Email Quarantine Automation: Policy Design That Avoids False Positives

The False Positive Problem

Email quarantine automation is essential for managing the volume of threats targeting modern organizations. However, poorly designed policies create two significant problems:

Quarantine Policy Tiers

Not all suspicious emails should be treated equally. Design your quarantine policies in tiers based on threat confidence level and potential impact.

Allowlist Strategy

Allowlists are the primary tool for reducing false positives, but they must be carefully managed to avoid creating security gaps.

Impersonation Protection

Executive and brand impersonation are common BEC attack vectors. Configure impersonation protection carefully to avoid blocking legitimate communications.

Automated Release Workflow

For Tier 3 quarantine (user self-release), implement intelligent automation to reduce false positives while maintaining security.

WHEN user requests release:
  IF email.threat_confidence < 50%
    AND sender.has_history_with_recipient(30_days)
    AND sender.dmarc_pass = TRUE
    AND email.attachments.none(is_executable)
    AND email.urls.all(in_safe_list OR sandbox_clean)
  THEN
    auto_release()
    log_event("auto_release", email, user)
    update_sender_reputation(+1)
  ELSE
    escalate_to_soc(priority: "standard")
    notify_user("Request under review")
    set_sla(response: 2_hours)

Feedback Loop Integration

Use user feedback and release patterns to continuously improve detection accuracy and reduce false positives over time.

Key Metrics

Track these metrics to measure the effectiveness of your quarantine policies and identify areas for improvement.

Common Mistakes to Avoid