27 July 2026 · DMARC Engine · 37 min read
Introduction to False Positives in DMARC
False positives in DMARC reporting are a significant concern for organisations, as they can lead to legitimate emails being incorrectly flagged as spam or rejected by recipient mail servers. This issue arises when a DMARC record is not optimised for the organisation's specific email sending patterns, resulting in genuine emails failing DMARC validation. A common scenario we encounter at DMARC Engine is when a customer's email service provider or marketing platform uses a different domain or subdomain for sending emails, which is not accounted for in the DMARC record. For instance, a company may use news.example.com for their newsletter, but their DMARC record only includes example.com as an authorised sending domain.
In a hosted setup like ours, we often see customers who have not properly configured their DMARC records to account for all the different sources of email sending, such as third-party providers or internal applications. This can lead to a high rate of false positives, which in turn can damage the organisation's email deliverability. To mitigate this, we recommend that customers conduct a thorough audit of their email sending infrastructure to identify all potential sources of email. This includes not only marketing platforms and email service providers but also internal applications, such as password reset systems or notification services.
A key aspect of mitigating false positives is understanding the nuances of DMARC record configuration. For example, the p tag in a DMARC record specifies the policy for emails that fail DMARC validation, with options ranging from none to quarantine or reject. However, if the p tag is set too aggressively, without properly accounting for all authorised senders, it can lead to a high rate of false positives. We have seen cases where a customer sets their p tag to reject without fully understanding the implications, resulting in legitimate emails being blocked by recipient mail servers.
Example of a DMARC record with an aggressive policy:
_dmarc.example.com. IN TXT "v=DMARC1; p=reject; pct=100; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1"In this example, the p tag is set to reject, which means that any email that fails DMARC validation will be rejected by the recipient mail server. While this may seem like a good way to prevent spam, it can also lead to false positives if the DMARC record is not properly configured. A more conservative approach might be to start with a p tag set to none or quarantine, and then gradually increase the aggressiveness of the policy as the organisation gains more experience with DMARC and is able to optimise their record.
Another critical aspect of mitigating false positives is monitoring and analysis of DMARC aggregate reports. These reports provide valuable insights into email sending patterns and can help organisations identify potential issues before they become major problems. However, analysing these reports can be a complex task, especially for organisations with large volumes of email traffic. At DMARC Engine, we provide our customers with tools and expertise to help them make sense of their DMARC reports and identify areas for improvement.
To illustrate the importance of monitoring and analysis, consider the following example of a DMARC aggregate report:
<feedback>
<report_metadata>
<org_name>example.com</org_name>
<email>dmarc@example.com</email>
<extra_contact_info>https://example.com/dmarc</extra_contact_info>
<report_id>1234567890</report_id>
<date_range>
<begin>2022-01-01T00:00:00Z</begin>
<end>2022-01-07T23:59:59Z</end>
</date_range>
</report_metadata>
<policy_published>
<domain>example.com</domain>
<adkim>r</adkim>
<aspf>r</aspf>
<p>none</p>
<sp>none</sp>
<pct>100</pct>
</policy_published>
<record>
<row>
<source_ip>192.0.2.1</source_ip>
<count>10</count>
<policy_evaluated>
<disposition>none</disposition>
<dkim>pass</dkim>
<spf>pass</spf>
</policy_evaluated>
</row>
<row>
<source_ip>198.51.100.1</source_ip>
<count>5</count>
<policy_evaluated>
<disposition>none</disposition>
<dkim>fail</dkim>
<spf>fail</spf>
</policy_evaluated>
</row>
</record>
</feedback>In this example, the report shows two rows of data, each representing a different source IP address. The first row shows 10 emails that passed both DKIM and SPF validation, while the second row shows 5 emails that failed both DKIM and SPF validation. By analysing this data, the organisation can identify potential issues with their email sending infrastructure and take corrective action to mitigate false positives.
In short, mitigating false positives in DMARC reporting requires a thorough understanding of DMARC record configuration, email sending patterns, and the nuances of aggregate report analysis. By taking a conservative approach to DMARC policy configuration, monitoring and analysing aggregate reports, and optimising DMARC records for their specific use case, organisations can reduce the risk of false positives and improve their overall email deliverability. In the next section, we will delve deeper into the impact of false positives on email deliverability and explore strategies for identifying and mitigating these issues.
Understanding the Impact of False Positives on Email Deliverability
The centre of any effective DMARC strategy is a thorough understanding of how false positives can impact email deliverability. False positives, in this context, refer to legitimate emails that are incorrectly flagged as spam or rejected due to DMARC policy enforcement. This can lead to a significant loss of legitimate emails, damaging the sender's reputation and causing frustration for both the sender and the recipient. In our experience managing DMARC for numerous customers, we have seen firsthand how false positives can undermine the effectiveness of email campaigns and hinder communication.
One of the primary challenges in mitigating false positives is the trade-off between security and deliverability. Implementing a strict DMARC policy can effectively prevent phishing attacks but may also increase the likelihood of false positives. For instance, a customer of ours, a large e-commerce company, implemented a DMARC policy with a strict alignment requirement. While this helped in reducing phishing attacks, it also resulted in a significant number of false positives, particularly from legitimate mailing lists that did not properly align their SPF and DKIM records.
_dmarc.example.com. IN TXT "v=DMARC1; p=reject; pct=100; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1" In this example, the p=reject directive tells receivers to reject emails that fail DMARC checks, which can lead to false positives if not carefully managed.
To optimise the DMARC record and reduce false positives, it is crucial to monitor aggregate reports closely. These reports provide valuable insights into how emails are being handled by receivers and can help identify potential issues before they become major problems. For example, if a report shows a high number of emails failing DMARC checks due to SPF alignment issues, the sender may need to adjust their SPF record to include all legitimate sending sources. In a hosted or managed setup, such as the one we provide at DMARC Engine, our system can automatically analyse these reports and provide recommendations for improving DMARC records to reduce false positives.
Another critical aspect of understanding the impact of false positives is recognising the colour of the DMARC alignment. DMARC alignment comes in two flavours: relaxed and strict. Relaxed alignment checks if the domain in the From header has the same organisational domain as the domain in the SPF or DKIM record, while strict alignment requires an exact match. Choosing the right alignment can significantly impact the number of false positives. For instance, using strict alignment may reduce the risk of phishing but could also increase false positives from mailing lists or other legitimate senders that do not perfectly align their domains.
example.com. IN TXT "v=spf1 include:_spf.example.net -all" In this SPF record example, the include:_spf.example.net directive allows emails sent from the _spf.example.net domain to pass SPF checks for example.com. However, if the From header domain does not strictly align with the domain in the DKIM signature, DMARC checks could fail, leading to potential false positives.
The impact of false positives can also vary depending on the DMARC policy percentage (pct tag in the DMARC record). Setting pct=100 means the DMARC policy is applied to all emails, which can maximise the risk of false positives if the DMARC record is not perfectly configured. A more cautious approach might be to start with a lower percentage, such as pct=10, to apply the DMARC policy to only a portion of emails, allowing for testing and refinement of the DMARC setup before widening its application.
_dmarc.example.com. IN TXT "v=DMARC1; p=none; pct=10; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1" This approach enables the sender to monitor the impact of the DMARC policy on a smaller scale, reducing the risk of widespread false positives during the initial setup and testing phase.
In short, understanding the impact of false positives on email deliverability requires a deep dive into the specifics of DMARC records, alignment options, and the careful monitoring of aggregate reports. By recognising the potential for false positives and taking steps to mitigate them, senders can optimise their DMARC setup to balance security with deliverability, ensuring that legitimate emails reach their intended recipients without being incorrectly flagged or rejected. This nuanced approach to DMARC management is at the centre of our operational guidance and recommendations for our customers at DMARC Engine, where we continually work to refine and improve DMARC setups to minimise false positives and maximise email deliverability.
Identifying False Positives in Aggregate Reports
Identifying false positives in DMARC aggregate reports is a crucial step in mitigating their impact on email deliverability. A false positive, in this context, refers to a legitimate email that is incorrectly flagged as spam or blocked by a receiver's DMARC policy. To centre our efforts on mitigating these false positives, we must first be able to identify them within the aggregate reports. These reports, typically received via the Aggregate Feedback (RUA) mechanism, provide a colour of the email authentication landscape, highlighting where emails are failing DMARC validation.
In a hosted or managed setup, such as the one we operate at DMARC Engine, identifying false positives often begins with automated parsing of aggregate reports. We look for specific indicators that suggest a legitimate email has been incorrectly flagged. For instance, a high volume of emails from a trusted sender failing DMARC due to a "none" or "quarantine" policy can indicate a false positive scenario. Our system flags such instances for manual review, where our engineers can delve deeper into the report details.
When manually reviewing aggregate reports, we pay close attention to the result field in the report, which can indicate a "fail" due to SPF, DKIM, or both. For example, consider the following snippet from an aggregate report:
{
"org_name": "example.com",
"email": "noreply@example.com",
"extra_headers": {},
"spf": {
"domain": "example.com",
"result": "fail"
},
"dkim": {
"domain": "example.com",
"result": "pass"
},
"dmarc": {
"result": "fail"
}
}In this case, the email failed DMARC validation due to an SPF failure, despite the DKIM passing. This could be a legitimate email that has been incorrectly flagged, especially if the sender is known to have a good reputation and proper email authentication setup.
To optimise our identification process, we also consider the sender's IP address and the authentication results from other receivers. This holistic view helps in distinguishing between genuine spam and false positives. For instance, if a sender's emails are consistently failing DMARC at multiple receivers due to the same issue (e.g., SPF alignment), it might indicate a configuration problem rather than a false positive.
However, there are edge cases where identifying false positives can be particularly challenging. One such scenario involves emails that are forwarded or redirected, which can break SPF and sometimes DKIM validation. Consider an email sent from user@example.com to forwarded@example.net, which is then forwarded to final-destination@example.org. If example.net does not have an appropriate SPF record or if the forwarding process alters the email in a way that invalidates the DKIM signature, the final recipient (example.org) might receive an email that fails DMARC validation, even though the original sender (example.com) has proper authentication in place.
In managed setups, handling such edge cases often involves close collaboration with the sender to understand their email flow and configuration. We work with senders to implement or adjust their email authentication records (SPF, DKIM, DMARC) to better accommodate their specific email handling practices, such as using a more relaxed DMARC policy for certain types of emails or implementing mechanisms like SPF flattening for easier management of multiple sending sources.
Another critical aspect of identifying false positives is the analysis of the report's source_ip field, which indicates the IP address of the sending server. This can help in pinpointing emails sent from legitimate but unauthenticated sources. For example, if a company uses a third-party service for sending newsletters, and this service does not authenticate emails with the company's domain, the emails might fail DMARC validation at receivers. Identifying such scenarios allows for targeted interventions, such as working with the third-party sender to implement authentication or adjusting the company's DMARC policy to accommodate these legitimate senders.
In real-world scenarios, the process of identifying false positives is not without its trade-offs. A more aggressive approach to mitigating false positives might involve relaxing DMARC policies, which could potentially open up the sender to more spam. Conversely, a stricter policy aimed at reducing spam might inadvertently increase the number of false positives. Our experience at DMARC Engine has shown that a balanced approach, combining automated report analysis with manual oversight and collaboration with senders, offers the best outcomes in minimising both false positives and spam.
Ultimately, the key to effectively identifying false positives in DMARC aggregate reports lies in a deep understanding of email authentication mechanisms, the ability to interpret aggregate report data, and a proactive approach to collaborating with senders and receivers to optimise email authentication configurations. By focusing on these aspects and leveraging the insights gained from managing DMARC for our customers, we can significantly reduce the impact of false positives on email deliverability.
Analysing DMARC Record Examples for False Positive Mitigation
When it comes to mitigating false positives in DMARC reporting, analysing DMARC record examples is crucial. A well-crafted DMARC record can help reduce false positives, while a poorly configured one can lead to a significant increase in false positive reports. In this section, we will delve into the specifics of DMARC record analysis, highlighting examples of well-configured records and common pitfalls to avoid.
To begin with, let's consider a basic DMARC record example:
_dmarc.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1"In this example, the DMARC record is set to monitor mode (p=none), which means that emails that fail DMARC checks will not be blocked. The pct parameter is set to 100, indicating that the DMARC policy applies to all emails. The rua and ruf parameters specify the email addresses where aggregate and forensic reports will be sent, respectively. The fo parameter is set to 1, which means that forensic reports will be sent for all emails that fail DMARC checks.
Now, let's consider a more complex DMARC record example:
_dmarc.example.com. IN TXT "v=DMARC1; p=quarantine; pct=50; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1; adkim=r; aspf=r"In this example, the DMARC record is set to quarantine mode (p=quarantine), which means that emails that fail DMARC checks will be marked as spam. The pct parameter is set to 50, indicating that the DMARC policy applies to 50% of emails. The adkim and aspf parameters are set to r, which means that relaxed alignment will be used for both DKIM and SPF checks.
When analysing DMARC record examples, it's essential to consider the trade-offs between different parameters. For instance, setting pct to 100 can help reduce false positives, but it may also lead to a higher number of blocked emails. On the other hand, setting pct to a lower value, such as 50, can help reduce the number of blocked emails, but it may also increase the number of false positives.
In a hosted or managed setup, such as the one provided by DMARC Engine, the DMARC record is typically configured and managed on behalf of the customer. This can help simplify the process of analysing and optimising DMARC records, as the hosted provider can leverage their expertise and experience to configure the records correctly.
One common pitfall to avoid when configuring DMARC records is the use of overly restrictive alignment modes. For example, setting adkim and aspf to s (strict alignment) can lead to a higher number of false positives, as it requires an exact match between the sender's domain and the domain in the From header. In contrast, using relaxed alignment (adkim and aspf set to r) can help reduce false positives, as it allows for a more flexible matching of domains.
Another important consideration when analysing DMARC record examples is the use of subdomains. In some cases, subdomains may not be properly configured, leading to false positives. For instance, if a company uses a subdomain (sub.example.com) for their email marketing campaigns, but the subdomain is not properly configured in the DMARC record, it may lead to false positives.
To mitigate this issue, it's essential to ensure that all subdomains are properly configured in the DMARC record. This can be done by adding a separate DMARC record for each subdomain or by using a wildcard record that covers all subdomains. For example:
*.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1"In this example, the wildcard record (*.example.com) covers all subdomains, ensuring that they are properly configured and reducing the risk of false positives.
In addition to analysing DMARC record examples, it's also essential to monitor aggregate reports to identify potential issues. Aggregate reports provide valuable insights into email delivery and can help identify false positives. By analysing these reports, email administrators can identify trends and patterns that may indicate false positives and take corrective action to mitigate them.
In a hosted or managed setup, aggregate reports are typically provided to customers on a regular basis, allowing them to monitor and analyse email delivery. The reports can be used to identify issues with DMARC records, such as misconfigured subdomains or overly restrictive alignment modes, and take corrective action to mitigate false positives.
In conclusion to this section, analysing DMARC record examples is a critical step in mitigating false positives. By understanding the trade-offs between different parameters and avoiding common pitfalls, email administrators can configure DMARC records that reduce false positives and improve email deliverability. In the next section, we will provide operational guidance for mitigating false positives, including best practices for configuring DMARC records and monitoring aggregate reports.
Operational Guidance for Mitigating False Positives
Mitigating false positives in DMARC reporting requires a centre of attention on the operational aspects of DMARC record management, as well as a colour of understanding of the email ecosystem. At DMARC Engine, we have seen firsthand the impact of false positives on email deliverability, and we have developed strategies to optimise DMARC records and reduce false positives.
One key aspect of mitigating false positives is to ensure that the DMARC record is correctly configured. This includes setting the correct policy, such as p=none or p=quarantine, and ensuring that the record is properly formatted. For example, a DMARC record with a typo in the policy field can lead to false positives, as seen in the following example:
_dmarc.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:dmarc@example.com" In this example, the policy field is set to p=none, which means that the receiver should not take any action on emails that fail DMARC validation. However, if the policy field were set to p=quarantine, the receiver might quarantine emails that fail DMARC validation, leading to false positives.
Another important aspect of mitigating false positives is to ensure that the SPF record is correctly configured. This includes ensuring that all sending IPs are included in the SPF record, and that the record is properly formatted. For example, an SPF record with a missing IP address can lead to false positives, as seen in the following example:
example.com. IN TXT "v=spf1 ip4:192.0.2.1 ip4:192.0.2.2 -all" In this example, the SPF record includes two IP addresses, 192.0.2.1 and 192.0.2.2. However, if there is a third IP address, 192.0.2.3, that is not included in the SPF record, emails sent from that IP address might fail DMARC validation, leading to false positives.
DKIM configuration is also crucial in mitigating false positives. This includes ensuring that the DKIM key is correctly configured, and that the key is properly rotated. For example, a DKIM key with a low key size can lead to false positives, as seen in the following example:
default._domainkey.example.com. IN TXT "v=DKIM1; k=rsa; p=MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQCqGKukO1De7zhZj6+H0qtjTkVxwTCpvKe4eCZ0FPqri0cb2JZfXJ/DgYSF6vUpwmJG8wVQZKjeGcjDOL5UlsuusFncCzWBQ7RKNUSesmQRMSGkVb1/3j+skZ6UtW+5u09lHNsj6tQ51s1SPrCBkedbNf0Tp0GbMJDyR4e9T04ZZwIDAQAB" In this example, the DKIM key size is 512 bits, which is considered low. A larger key size, such as 2048 bits, is recommended to reduce the risk of false positives.
In a hosted or managed setup, such as DMARC Engine, the configuration of DMARC, SPF, and DKIM records is handled by the provider. This can help reduce the risk of false positives, as the provider can ensure that the records are correctly configured and properly formatted. However, it is still important for the customer to understand the configuration of their records, and to work with the provider to optimise their records for reduced false positives.
Another strategy for mitigating false positives is to use a third-party service to monitor and analyse DMARC reports. These services can help identify false positives, and provide recommendations for optimising DMARC records. For example, DMARC Engine provides a dashboard for monitoring DMARC reports, and offers recommendations for optimising DMARC records based on the data in the reports.
In addition to these strategies, it is also important to regularly review and update DMARC records to ensure that they are correctly configured and properly formatted. This can help reduce the risk of false positives, and ensure that emails are delivered to the inbox. For example, a company that sends emails from multiple IP addresses should regularly review their SPF record to ensure that all IP addresses are included.
It is also worth noting that the use of BIMI (Brand Indicators for Message Identification) can help reduce the risk of false positives. BIMI is a standard for authenticating email senders, and can help ensure that emails are delivered to the inbox. For example, a company that uses BIMI can include a BIMI record in their DNS, which can help authenticate their emails and reduce the risk of false positives.
In terms of trade-offs, one of the main trade-offs in mitigating false positives is the balance between security and deliverability. For example, a company that sets a strict DMARC policy, such as p=reject, may reduce the risk of phishing attacks, but may also increase the risk of false positives. On the other hand, a company that sets a lenient DMARC policy, such as p=none, may reduce the risk of false positives, but may also increase the risk of phishing attacks.
To optimise DMARC records for reduced false positives, it is recommended to start with a lenient policy, such as p=none, and gradually increase the strictness of the policy over time. This can help ensure that the DMARC record is correctly configured, and that the risk of false positives is reduced. For example, a company that starts with a lenient policy, such as p=none, can gradually increase the strictness of the policy to p=quarantine, and finally to p=reject.
In conclusion to this section, mitigating false positives in DMARC reporting requires a centre of attention on the operational aspects of DMARC record management, as well as a colour of understanding of the email ecosystem. By ensuring that DMARC, SPF, and DKIM records are correctly configured, using third-party services to monitor and analyse DMARC reports, and regularly reviewing and updating DMARC records, companies can reduce the risk of false positives and ensure that emails are delivered to the inbox.
Trade-Offs in False Positive Mitigation Strategies
When mitigating false positives in DMARC reporting, it is crucial to consider the trade-offs involved in various strategies. A common approach is to adjust the DMARC policy to a more relaxed setting, such as switching from p=reject to p=quarantine, to reduce the number of false positives. However, this approach can also increase the risk of phishing attacks, as malicious emails may be allowed to reach the recipient's inbox. For instance, a customer of ours, a large financial institution, opted for a p=quarantine policy to avoid blocking legitimate emails from their marketing team, but this led to an increase in phishing attempts reaching their customers' inboxes.
Another strategy is to implement a more granular DMARC record, with separate policies for different subdomains or mail streams. This approach can help reduce false positives by allowing for more precise control over email authentication. For example, a company may have a subdomain for marketing emails that requires a more relaxed DMARC policy, while their primary domain requires a stricter policy. A hosted DMARC setup, such as the one we offer at DMARC Engine, can simplify the process of managing multiple DMARC records and policies.
_dmarc.marketing.example.com. IN TXT "v=DMARC1; p=quarantine; pct=100; rua=mailto:rua@example.com; ruf=mailto:ruf@example.com; fo=1" In this example, the marketing subdomain has a p=quarantine policy, while the primary domain may have a p=reject policy.
The use of wildcard records is another approach to mitigate false positives. Wildcard records allow for the authentication of emails from subdomains that may not be explicitly listed in the DMARC record. However, this approach can also increase the risk of spoofing attacks, as an attacker may be able to send emails from a subdomain that is not properly authenticated. To mitigate this risk, it is essential to carefully monitor DMARC reports and adjust the wildcard record as needed.
*.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:rua@example.com; ruf=mailto:ruf@example.com; fo=1" In this example, the wildcard record applies to all subdomains of example.com, but the p=none policy means that emails from these subdomains will not be blocked, even if they fail authentication.
The percentage of emails to which the DMARC policy is applied is another critical factor to consider when mitigating false positives. The pct tag in the DMARC record allows for the specification of a percentage of emails to which the policy should be applied. For instance, setting pct=50 would apply the DMARC policy to 50% of emails. This approach can help reduce the impact of false positives, but it can also reduce the effectiveness of the DMARC policy in preventing phishing attacks.
example.com. IN TXT "v=DMARC1; p=reject; pct=50; rua=mailto:rua@example.com; ruf=mailto:ruf@example.com; fo=1" In this example, the DMARC policy is applied to 50% of emails, which can help reduce the number of false positives, but may also allow some phishing emails to reach the recipient's inbox.
In a hosted or managed setup, such as the one we offer at DMARC Engine, the pct tag can be adjusted based on the customer's specific needs and the analysis of their DMARC reports. Our system can also automatically adjust the pct tag to optimise the balance between false positives and phishing protection.
The Organisation Domain (OD) concept in DMARC is another critical aspect to consider when mitigating false positives. The OD is the domain that is specified in the FROM header of an email, and it is used to determine which DMARC record should be applied. However, in some cases, the OD may not match the domain of the email sender, which can lead to false positives. To mitigate this risk, it is essential to carefully monitor DMARC reports and adjust the DMARC record as needed to ensure that the correct OD is being used.
In addition to these strategies, it is also essential to consider the impact of email forwarding on DMARC reporting. Email forwarding can cause emails to fail authentication, leading to false positives. To mitigate this risk, it is crucial to use a DMARC record that takes into account email forwarding, such as by using the fo tag to specify the forwarding behaviour.
example.com. IN TXT "v=DMARC1; p=reject; pct=100; rua=mailto:rua@example.com; ruf=mailto:ruf@example.com; fo=1" In this example, the fo tag is set to 1, which means that emails that are forwarded will be subject to the DMARC policy.
In conclusion to this section, mitigating false positives in DMARC reporting requires a careful consideration of the trade-offs involved in various strategies. By adjusting the DMARC policy, implementing granular DMARC records, using wildcard records, adjusting the percentage of emails to which the policy is applied, and considering the Organisation Domain concept and email forwarding, organisations can reduce the number of false positives while also protecting against phishing attacks. A hosted or managed setup, such as the one we offer at DMARC Engine, can simplify the process of managing DMARC records and policies, and can help organisations optimise their DMARC setup to reduce false positives.
Case Studies of Successful False Positive Mitigation
Mitigating false positives in DMARC reporting is crucial for maintaining the integrity of email deliverability. At DMARC Engine, we have encountered numerous cases where false positives have significantly impacted our customers' ability to reach their intended audience. In this section, we will delve into specific case studies that highlight successful strategies for mitigating false positives, including the trade-offs and recommendations that arose from these experiences.
One notable case involved a large e-commerce company that was experiencing a high volume of false positives due to a third-party email service provider (ESP) that was not properly aligned with their DMARC record. The ESP was sending emails on behalf of the company, but these emails were not being authenticated correctly, resulting in a significant number of false positives. To mitigate this issue, we worked with the company to implement a subdomain-specific DMARC record that allowed them to isolate and manage the ESP's email traffic more effectively.
_dmarc.esp.example.com. IN TXT "v=DMARC1; p=none; sp=none; adkim=r; aspf=r; fo=1" This approach enabled the company to maintain a more stringent DMARC policy for their primary domain while allowing for more flexibility in managing the ESP's email traffic. The adkim=r and aspf=r tags in the DMARC record specify relaxed alignment for DKIM and SPF, respectively, which helped to reduce the number of false positives.
Another case study involved a financial services company that was experiencing false positives due to a misconfigured SPF record. The company had recently changed their email infrastructure, but their SPF record had not been updated to reflect the new IP addresses. As a result, emails sent from the new infrastructure were being flagged as false positives. To resolve this issue, we worked with the company to update their SPF record to include the new IP addresses.
example.com. IN TXT "v=spf1 ip4:192.0.2.1 ip4:192.0.2.2 include:_spf.example.net -all" We also recommended that the company implement a monitoring system to track changes to their email infrastructure and ensure that their SPF record remains up-to-date. This approach helped to minimise the risk of false positives and ensure that the company's emails were delivered reliably.
In a hosted or managed setup, such as the one provided by DMARC Engine, mitigating false positives can be more straightforward. Our platform provides automated tools for analysing DMARC reports and identifying potential issues, including false positives. We also offer expert guidance and support to help our customers implement effective mitigation strategies. For example, our platform can help customers to identify and manage third-party ESPs, update SPF records, and implement subdomain-specific DMARC policies.
However, even with automated tools and expert support, mitigating false positives requires careful consideration of the trade-offs involved. For instance, relaxing the alignment settings in a DMARC record can help to reduce false positives, but it may also increase the risk of spoofing. Similarly, implementing a more stringent DMARC policy can help to prevent spoofing, but it may also increase the risk of false positives.
To illustrate this point, consider the case of a company that implemented a DMARC policy with strict alignment settings (adkim=s and aspf=s) in an effort to prevent spoofing. While this approach was effective in preventing spoofing, it also resulted in a significant number of false positives due to the company's use of third-party ESPs that were not properly aligned with their DMARC record. To mitigate this issue, the company had to relax the alignment settings in their DMARC record, which increased the risk of spoofing.
example.com. IN TXT "v=DMARC1; p=quarantine; sp=none; adkim=r; aspf=r; fo=1" This example highlights the importance of carefully evaluating the trade-offs involved in mitigating false positives and implementing a DMARC policy that balances the need to prevent spoofing with the need to ensure reliable email delivery.
In addition to the technical considerations, it is also important to consider the organisational and process-related aspects of mitigating false positives. For example, companies should ensure that they have a clear understanding of their email ecosystem, including all the third-party ESPs and infrastructure used to send emails on their behalf. They should also establish processes for monitoring and updating their DMARC records, SPF records, and other email infrastructure to ensure that they remain aligned and effective.
In conclusion to this section, mitigating false positives in DMARC reporting requires a combination of technical expertise, organisational awareness, and careful consideration of the trade-offs involved. By implementing effective mitigation strategies, such as subdomain-specific DMARC records, updated SPF records, and automated monitoring tools, companies can help to ensure reliable email delivery and maintain the integrity of their email ecosystem. At DMARC Engine, we have seen firsthand the impact that effective false positive mitigation can have on email deliverability, and we recommend that all companies prioritise this aspect of their email security strategy.
Optimising DMARC Records for Reduced False Positives
To effectively mitigate false positives in DMARC reporting, it is crucial to optimise DMARC records, taking into account the nuances of SPF, DKIM, and the DMARC policy itself. A well-crafted DMARC record can significantly reduce the occurrence of false positives, thereby improving email deliverability.
One of the key aspects of optimising DMARC records is ensuring that the SPF record is correctly configured. The SPF record should include all the IP addresses that are authorised to send emails on behalf of the domain. For instance, if a domain uses a third-party email service provider, the IP addresses of that provider should be included in the SPF record.
Example of an SPF record:
v=spf1 include:_spf.example.com ip4:192.0.2.1 ip4:192.0.2.2 -allIn this example, the SPF record includes the IP addresses 192.0.2.1 and 192.0.2.2, as well as the IP addresses specified in the _spf.example.com record. The -all directive at the end of the record indicates that any IP address not explicitly listed should be rejected.
However, it is essential to note that over-optimising the SPF record can lead to false negatives, where legitimate emails are incorrectly flagged as spam. Therefore, it is vital to strike a balance between including all the necessary IP addresses and avoiding unnecessary inclusions.
Another critical aspect of optimising DMARC records is DKIM configuration. DKIM involves adding a digital signature to emails, which can be verified by the recipient's email server. To reduce false positives, it is crucial to ensure that the DKIM selector and private key are correctly configured.
Example of a DKIM record:
default._domainkey.example.com. IN TXT "v=DKIM1; k=rsa; p=MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQCq1RlH1j8zjK4Ow0+VzR9xT1Gg1y5ZtGZeR4W5s6N0Qx2+J8R1Q9jJ8P4Vz3ZMy6Z9GZeL9ZtGg1yJ8Qx2+J8P4Vz3ZMy6ZN0Qx2J8Qx2J8P4V"In this example, the DKIM record specifies the domain key selector default, the domain example.com, and the public key. The p parameter contains the public key, which is used to verify the digital signature added to emails.
In a hosted or managed setup, such as the one provided by DMARC Engine, the configuration of SPF and DKIM records is often automated, reducing the risk of human error. However, it is still essential to review and optimise these records regularly to ensure that they are correctly configured and aligned with the organisation's email infrastructure.
The DMARC policy itself also plays a critical role in mitigating false positives. The policy should be set to none or quarantine initially, to allow for monitoring and testing of the DMARC configuration. Once the configuration is verified to be correct, the policy can be set to reject, which will reject emails that fail DMARC validation.
Example of a DMARC record:
_dmarc.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:dmarc@example.com; ruf=mailto:dmarc@example.com; fo=1"In this example, the DMARC record specifies the policy none, which means that emails that fail DMARC validation will not be rejected. The pct parameter is set to 100, which means that the policy applies to all emails. The rua and ruf parameters specify the email addresses to which aggregate and failure reports should be sent, respectively.
To further reduce false positives, it is recommended to implement a subdomain-based DMARC configuration. This involves creating separate DMARC records for each subdomain, allowing for more granular control over email validation. For instance, a domain example.com may have subdomains mail.example.com and marketing.example.com, each with its own DMARC record.
Example of a subdomain-based DMARC record:
_dmarc.mail.example.com. IN TXT "v=DMARC1; p=none; pct=100; rua=mailto:dmarc@mail.example.com; ruf=mailto:dmarc@mail.example.com; fo=1"In this example, the DMARC record is specific to the mail.example.com subdomain, allowing for separate configuration and monitoring of email validation for this subdomain.
In addition to optimising DMARC records, it is also essential to monitor and analyse DMARC reports regularly. These reports provide valuable insights into email validation and can help identify potential issues with the DMARC configuration. By reviewing these reports, organisations can identify and mitigate false positives, improving email deliverability and reducing the risk of email-based threats.
In a real-world scenario, a company like Example Inc. may have a complex email infrastructure, with multiple subdomains and third-party email service providers. To optimise their DMARC records, they would need to ensure that all the IP addresses of their email service providers are included in their SPF record, and that their DKIM configuration is correctly set up. They would also need to implement a subdomain-based DMARC configuration, to allow for more granular control over email validation. By doing so, they can reduce the occurrence of false positives, improve email deliverability, and enhance the overall security of their email infrastructure.
In short, optimising DMARC records is a critical step in mitigating false positives and improving email deliverability. By ensuring that SPF and DKIM records are correctly configured, implementing a subdomain-based DMARC configuration, and monitoring DMARC reports regularly, organisations can reduce the risk of false positives and enhance the security of their email infrastructure. In a hosted or managed setup, such as the one provided by DMARC Engine, the configuration of DMARC records is often automated, but it is still essential to review and optimise these records regularly to ensure that they are correctly configured and aligned with the organisation's email infrastructure.
To take it a step further, organisations should also consider implementing additional security measures, such as BIMI and MTA-STS, to further enhance the security of their email infrastructure. BIMI, or Brand Indicators for Message Identification, allows organisations to specify a logo that should be displayed next to their emails in the recipient's inbox, providing an additional layer of authentication. MTA-STS, or Mail Transfer Agent Strict Transport Security, allows organisations to specify a policy for the use of TLS encryption when sending emails, providing an additional layer of security. By implementing these additional security measures, organisations can further reduce the risk of email-based threats and improve the overall security of their email infrastructure.
Ultimately, the key to mitigating false positives and improving email deliverability is to ensure that DMARC records are correctly configured and aligned with the organisation's email infrastructure. By optimising DMARC records, implementing a subdomain-based DMARC configuration, and monitoring DMARC reports regularly, organisations can reduce the occurrence of false positives, improve email deliverability, and enhance the overall security of their email infrastructure.
Implementing and Monitoring False Positive Mitigation Plans
Implementing a false positive mitigation plan for DMARC requires careful consideration of the potential impact on email deliverability, as well as ongoing monitoring to ensure the plan is effective. A key aspect of this is organising the DMARC records to optimise the flow of legitimate email while minimising the risk of false positives. For example, the DMARC record for a large online retailer might include multiple SPF and DKIM entries to account for different email streams, such as marketing and transactional emails.
_dmarc.example.com. IN TXT "v=DMARC1; p=reject; pct=100; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1" In a hosted or managed setup, such as the one provided by DMARC Engine, the centre of attention is on ensuring that the DMARC records are correctly configured to handle the complexities of email streams from various sources. This includes handling colour-coded reports to highlight potential issues, and providing tools to optimise the DMARC records for reduced false positives.
When implementing a false positive mitigation plan, it is crucial to consider the trade-offs between blocking malicious email and allowing legitimate email to reach its destination. A common approach is to use a combination of SPF and DKIM to authenticate email, and then use the DMARC policy to determine the action to take when an email fails authentication. For instance, a company might use a DMARC policy of p=none to monitor email authentication without blocking any emails, and then adjust the policy to p=quarantine or p=reject once they are confident that the authentication is working correctly.
Monitoring the effectiveness of a false positive mitigation plan is also vital, as it allows for the identification of potential issues before they become major problems. This can be done by regularly reviewing the aggregate reports, such as the one shown below, to identify any trends or patterns that may indicate a problem with the DMARC configuration.
<feedback>
<version>1</version>
<record>
<row>
<source_ip>192.0.2.1</source_ip>
<count>10</count>
<policy_evaluated>
<disposition>none</disposition>
<dkim>fail</dkim>
<spf>fail</spf>
</policy_evaluated>
</row>
</record>
</feedback>In this example, the report shows that 10 emails from the IP address 192.0.2.1 failed both DKIM and SPF authentication, but were not blocked due to the DMARC policy being set to none. This could indicate a potential problem with the email authentication configuration, and would require further investigation to determine the cause.
To mitigate false positives, it is also important to ensure that the DMARC records are correctly configured to handle subdomains. This can be done by using the sp and adkim tags in the DMARC record to specify the policy for subdomains. For example:
_dmarc.example.com. IN TXT "v=DMARC1; p=reject; sp=none; adkim=r; aspf=r; pct=100; rua=mailto:aggrep@example.com; ruf=mailto:forensics@example.com; fo=1"In this example, the sp tag is set to none, which means that the DMARC policy will not be applied to subdomains. The adkim and aspf tags are set to r, which means that the DMARC policy will be applied to subdomains only if the subdomain has its own DMARC record.
In addition to configuring the DMARC records correctly, it is also important to ensure that the email authentication configuration is correct. This includes ensuring that the SPF and DKIM records are correctly configured, and that the email servers are correctly configured to use these records. For example, the SPF record for a company might include the IP addresses of all the email servers that are authorised to send email on behalf of the company.
example.com. IN TXT "v=spf1 ip4:192.0.2.1 ip4:192.0.2.2 include:_spf.example.net -all"In this example, the SPF record includes the IP addresses 192.0.2.1 and 192.0.2.2, which are the IP addresses of the company's email servers. The record also includes the include tag, which allows the company to include the SPF record of a third-party email service provider.
In a hosted or managed setup, the email authentication configuration is typically handled by the provider, who will work with the company to ensure that the configuration is correct. This can include providing tools to help the company configure their SPF and DKIM records, as well as monitoring the email authentication configuration to ensure that it is working correctly.
Overall, implementing and monitoring a false positive mitigation plan for DMARC requires careful consideration of the potential impact on email deliverability, as well as ongoing monitoring to ensure the plan is effective. By configuring the DMARC records correctly, ensuring that the email authentication configuration is correct, and monitoring the effectiveness of the plan, companies can help to mitigate the risk of false positives and ensure that their email is delivered to its intended recipients.