Email authentication reduces direct spoofing, but it does not reliably identify threats that use compromised accounts, trusted vendor identities, or lookalike domains.
Email authentication validates domains and message integrity, but sophisticated threats often require additional context to evaluate sender trust. For example, compromised accounts and lookalike domains can still produce messages that pass protocol checks.
Signals that can help identify higher-risk messages beyond authentication include:
Identity Analysis: Does the sender identity align with established relationships and expected roles?
Behavioral Analysis: Is the sending pattern, timing, or location unusual for this sender?
Header Analysis: Do technical headers indicate anomalies such as unusual routing or client artifacts?
Communication Patterns: Does the message fit the typical cadence, tone, and thread context of the relationship?
This is also where Abnormal can complement SPF, DKIM, and DMARC. Abnormal’s behavioral AI is designed to model known-good relationships and identity signals in cloud email, helping security teams surface account compromise and socially engineered messages that can still pass authentication checks.
As shown in the webinar, one practical indicator is unusual sign-in and sending behavior. If a vendor sender’s geolocation was not previously seen, or the IP address is new for that sender, those deviations can be worth investigation even when SPF, DKIM, and DMARC pass.
This is also where vendor risk monitoring can help. By tracking compromise indicators across trusted third parties, organizations can prioritize investigations and controls around higher-risk partners.