Understanding false positives in email security

False positives occur when email security solutions mistakenly identify safe emails as harmful, often due to overly stringent detection algorithms or misinterpreted behaviors. While the focus is often on preventing cyberattacks, minimizing false positives is equally important for ensuring seamless communication and operational efficiency. 

Security tools such as network monitoring tools are designed to identify and block malicious emails, but when legitimate messages are flagged as threats, organizations face a challenging problem: false positives. These misclassifications can disrupt workflows, delay critical communications, and erode trust in security systems.

What are false positives in email security?

The Messaging, Malware and Mobile Anti-Abuse Working Group (M3AAWG) define false positives as “legitimate emails classified as junk”. This misidentification results in legitimate messages being blocked, quarantined, or marked as suspicious, disrupting normal communication and productivity. 

Examples of false positives in email security

• Flagged invoices: Legitimate invoices from trusted vendors may be flagged as phishing attempts, causing delays in payment processing.
• Newsletters: Regular newsletters from subscribed sources might be mistakenly categorized as spam, leading to missed information.
• Internal communications: Important internal emails, such as project updates or meeting invitations, could be blocked or quarantined, impacting team collaboration. This can happen due to the following reasons:
  • Stringent filtering rules: Overly aggressive security filters may misinterpret certain phrases or content patterns as malicious.
  • Misconfigured security settings: Incorrectly configured settings might fail to recognize internal email traffic, flagging it as suspicious.
  • Content similarity: Internal emails containing attachments, links, or language similar to known phishing or spam patterns can be wrongly identified as threats.
  • Heuristic-based detection: Some security systems use heuristic analysis, which might misjudge the intent of an email and trigger false positives.
  • Reputation-based filters: If internal email addresses or domains are flagged in reputation-based filters due to past incidents, emails from those sources might be blocked.

What do false positives happen?

False positives happen due to overly sensitive security settings, which may flag legitimate emails as threats. Misclassification can also occur with new or uncommon sender behaviors that don't match typical patterns, and complex or ambiguous content, such as numerous hyperlinks or specific keywords, can trigger security red flags. These factors can lead to legitimate emails being mistakenly identified as malicious.

The impact of false positives

False positives can impact an organization by causing productivity loss due to delayed communications, leading to frustration and decreased trust in email systems. This frustration may result in employees bypassing security measures, increasing the risk of genuine threats. Additionally, the resource drain on IT teams, who must address and investigate unnecessary alerts, diverts their attention from more critical security tasks and can lead to alert fatigue. 

Balancing security and productivity

While security measures protect the organization’s email systems, an overly cautious approach can be counterproductive. Excessive sensitivity in detection systems often leads to a high number of false positives, which can disrupt business operations, frustrate employees, and erode trust in the security system. This constant disruption may encourage employees to find ways to circumvent security protocols, ultimately increasing the risk of genuine threats slipping through.

Tuning detection systems too leniently can leave the organization vulnerable to actual threats. If the security system fails to detect and block malicious activities accurately, attackers can exploit these gaps, leading to data breaches, loss of sensitive information, and potential financial and reputational damage, according to a study, these costs are especially higher for healthcare organizations that have experienced a breach of protected health information (PHI). Striking the right balance between security and productivity involves carefully calibrating detection systems to minimize false positives while ensuring robust protection against real threats.

Strategies to minimize false positives

A research paper titled, Reducing False Positives in Cybersecurity with Interpretable AI Models, proposes that organizations should make use of the following strategies to minimize false positives:

• Interpretable AI models
  • Decision trees: Provide clear, visible decision paths for email classification.
  • SHAP models: Highlight feature contributions, such as sender reputation or content patterns.
  • Rule-based systems: Tailor detection rules to organizational needs using domain knowledge.
• Data processing
  • Collect comprehensive network and system data.
  • Preprocess data with normalization and feature extraction.
  • Focus on features like IP addresses and user behaviors.
• Model development
  • Combine rule-based and AI models for balance.
  • Use cross-validation for accuracy across datasets.
  • Emphasize transparent models that maintain high precision.
• Evaluation and refinement
  • Use metrics like precision and false positive rates to optimize models.
  • Continuously update based on evolving threats.
  • Compare models against black-box alternatives to ensure effectiveness.
• Operational deployment
  • Fine-tune detection rules for specific needs.
  • Enable quick validation of flagged emails.
  • Foster collaboration between technical and non-technical teams with interpretable results.

FAQs

What are decision trees?

Decision trees are a type of interpretable AI model that uses a tree-like structure to make decisions based on input data. Each node in the tree represents a decision point, with branches leading to different outcomes.

What are rule-based systems in AI?

Rule-based systems in AI use explicit rules derived from domain knowledge to make decisions. These rules are typically in the form of "if-then" statements that define how the system should respond to different inputs.