How MSSPs detect threats email gateways miss

Traditional email gateways still lean on fixed rules, blacklists, and signature matching. These tools work well for known threats but struggle with modern attacks that shift tactics, hide malicious content, or mimic real conversations. Attackers now use obfuscation, social engineering, and rapidly evolving payloads to slip past static filters. 

As ‘Improving phishing email detection performance through deep learning with adaptive optimization’ notes, “Phishing email attacks are becoming increasingly sophisticated, placing a heavy burden on cybersecurity, which requires more advanced detection techniques.” The sophistication also shows in the numbers: the same research reports an accuracy rate of 96.8% in detecting phishing emails when deep learning models are optimized with approaches like the Mountain Gazelle Optimizer, far outperforming traditional filters.

A major advantage MSSPs bring is their use of advanced phishing detection models built on deep learning. Architectures that blend Bidirectional Encoder Representations from Transformers (BERT), Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRU), and attention layers analyze the deeper context of email text rather than just scanning for keywords. BERT’s bidirectional embeddings capture how words relate to each other across the entire message, which makes cues like urgent requests, subtle spoofing, or abnormal tone easier to flag. 

MSSPs also use ensemble machine learning to boost detection power. Instead of relying on one algorithm, they stack models such as Naïve Bayes, k-nearest neighbors, logistic regression, and XGBoost. Each model contributes a different perspective, and the combined output produces stronger, more reliable predictions. These ensembles handle imbalanced email datasets more effectively by using oversampling, undersampling, and smart voting layers.

Why email gateways miss emerging threats

Email gateways sit between an organization’s internal network and the outside world, acting as the first checkpoint for every message that comes in or goes out. They translate protocols when needed and inspect message headers, bodies, attachments, and metadata to decide whether an email should be delivered, tagged, quarantined, or rejected. 

The problem is that traditional gateways have real blind spots. They rely heavily on static rules, heuristics, and signature matching, methods that work well for known threats but fall apart when attackers change their techniques. As Accurate and Scalable Detection and Investigation of Cyber Persistence Threats explains, “Advanced Persistent Threat (APT) attacks are increasingly leveraging Living-Off-the-Land Binaries (LOLBins), shifting the strategic focus from traditional malware to more nuanced persistence techniques.” 

These techniques aren’t built around obvious malicious payloads. They mimic normal system behavior, making them far harder for rule-based gateways to detect. The same research highlights how stealthy attackers can be, noting that “persistence techniques were a key feature in nearly 75% of cyberattacks in 2022.”

Modern phishing and malware campaigns follow the same playbook: HTML obfuscation, randomized text, image-based payloads, and zero-day exploitation designed to blend into normal email traffic. Gateways tuned to catch yesterday’s threats often miss these newer, low-signal attacks crafted to evade pattern-matching filters.

The shift toward persistent 24/7 threat monitoring

CNNs catch suspicious phrase patterns, and GRUs follow the flow of the message to spot inconsistencies that attackers try to hide. When paired with optimizers like the Mountain Gazelle Optimizer, these models reach higher accuracy and cut down on false positives far better than traditional filters.

Deep learning also leans heavily on ensemble machine learning because, as one PLoS One study explains, “Algorithmic ensemble methods consistently outperform individual models in detection accuracy.” Instead of trusting a single classifier, MSSPs stack models like Naïve Bayes, k-nearest neighbors, logistic regression, and XGBoost so each contributes its strengths. 

Real-world email traffic is overwhelmingly legitimate, and filtering systems must work against an extreme imbalance. Ensemble techniques help correct that imbalance, and the research backs it up; one paper notes that “the enhanced model was rigorously evaluated… demonstrating statistically significant improvements over both baseline models and existing solutions,” reporting accuracy as high as 99.79%.

Advanced behavioral analytics that spot hidden email risks

MSSPs pull data from global threat feeds and their own incident histories to score domains and IPs based on past behavior and hosting details. Newly registered domains, especially ones mimicking well-known brands or medical institutions, are treated with caution because attackers often use them for phishing. 

As ‘A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience’ explains, “Cyber threat intelligence (CTI) enhances organizational cybersecurity resilience by obtaining, processing, evaluating, and disseminating information about potential risks and opportunities inside the cyber domain.” By checking domain reputation in real time, MSSPs can block or warn users about risky senders long before traditional gateways update their blocklists. By checking domain reputation in real time, MSSPs can block or warn users about risky senders long before traditional gateways update their blocklists.

URL analysis goes far deeper than simple blacklisting. MSSPs look at redirect paths, hosting infrastructure, and embedded scripts to identify fake login pages designed to steal credentials, which are common in BEC and healthcare-focused phishing campaigns. They pair this with external intelligence on phishing kits being sold or shared online. When a new kit appears, URLs tied to it can be flagged immediately, helping MSSPs stop attacks before they spread in clinical environments.

This aligns with CTI research showing that “the proposed framework is comprised of a knowledge base, detection models, and visualization dashboards,” all working together to turn raw threat data into actionable defenses.

DMARC enforcement adds another layer of protection. MSSPs monitor and apply strict DMARC policies to prevent domain spoofing, ensuring messages that fail authentication are rejected or quarantined. This reduces impersonation attacks that exploit trusted healthcare domains. Consistent DMARC monitoring, combined with threat intel, helps detect unusual sender activity early. 

Machine learning and threat intelligence enrichment

MSSPs use machine learning models trained on huge, varied datasets of both legitimate and malicious emails. Over time, these models learn the patterns that give dangerous messages away, whether it’s a subtle brand impersonation, a suspicious URL, or the pacing and tone that often signal a phishing attempt. According to Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models deep learning architectures like BERT paired with CNNs and GRUs routinely reach detection rates above 95%, and they do it with far fewer false positives than traditional filters. That accuracy helps MSSPs spot new threats earlier and with more confidence.

Threat intelligence enrichment takes this even further. MSSPs pull in real-time data from domain reputation services, URL feeds, dark web monitoring, and security community sources to add context that models alone can’t see. This is how they catch newly registered look-alike domains, fresh phishing kits, or fake login pages long before secure email gateways update their blocklists. When a new phishing kit shows up on an underground forum, MSSPs can flag related URLs almost immediately and block them before they reach inboxes.

They enforce authentication standards like DMARC, which closes one of the biggest gaps SEGs struggle with–domain spoofing. Continuous DMARC monitoring helps MSSPs catch attempts to impersonate trusted hospital or clinic domains, an extremely common tactic in healthcare-focused phishing. When you combine ML-powered detection, threat intelligence, domain reputation scoring, and strict policy enforcement, you get a layered defense that spots threats earlier, responds faster, and cuts down the window attackers have to do damage.

See also: HIPAA Compliant Email: The Definitive Guide (2025 Update)

FAQs

How does an email gateway block threats?

It scans email headers, bodies, and attachments for known indicators of spam, phishing, or malware, then blocks, tags, or quarantines suspicious messages.

Why do companies use email gateways?

Organizations use gateways to reduce spam, prevent malware infections, and protect users from phishing attacks before they ever hit an inbox.

Do email gateways catch all phishing emails?

No. Traditional gateways often miss new or highly sophisticated attacks because they rely heavily on static rules and signatures.

Can email gateways detect zero-day threats?

Most gateways struggle with zero-day threats because these attacks don’t match known patterns or signatures.