Multimodal describes an AI system that can process and connect more than one kind of data, such as text, images, audio, and video. Instead of treating each input separately, the model can combine them to infer meaning across formats, for example reading a message, inspecting a screenshot, and analyzing a voice clip as one context.
In cyber security, multimodal AI matters because attacks and defenses are rarely text-only. Defenders use multimodal systems to spot phishing kits, deepfakes, malicious QR codes, suspicious screenshots, or manipulated audio. Attackers also benefit from the same capability: they can generate convincing fake videos, clone voices, and craft social-engineering lures that mix media types to bypass human scrutiny. As these systems become more capable, security teams need controls for provenance, content validation, and prompt-injection resistance across every input channel.