How generative adversarial networks power generative AI

Generative adversarial networks (GANs) work by synthesizing realistic adversarial examples that mirror the way attackers continuously evolve. As one Scientific Reports study on Android malware detection noted, “Adversarial examples, characterized by their adeptness in evading handheld security systems, pose formidable challenges for traditional detection methods,”. By generating synthetic phishing or malware samples, GANs help with defense systems to anticipate and recognize both known and novel threats that traditional signature-based methods might miss. 

The effectiveness of these techniques is evident in malware research. For example, AndrOpGAN, an Android Opcode Modification GAN, “Intelligently modifies opcode distribution features using the Opcode Frequency Optimal Adjustment algorithm,” successfully bypassing multiple classifiers. 

The authors also warn that more advanced frameworks like DOpGAN, a dual-opponent GAN, introduce “a grey-box attack strategy that misclassifies generated examples as benign, significantly evading detection.” Reported evasion capacities of GAN-based malware reach as high as 99% in some tests, underscoring both the potency of adversarial AI and the urgent need for robust countermeasures.

Applying these insights to email, generative AI security software can create diverse phishing samples that expose the subtle tricks hackers use to disguise danger. Much like opcode-level adversarial modifications in Android malware, GANs generate nuanced phishing email variants that traditional filters might misclassify as safe. 

The adversarial data trains detection models to identify phishing attempts based on behavioral patterns and contextual cues rather than exact keyword matches. As a result, phishing filters become sharper, catching both known campaigns and zero-day exploits.

What are generative adversarial networks?

A GAN is built around two models: the generator and the discriminator. The generator produces data samples designed to look real, while the discriminator evaluates whether a sample is genuine or generated. As they train together, each model improves by responding to the other’s performance. Over time, this process allows the generator to produce data that becomes harder and harder to distinguish from the real thing.

The generator’s task is to fool the discriminator, while the discriminator’s task is to spot the fakes. This setup is often described as a minimax game, where one model minimizes errors while the other maximizes accuracy. An editorial article in the Korean Journal of Radiology states, “This adversarial approach propels both networks to constantly improve: the generator by mimicking reality with ever-increasing precision, and the discriminator by detecting even the subtlest imperfections.” Ideally, the system reaches a point where the generator produces outputs that the discriminator can no longer reliably separate from real data. Unlike traditional methods that attempt to model data distributions directly, GANs learn these distributions implicitly through this adversarial process.

The link between GANs and generative AI advancements 

An Archives of Computational Methods in Engineering study noted, “Generative models unlike discriminative models, successfully aim to understand the underpinning data distribution by learning the fundamental parameters which enables model data analysis, extraction of novel intuitions and synthetic data generation such that conversion of a low dimension input to a high dimension output.”

Before GANs, generative models often struggled to create realistic data, especially with complex and high-dimensional sources like images, videos, or natural language. GANs changed this by letting two networks compete with each other, the generator trying to create convincing outputs and the discriminator trying to spot fakes. As we discussed, the back-and-forth allows the system to learn patterns in the data without having to calculate exact probabilities. It can now produce data that looks and feels real as well, while respecting the subtle context and details needed for advanced applications.

One of GANs’ biggest impacts is their ability to generate data varieties that were nearly impossible before. In computer vision, for example, they enable the creation of photorealistic images, high-resolution visuals, and even video sequences that flow naturally. GAN architectures, such as conditional GANs (cGANs), take this further by letting outputs be guided by labels or other information, giving precise control and making it possible to produce highly realistic synthetic data.

How GANs power email security 

Email related cyber threats can take the form of phishing campaigns, malware that changes form, social engineering attacks, and advanced persistent threats (APTs) that adapt to bypass traditional defenses. A study titled ‘A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes’ notes, “From the perspective of detection tasks, such as detecting malware and intrusion attempts on a network, GANs can augment threat detection mechanisms by simulating malicious behaviors, such as creating malware that can bypass antivirus or generating phishing emails that can fool both humans and machines.” 

Standard signature- or rule-based detection systems often struggle against these threats because attackers continually change their tactics. GANs help by creating realistic examples of phishing emails, malware, and other malicious messages. Instead of reacting only to known threats, GANs simulate new attack methods, helping identify weaknesses before they are exploited.

GANs are especially useful when real-world malicious examples are limited, sensitive, or quickly become outdated. In healthcare and other regulated fields, sharing real email datasets may be restricted, making it harder to build effective AI models. GANs can produce synthetic datasets that keep the patterns and structure of real emails without exposing sensitive information. These synthetic datasets expand the training pool and help AI models detect subtle threats more reliably.

The discriminator network in GANs also assists by learning to distinguish normal from malicious behaviors in email traffic. It strengthens anomaly detection systems against new phishing tricks or malware variants. Acting as an adaptive monitor, the discriminator spots unusual changes in sender behavior, message content, or attachments and can trigger alerts before attacks succeed. Using GANs in this way improves threat detection and supports proactive cybersecurity measures.

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

FAQs

What is generative AI?

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, audio, or video, based on patterns learned from existing data. Unlike traditional AI that focuses on analyzing or classifying data, generative AI produces original outputs.

What is the difference between GANs and VAEs?

GANs use a generator and discriminator in a competitive setup to produce realistic outputs, while VAEs encode data into a latent space and then decode it to generate new samples. GANs often produce more visually realistic outputs, while VAEs are more stable and interpretable.

What are conditional GANs (cGANs)?

cGANs are a type of GAN that generates outputs based on additional information, such as labels or attributes. For example, a cGAN can generate images of a specific type of animal or a specific style of artwork.