Anomaly detection in generative AI models

Anomaly detection helps identify patterns that fall outside what is considered normal communication behavior. Generative artificial intelligence (AI) takes this further by learning how legitimate emails look, sound, and behave, then using that understanding to detect messages that deviate from these learned norms.

Generative models improve anomaly detection by producing examples of legitimate data and contrasting them with real-world inputs. In a generative adversarial network (GAN) based setup, the generator creates synthetic safe emails while the discriminator evaluates how closely these samples match real ones. When an incoming email differs from these learned examples, it receives a high anomaly score, indicating possible phishing, impersonation, or malware activity.

A 2024 study on Generative Anomaly Detection using Prototypical Networks (GAD-PN) supports the strength of this approach. The authors found that “GAD-PN achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data,” and that performance “improved by approximately 30% compared with traditional unsupervised learning models.” Although the research targeted power plant leak detection, it provides a comparative view on how generative models can reliably detect anomalies, even when labeled abnormal data are scarce.

Applying the same logic to email security, generative AI can simulate a variety of legitimate communication patterns and use those baselines to identify suspicious deviations. Much like GAD-PN adapts across different industrial environments, email-focused generative models can adjust to evolving phishing tactics and organization-specific communication habits.

The threats in everyday emails 

A single phishing message can do far more than steal login credentials. It can open the door to systemwide breaches, exposing protected health information (PHI), enabling financial manipulation, or triggering ransomware attacks. Studies from the Journal of Medical Internet Research has shown that “the health sector has become a primary target of adapted cybersecurity attacks,” with attackers exploiting the crisis to launch ransomware, phishing, DDoS, and malware campaigns against hospitals, pharmaceutical companies, and health supply chains.

Many healthcare workers receive little to no cybersecurity training, which makes them easy targets. During the pandemic, staff often shifted focus from security to patient care, placing themselves in a more exposed position: “health services staff often have limited previous experience with remote working…which leaves the sector vulnerable to cyberattacks.” Misaddressed emails, misplaced attachments, or accidental sharing with unauthorized recipients remain some of the most common causes of data exposure.

The danger grows when staff access corporate email on personal or outdated devices. Endpoint vulnerabilities, like unpatched systems or insecure networks, create entry points for malware hidden in links or attachments. Business email compromise in healthcare has proven especially costly, as attackers exploit trusted communication channels to send fraudulent billing or insurance requests. 

During COVID-19, phishing and ransomware incidents affected organizations ranging from Brno University Hospital, which had to postpone surgeries, to the WHO, which faced credential-stealing attacks. The financial and operational impact can be severe, particularly for organizations managing high volumes of patient and insurance communications daily.

How generative AI contributes to smart email protection 

Generative AI learns what normal communication looks like by studying large sets of real-world email data. Once it understands those patterns, it can spot messages that feel off or break from expected behavior. As one paper published in Frontiers in Artificial Intelligence notes, “Generative AI represents a significant departure from classical algorithmic methods,” because it doesn’t just follow fixed rules; it evolves autonomously to produce new outputs and adapt to new threats. Its ability to “employ latent space manipulation and probabilistic modelling” allows it to analyze subtle variations in tone, structure, or metadata that traditional filters often miss.

GANs, for example, can create fake but convincing emails, allowing security systems to train on realistic threats and recognize subtle signs of deception before they slip through. That back-and-forth training process helps strengthen defenses against the constantly evolving wave of email attacks.

Generative AI adds another layer of protection through anomaly detection. It learns the rhythm of an organization’s email traffic, who sends what, when, and how, and builds a baseline for normal behavior. 

When an email strays too far from that pattern, the system flags it for review. In healthcare, where a single compromised email can expose sensitive patient information, that kind of early warning is invaluable.

The technology also shows promise in catching deepfake-driven phishing attempts. Attackers now use AI to create synthetic voices and images that mimic real people, making fraudulent emails far harder to spot. Generative AI can counter this by detecting the tiny inconsistencies and digital fingerprints left behind in fake content. 

For hospitals and clinics, where emails can authorize medical procedures or transfer patient records, identifying these threats early can prevent serious breaches. Strong generative AI systems also balance detection with ethical safeguards, ensuring that automated defenses protect privacy and avoid introducing new risks or biases.

When generative AI and anomaly detection work together 

Deep learning architectures like convolutional neural networks (CNNs) can examine multiple parts of an email simultaneously, headers, body, and attachments. This multi-layered approach merges generative AI’s ability to create realistic content with anomaly detection’s skill at recognizing patterns. 

As Khan et al. (2025) discuss in ‘AI-driven cybersecurity framework for software development based on the ANN-ISM paradigm,’ AI systems in cybersecurity “help us adapt a little better, as traditional measures in security have failed to respond to the upcoming threats.” For instance, some systems score emails based on their content and the sender’s history, flagging messages that fall outside expected norms. These methods outperform traditional classifiers that often fail on complex email structures or brand-new phishing techniques.

Generative AI can also simulate phishing emails that mimic threats, giving anomaly detectors continuous training to recognize even subtle deviations among legitimate messages. This aligns with the study’s findings that “AI outperforms traditional systems in detecting security weaknesses and simultaneously fixing problems,” showing AI’s real-time adaptation and predictive capabilities.

Beyond simple keyword scanning, generative AI improves content inspection by understanding the meaning and context of messages. This allows the detection of cleverly crafted phishing attempts, impersonations, and malicious attachments that look legitimate. When anomaly detection is applied to AI-generated representations of email flows, it captures irregularities throughout the communication, providing accurate, high-fidelity identification of threats.

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

FAQs

What is generative AI?

Generative AI is a branch of artificial intelligence that creates new content, like text, images, audio, or code, by learning patterns from existing data.

What makes generative AI different from traditional AI?

Traditional AI focuses on recognizing patterns and making predictions. Generative AI goes a step further, it produces new data rather than just interpreting existing information.

Is generative AI safe to use?

Generative AI can be safe when used responsibly, but it comes with risks. Poorly monitored models may produce inaccurate, biased, or misleading content.