A diffusion model is a generative AI system that creates output by starting from noise and refining it through many iterative denoising steps. Instead of producing the final result in one pass, it gradually moves toward a coherent image, text, or other data type. This makes its runtime and quality depend heavily on how many steps are used.
In cyber security, diffusion models matter because they can be used to generate realistic content for both attack and defense. Attackers may use them to mass-produce convincing phishing text, synthetic personas, or evasive multimedia. Defenders may use them for content generation, data augmentation, or simulation, especially where privacy or local deployment is important. Their step-by-step process also creates operational trade-offs: more steps can mean better output but higher latency, cost, and a larger surface for monitoring or abuse detection.