Generative AI is a machine learning model, like generative adversarial networks (GANs) and large language models, that can create new outputs based on patterns learned from existing data.
As described in the study ‘Is Generative AI Increasing the Risk for Technology‐Mediated Trauma Among Vulnerable Populations?’, “Generative AI automatically learns patterns and structures from texts, images, sounds, animations, models, or other media inputs to produce new ones with similar characteristics.” GANs operate through a feedback loop in which one model generates synthetic content while another evaluates whether it appears authentic.
Simulated adversarial activity allows security teams to test how systems respond to evolving attack techniques. It exposes weaknesses that static detection methods often miss. The study ‘Collaborative penetration testing suite for emerging generative AI algorithms’ shows that “AI-driven red team simulations emulate adversarial and quantum-assisted attacks, uncovering vulnerabilities overlooked by traditional methods.” For phishing simulation, generative AI produces highly convincing emails and social engineering scenarios used in red-team exercises and security training.
Large language models, including transformer-based systems such as GPT, generate text, code, and dialogue by learning how sequences of words tend to flow together. They rely on attention mechanisms that weigh the surrounding context.
A study indexed in Arxiv on the foundational work on GPT-3, the model was trained with “175 billion parameters” and demonstrated “strong performance on many NLP tasks without task-specific training,” often rivaling or surpassing fine-tuned systems in translation and question answering
Diffusion models begin with unstructured inputs and gradually shape them into usable outputs through repeated refinement. It is a process that makes them particularly effective for producing high-quality images, audio, and multimodal content. These models as especially relevant for cybersecurity because they can create realistic synthetic data, giving defenders tools that go beyond traditional, purely predictive approaches.
Within security operations, it translates directly into stronger threat security protocols. Generative models can analyze massive volumes of security data and surface subtle patterns that indicate emerging attacker behavior, such as anomalies hidden in network logs or early signals of new malware variants.
Large language models also support practical workflows by generating incident summaries, translating technical findings into readable reports, and creating realistic phishing examples for training and testing. Together, these functions help security teams anticipate attacks, improve human awareness, and respond more effectively to evolving threats.
Data ingestion and pattern extraction with generative AI start with pulling in large volumes of very different data, such as network logs, malware files, and collections of phishing emails. Before a model can learn from this information, the data has to be cleaned, standardized, and translated into a format the system can understand.
As the study ‘Generative AI cybersecurity and resilience’ explains, “Generative AI operates through self-evolving uses that can autonomously produce new data outputs,” relying on deep learning architectures that transform raw inputs into structured formats suitable for analysis across text, code, and other modalities.
Once the data is ingested, generative AI focuses on finding patterns that matter. Techniques such as autoencoders and contrastive learning help the model separate meaningful signals from background noise. The study notes that generative architectures such as GANs and VAEs “facilitate the generation of high-dimensional data by employing latent space manipulation and probabilistic modelling,” allowing systems to capture subtle behaviors that would otherwise remain hidden in large datasets.
This allows security platforms to spot subtle indicators of advanced persistent threats within SIEM data that might otherwise blend into normal activity. Pattern extraction then builds on these representations. Models like variational autoencoders and GAN-based systems learn what “normal” looks like and flag deviations, such as unusual email sequences that point to phishing campaigns or previously unseen exploit techniques.
Trend forecasting and behavior profiling with generative AI focus on predicting how attackers are likely to change their tactics over time. Instead of reacting only after an attack happens, these models analyze large collections of threat data to spot patterns that signal what may come next. Probabilistic models are especially useful for this kind of forward-looking analysis because they can estimate likely attacker behavior rather than relying on fixed rules.
Large language models analyze phishing activity. By reviewing large volumes of email data, LLMs learn to recognize common signals such as urgency-driven language, impersonation of trusted organizations. As the above mentioned ‘Generative AI cybersecurity and resilience’ explains, “Generative AI represents a significant departure from classical algorithmic methods,” because models such as GANs and VAEs can synthesize new outputs rather than simply classify existing ones. These patterns help anticipate how phishing campaigns may evolve, rather than just identifying copies of past attacks. Diffusion models add another layer by generating realistic variations of phishing messages.
Instead of waiting for threats to appear in the wild, security teams use advanced AI models to create realistic fake phishing emails that closely resemble the techniques used by cybercriminals. These simulations support red‑team exercises and defensive testing by reproducing the same pressure tactics attackers rely on.
A review indexed in PeerJ Computer Science notes, “Phishing attacks are now regarded as one of the most prevalent cyberattacks that often compromise the security of different communication and internet networks.”
These models are trained on large collections of past phishing emails, learning patterns such as how malicious links are structured, how attackers manipulate tone and timing, and how attachments are disguised. During training, one AI system generates convincing phishing messages while another evaluates how realistic they appear, forcing improvement.
Running these AI‑generated attacks in controlled environments allows organizations to stress‑test their email gateways without putting users at risk. Simulations can mirror complex, multi‑step campaigns, including constantly changing subject lines, fake documents, or messages.
AI-driven security analysis is increasingly integrated into healthcare email protection through platforms like Paubox’s generative AI for inbound email. These systems use advanced language models and smart databases directly within email gateways to evaluate messages in context, spotting threats before they reach users.
As a Risk Management Healthcare Policy study notes, “Artificial intelligence (AI) is revolutionizing the healthcare industry, improving diagnoses, treatments, and clinical processes. However, its integration poses significant cybersecurity risks, including data breaches, algorithmic opacity, and vulnerabilities in AI-controlled medical devices.” Incoming emails are analyzed for tone, sender behavior, and past patterns, all while staying compliant with HIPAA, allowing the system to automatically quarantine suspicious messages.
Integration with security platforms like SIEM and SOAR means AI-generated risk scores and concise, explainable summaries appear on analyst dashboards. Security teams can quickly prioritize alerts and follow pre-built response playbooks without sifting through false positives. These systems continuously improve through feedback, adapting to user input and refining detection models while protecting sensitive health information.
See also: HIPAA Compliant Email: The Definitive Guide (2025 Update)
Generative AI can automatically analyze incoming emails and network data to identify potential phishing or malware threats before they reach users.
Integration with platforms like SIEM or SOAR allows AI-generated insights to feed dashboards and automate incident response workflows.
Generative AI can simulate attack scenarios to test defenses and improve organizational preparedness against emerging threats.