VEC (Vendor Email Compromise) attacks are increasing at an alarming rate. In fact, the manufacturing sector alone climbed 24% year-over-year between September 2023 and September 2024. In today’s situation, emails are being exploited as both a weapon and a gateway. Companies usually trust their vendors as they have been in business for years, and often communicate via email. Moreover, they don’t cross-check their invoice requests, which is exactly what threat actors feed on.
VEC and BEC attacks have become a new critical blind spot, requiring companies to be proactive with vendor assessment drills. However, manual vendor assessments are slow, error-prone, and rarely keep pace with the dynamic nature of threats. This is where automation and machine learning step in to streamline email security checks, reducing human effort and providing real-time insights to detect anomalies before they escalate. The result? Stronger vendor governance, proactive risk management, and simplified compliance readiness without drowning in spreadsheets.
Challenges with Manual Vendor Email Security Checks
When it comes to assessing vendor email security, most business owners take the manual route, underestimating the complexities until they hit a bottleneck. Here are the pain points you can face if you choose to do it manually-
1. Time-consuming DNS record validations (SPF, DKIM, DMARC)
Validating email authentication records across multiple vendors is a continuous process and not a ‘once-done-and-forget’ job. You have to verify syntax, query DNS, and confirm proper alignment, which becomes all the more difficult if your vendor keeps updating devices and mail servers.
For a business that collaborates with hundreds of vendors, keeping up with this task translates into hours of work each month.
What makes this even worse is the fact that these manual checks often only happen during onboarding, leaving your IT infrastructure exposed to threats when vendors change the configurations in the future.
2. Lack of visibility into policy changes
Email authentication is not static because vendors continually modify their DNS records for legitimate reasons, such as adding marketing platforms, updating email gateways, or outsourcing support functions.
The problem lies in the fact that when these changes occur, business owners are not notified, and therefore, they do not make any changes to their SPF, DKIM, and DMARC records.
3. Human error in continuous compliance
When relying completely on spreadsheets or ticketing systems to track vendor email security, the chances of making silly mistakes increase. DNS records are lengthy and quite technical; therefore, even a slight mistake or misinterpreted alignment can render the entire effort invalid.
Compliance frameworks, such as SOC 2 and ISO 27001, also require proof of ongoing monitoring, not just one-time checks. Even a minor oversight during an audit can result in non-compliance findings, which can damage client trust and delay certification timelines.
4. Lack of stability for growing vendor ecosystems
Typically, when a business grows, the demand for working with vendors also multiplies. In some cases, they even collaborate with hundreds of vendors and imagine the manual process that goes behind keeping pace with this growth.
Onboarding each vendor, validating their email security posture, and re-checking periodically becomes an operational nightmare. In practice, many teams either conduct surface-level checks or skip re-validation altogether, introducing unmanaged risk across a wide supply chain. Automation solves this by enabling continuous, scheduled checks across all vendors without additional headcount.
5. Limited ability to detect anomalies and emerging threats
Manual checks are like point-in-time snapshots. They can tell you what your vendor looked like on a certain day, but they can’t detect subtle behavioural anomalies that are the loud red flags of cybercriminal movements.
Without automated anomaly detection using machine learning, these subtle warning signs often go unnoticed until a breach actually occurs. And that kind of ‘wait until it breaks’ approach is exactly what compliance frameworks want you to avoid.
How do automation and machine learning enhance vendor risk assessment?
In contrast to outdated static checks, machine learning and automation analyze patterns, predict risks, and connect the dots with global threat data to provide a more comprehensive view. Here is how ML + automation make vendor risk assessment way more efficient:
1. Pattern detection in vendor behaviour
One of the biggest advantages of using machine learning for vendor assessments is that it doesn’t just look at static records; it learns how those records normally behave over time. For example, if a vendor suddenly expands its SPF record to include multiple new IPs in one shot, or if their DKIM keys are rotating more frequently than usual, ML systems can flag that as a deviation from their baseline.
These aren’t always signs of compromise, but they’re the kind of changes that slip past manual checks. The power here is in consistency—ML notices the small shifts that a human analyst would only catch after digging through weeks of logs. That early flag gives security teams a head start to question the vendor before the risk escalates.
2. Predictive analysis for spoofing risks
Another layer ML brings is prediction. Instead of waiting for an attack to land, it can analyze vendor history, traffic patterns, and common spoofing techniques in the wild to highlight domains that might be misused next.
For instance, if a vendor operates multiple lookalike subdomains, or their naming conventions are easy to spoof, ML can flag those as high-risk before attackers weaponize them. This is where automation alone isn’t enough—yes, automation can check if a vendor has DMARC at reject, but only ML can anticipate where the gaps will be exploited. Predictive signals like these are crucial because by the time spoofed emails hit inboxes, the damage is already unfolding.
3. Integration with threat intelligence feeds
Machine learning doesn’t operate in a vacuum. It works best when tied into global threat intelligence feeds that constantly update with newly registered domains, active phishing kits, and compromised IP addresses.
The correlation piece is what makes it powerful—an ML model can map a vendor’s email activity against known malicious infrastructure and raise alerts if there’s an overlap. For example, if one of your vendors’ sending IPs suddenly starts showing up in a global spam feed, ML surfaces that connection immediately.
Without this correlation, organizations only find out about vendor compromise when users start reporting suspicious emails. With it, you’re already two steps ahead.
4. Continuous compliance- a bonus
Automation and ML complement each other in a way that directly supports compliance mandates. Automation takes care of repetitive, time-sensitive tasks—querying DNS, checking SPF/DKIM/DMARC records, running daily validations, without needing someone to babysit the process. ML, on the other hand, gives context and intelligence, spotting patterns, predicting misuse, and cross-checking against threat intel. Together, they provide what SOC 2 refers to as “continuous monitoring” and what ISO 27001 describes as “supplier relationship oversight.” Instead of point-in-time audits that become outdated the moment a vendor updates their records, this approach creates a living system that is always watching. The result is not only stronger security but also smoother audits, since organizations can show evidence of ongoing vendor monitoring without scrambling at the last minute.
