Steganography is the practice of hiding a message or signal inside another medium so it stays invisible unless you know how to look for it. In the context of AI-generated text, this usually means hiding a detectable pattern inside normal-looking words, rather than adding a visible label, icon, or disclaimer.
For text watermarking, the hidden signal is often statistical. A model can slightly adjust how it chooses among likely next tokens, which are words or pieces of words, so the final text carries a pattern that a detector can later score. Google’s SynthID Text is one example: it adjusts token probability scores during generation and uses probabilistic detection to decide whether a piece of text appears watermarked.
This is much harder than watermarking an image. Images, audio, and video have lots of tiny details that can be changed without humans noticing. Text is more compressed. Change too many words and the meaning, tone, or quality changes. That is why AI text watermarking usually tries to nudge word choices subtly instead of inserting an obvious hidden code.
The business takeaway is that watermarking is useful, but limited. Light edits may preserve a watermark, but heavy paraphrasing, copy-and-paste cleanup, reformatting, or output from unwatermarked models can weaken or remove the signal. Treat AI text watermarking as one clue in a broader content policy, not as courtroom-level proof that something was or was not written by AI.
If this topic has you thinking about how your organization should label, review, and govern AI-generated content, Coursera’s AI Governance course offers a practical way to understand oversight, accountability, and responsible AI controls.*