential leaks, creating what are known as false positives. These false alerts, resulting from harmless activities, can hinder the identification and mitigation of actual data leaks. 

For instance, a financial institution utilizing DLP to monitor outgoing emails may encounter issues if harmless emails are consistently flagged as security threats. This could frustrate employees and lead them to seek ways around the security controls. Similarly, a healthcare provider experiencing a high number of false positives may face challenges in delivering timely patient care due to the constant need to review flagged communications. The time spent investigating these false positives can delay the detection and response to an actual data leak, potentially causing significant damage.

What are DLP False Positives?

A DLP false positive can occur when a system mistakenly identifies a valid action or piece of information as a potential data leak. This can happen due to overly strict policies, misconfigured rules, or ambiguous content. 

For example, in an organization with sensitive financial data and strict email policies, the DLP system may flag any email containing sequences resembling credit card numbers. If an employee unintentionally includes an internal reference number in their email that matches this pattern, it could trigger a false positive alert, causing unnecessary delays in communication.

Consider another scenario where an employee regularly uploads encrypted backups to an external cloud service as part of their job. The DLP system mistakes these transfers for suspicious activity and triggers a review process. This creates extra work and disrupts normal operations, as the system cannot distinguish between legitimate encrypted data transfers and potential data breaches.

Here are some more examples to illustrate DLP false positives:

  • Sharing internal data: Presenting financial information at an internal meeting could raise a red flag, but the context confirms it's legitimate sharing, not a breach. 
  • File conversions: Converting a file to another format may change its layout or trigger false alarms for sensitive keywords that aren't actually present. 
  • Sales setback: A manufacturing company's data loss prevention system flags a presentation with product designs for a sales meeting, delaying the sales process. 
  • Collaborative documents: A project team works on a document containing terms like “confidential” or “proprietary”. Even though it's shared internally among authorized users, the DLP system may flag it for potential data exposure.

Common Causes for False Positives in DLP Systems

While legacy Data Leakage Prevention (DLP) systems effectively handled structured data and used predefined regular expressions and dictionaries, they struggle with complex cloud-based landscapes. This often leads to false positives, as these outdated systems struggle to keep up with modern technology. 

Here are some of the main drawbacks of legacy DLP systems that contribute to this issue:

  • Dependence on regular expressions and structured Data: Traditional DLP systems heavily rely on structured data formats and regular expressions to identify sensitive information. While they can effectively detect data in standardized formats, they struggle with the diverse and unstructured data often found in modern SaaS environments. The inflexibility of these legacy tools is a significant drawback.
  • Rigidity: Legacy DLP tools' simplicity and rigidity can often lead to inaccurate results when differentiating between sensitive and non-sensitive data. For instance, a legacy DLP system may falsely flag a sequence of numbers as a credit card number, even if it is a customer reference code or phone number. This can result in numerous false positives and hinder the tool's effectiveness.
  • Manual intervention: Legacy data loss prevention systems often require manual intervention due to their reliance on static rules and patterns. This leads to frequent updates and interventions to adapt to new types of sensitive data, which can be time-consuming and prone to errors. It can also increase false positives, causing additional challenges for organizations using these systems.
  • Inadequate for unstructured data: Often, sensitive information is stored in unstructured formats like Slack chats, PDFs, and Word documents. Legacy DLP systems are not equipped to detect and classify this type of data accurately, leading to gaps in protection and more false positives.

While modern DLPs address many of these issues, they still generate false positives. Common causes include:

  • Complex data patterns

DLP systems often use complex data patterns to identify sensitive information, which can lead to false positives when similar patterns are found in non-sensitive data.

  • Encrypted data

The DLP system might identify encrypted data as suspicious due to its inability to analyze the actual content, which could result in false alarms.

  • Regular expressions and patterns

Improperly designed regular expressions and overly general patterns can inadvertently match non-sensitive text, such as credit card numbers or social security numbers. If data is not classified accurately within the system, DLP might flag non-sensitive data as sensitive.

  • Misconfigured policies

Improperly configured policies could result in excessive monitoring and false alarms. For instance, setting a rule to flag every commonly used word or number format occurrence can lead to unnecessary alerts.

  • Legitimate business processes

If the rules are not properly tuned, everyday business operations, like transmitting reports or client information within a secure network, could trigger unnecessary DLP notifications.

  • Data formats and variations

Different data formats and variations can result in false positives if the DLP system's pattern recognition lacks adaptability.

  • Shared content and collaboration

Collaborative tools and shared content platforms may trigger false positives in the DLP system when multiple users collaborate on a document.

  • Outdated or generic detection algorithms

Employing outdated or generic detection algorithms can also increase the occurrence of false positives.

  • Pattern-matching limitations

Relying solely on pattern-matching mechanisms for data leak detection tools has limitations. While they may scan for keywords like email addresses or secret key fragments on the surface and dark web, this approach lacks nuance and fails to consider the broader context of the data. This can result in false alarms, such as a random string in a code dump triggering an alert even though it is not a threat.

  • Fraudulent data dumps

Sophisticated attackers can purposely insert misleading data leaks containing small, seemingly sensitive information. These data dumps attempt to trigger false alarms and test the vigilance of security teams. It serves as a distraction tactic, redirecting attention away from the attacker's true cybercriminal actions. Many ransomware gangs utilize this strategy to deceive investigations, often releasing fake statements about breaches to divert law enforcement from their operations.

  • User error: 

It is possible for users to unintentionally trigger DLP alerts by including confidential information in emails or documents meant for internal sharing

Negative Effects of False Positives on Workflow and Security Protocols

False positives in DLP systems can have several negative effects on both workflow efficiency and overall security posture. Here's a breakdown of the negative effects:

1. Workflow Disruptions

Decreased productivity: Security teams and end-users often spend time investigating and resolving false positives, which can distract their attention from important tasks, interrupting business processes. Legitimate activities may be delayed or halted due to incorrect flags, disrupting normal business operations and potentially causing missed deadlines and decreased productivity.

Increased frustration and fatigue: When users are bombarded with an overload of false positives, they can become desensitized and start ignoring them. This can result in real security threats going unnoticed, increasing the risk of data breaches. Frequent false positives can also be challenging for users who constantly face unnecessary security checks, leading to frustration and possibly even attempts to bypass DLP policies.

2. Security Protocol Impacts

Reduced trust in DLP: When users experience a high number of false positives, they may lose trust in the effectiveness of the DLP system. This can lead to users bypassing DLP controls altogether, increasing the risk of data leakage. Frequent false positives can undermine trust in the DLP system, leading employees to disregard or undervalue genuine alerts and weakening the overall security posture.

Delayed response to real threats: The diversion of resources to investigate false alarms can hinder the timely detection and response to actual security breaches, leaving the organization vulnerable to data breaches. 

Resource depletion: Allocating extra resources to investigate false alarms can drain the team's capacity, diverting attention from genuine threats and critical security duties.

3. Operational Inefficiencies

a. Increased Administrative Overhead

Dealing with false alarms can lead to extra administrative tasks, like evaluating and resolving alerts, which can put pressure on already scarce resources.

b. Compliance Challenges

False positives can create difficulties in maintaining compliance by producing unnecessary records and reports, resulting in audit complications and heightened scrutiny. This may also lead to mistakenly blocking important file transfers, causing project timeline delays.

c. Impact on Collaboration

Collaboration and sharing can be negatively impacted if legitimate activities are continuously flagged as suspicious. This can make it difficult for teams to work together effectively. False positives can block important emails containing crucial business information, causing breakdowns in communication.

4. Financial Implications

Dealing with false positives can drain resources in terms of time and money. This can directly impact the budget and distract from other important initiatives. Not only that, but these false alarms can also cause delays and disruptions, which can result in missed business opportunities and dissatisfied customers, ultimately affecting revenue.

5. Cultural Resistance

Continuous false positives affect employee morale, causing them to feel micromanaged and untrusted, diminishing their ability to protect the organization effectively. This can eventually lead to cultural resistance to following these protocols, resulting in non-compliance and risky workarounds.

Strategies to Reduce DLP False Positives

1. Fine-tuning DLP policies

Refine Keyword Matching: Instead of depending only on keywords, utilize regular expressions (regex) with negative lookaheads to filter out irrelevant results. 

Data Classification Review: Conduct regular assessments to review and revise data classification rules to identify sensitive data accurately.

Granular Policy Creation: Establish specific policies that take into account the type of data, user roles, and recipients and allow applications to prevent false alarms from legitimate actions outside policy boundaries.

Ways to customize policies:

  • Classify policies according to the data type, like imposing stricter regulations on financial information. 
  • Personalize rules based on user responsibilities, granting developers necessary API keys while limiting access for marketing staff. 
  • Establish guidelines based on the intended recipients, flagging any data sent to unfamiliar external domains. 

Customized policies benefits: 

  • Reduces false alarms and enhances attention to real risks. 
  • Boosts efficiency and strengthens security measures. 
  • Minimizes interruptions in legitimate operations, fostering user confidence.

Whitelist known sources: To prevent legitimate activities from being flagged, whitelist trusted sources, email addresses, and IP addresses.

Role-based exceptions: Create exceptions based on roles to address frequent false positives with close monitoring, allowing trusted roles fewer restrictions.

2. Data classification

Categorize data sensitivity: Organize data according to its sensitivity and enforce suitable DLP regulations for each category, ensuring that stricter rules are implemented for highly sensitive information. 

Metadata Tagging: Metadata tagging offers extra information about the data, enabling the DLP system to enhance decision-making processes.

Advanced DLP features to Look for DLP software

1. Combine multiple detection methods

Context-aware analysis is a useful tool for understanding the sensitivity and relevance of data, taking into account both the content context (such as keywords and patterns) and the user context (including roles, behavior patterns, and access levels).

Behavioral analysis is a data-driven approach that helps establish a baseline for expected user behavior. It analyzes historical data to recognize unusual patterns or deviations from this baseline. Implementing behavioral baselines involves tracking typical user activities and behaviors to differentiate between normal and suspicious behavior. DLP systems can then use this information to generate alerts when significant deviations from the established baselines occur, indicating potential security threats.

Content inspection: Employ advanced methods such as fingerprinting, natural language processing (NLP), and data classification for thorough content inspection to identify sensitive data accurately

User and Entity Behavior Analytics (UEBA): Analyze user behavior patterns to distinguish between authorized data transfers and potential breaches, reducing false positives from routine activities by considering context.

Data masking: Anonymize sensitive information in authorized data transfers, ensuring that legitimate data flow is maintained.

2. User education and training

DLP policy awareness: Educate employees on DLP policies and best practices. This will empower them to avoid actions that could potentially trigger false positives.

Incident reporting: Encourage employees to report false positives to the security team, as this helps refine DLP policies and reduce future occurrences. This proactive approach allows the security team to quickly investigate and resolve true threats, minimizing the impact of false positives on the organization's overall security.

Feedback mechanism: Establish a feedback mechanism for employees to report any false positives that may occur while following DLP policies. Analyze this information to improve the DLP system, ensuring efficient handling of sensitive data. 

3. Integration With Other Security Tools

SIEM integration: Security Information and Event Management (SIEM) is a security platform that collects and analyzes data from various sources, including DLP systems. It correlates events detected by DLP with data from other security tools, such as firewalls and intrusion detection systems, to provide a broader view of potential threats. This helps to reduce false positives triggered by isolated incidents in the DLP system and identify the context behind each event.

Threat intelligence feeds: Use threat intelligence feeds to enhance the effectiveness of DLP by keeping it updated on the latest attack methods and indicators of compromise. This helps DLP to focus on the most relevant threats, reducing false positives caused by outdated rules.

Endpoint and network integration: Ensure seamless integration of DLP solutions across endpoints and networks for a holistic perspective on data flow and uniform policy enforcement.

4. Ongoing monitoring and refinement

False positive review: Review false positive logs regularly to identify recurring patterns and adjust DLP policies accordingly. Employ machine learning algorithms to assist with pattern recognition and performance optimization.

Regular audits and testing using simulation: Regularly audit DLP policies and rules. Test the DLP system with different scenarios to ensure that it accurately identifies true positives and minimizes false positives. Some DLP solutions also have sandboxing capabilities, which can isolate and test suspicious files in a controlled environment without interfering with legitimate data transfers. This reduces the chances of false positives caused by overly cautious file-blocking measures.

Strac DLP for SaaS, Endpoints, and Gen AI 

Strac SaaS DLP, Endpoint and Gen AI DLP protect businesses by discovering (scanning), classifying and remediating sensitive data like SSN, driver licenses, credit cards, bank numbers, IP (Confidential Data), etc., across all communication channels like O365, Slack, GWorkspace (Gmail, Google Drive), Email, One Drive, Sharepoint, Jira, Zendesk, Salesforce, etc. It protects endpoints like Mac, Windows, and ChatGPT.

Strac capabilities:

  • Discover, Classify, and Protect sensitive data: Detect sensitive data accurately and precisely across volumes of unstructured texts and documents while reducing false positives.
  • Remediate Sensitive Data: Strac provides remediation actions like redaction, blocking, alerting, and encryption. Strac's redaction replaces sensitive data with a link to Strac's secure vault.
  • API integration: With Strac, you can also leverage Strac's RESTful APIs to do the same thing alongside their native No-Code integrations
  • Dashboard and Analytics: In Strac's Vault, you can see all sensitive data discovered and remediated by Strac, with beautiful graphs and analytics results, such as which employees shared what sensitive data from which devices.
  • Achieve Compliance and Comply with Regulations/Privacy Laws: Strac Data Discover, DLP (Data Leak Prevention), and CASB (Cloud Access Security Broker) solutions will help you achieve PCI, SOC 2, NIST CSF, HIPAA, GDPR, CCPA, and India's DPDP (Digital Personal Data Protection).
  • Endless data protection: Protect PII, PHI, financial data, trade secrets, credit card information, and other sensitive data – even in images and PDFs
  • Improved visibility of sensitive data sharing: Improve the visibility of sensitive information through a panoramic view of unstructured data across the organization.

Secure your data with Strac today!

Founder, Strac. ex-Amazon Payments Infrastructure (Widget, API, Security) Builder for 11 years.

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