\u201cWas this image made with AI?\u201d
\n\u201cWhere did this image come from?\u201d
\n\u201cIs it okay to use this on a client\u2019s commercial website?\u201d<\/p>\n
Recently, we have started hearing these kinds of questions more often when publishing images on websites.<\/p>\n
In the past, when choosing images, the main things we checked were how they looked and how they performed:
\nwhether the image looked clean, displayed quickly, and stayed intact on mobile.<\/p>\n
Those things are still important.<\/p>\n
But as AI-generated images have become more common, the questions around images have started to change.<\/p>\n
It is no longer only about how an image looks.
\nTeams also need to be able to explain:<\/p>\n
Through our own work publishing both photos and AI-generated images on client websites, we have felt how difficult it can be to trace an image\u2019s origin later.<\/p>\n
This article is for website operations managers, directors, and production teams that publish photos and AI-generated images on client sites. We will explain why image provenance is becoming more important, and how to start recording it in a practical way.<\/p>\n
With AI image generation, the ways teams prepare website visuals have expanded significantly.<\/p>\n
You can buy stock photos.
\nYou can shoot original photos.
\nYou can receive assets from a client.
\nYou can generate images with AI.<\/p>\n
Having more options is a major advantage for production and operations teams.<\/p>\n
But the more options there are, the more decisions and checks are required.<\/p>\n
For example:<\/p>\n
Until now, many teams have managed images using file names and folders.<\/p>\n
However, file names and folders do not tell you who created the image, where it came from, whether AI was used, or whether it can be used commercially.<\/p>\n
When a project is handed over to another person six months later, someone may ask:<\/p>\n
\u201cWhere did this photo come from again?\u201d<\/p>\n
And the team may not be able to answer right away.<\/p>\n
That small gap can gradually become a real operational issue.<\/p>\n
One mechanism gaining attention in this context is Content Credentials<\/strong>.<\/p>\n Content Credentials is a way to record the provenance of an image. The information recorded may include:<\/p>\n This information is embedded into the image data with a digital signature. As of 2026, Content Credentials are being embedded by default in images created with tools and services such as Adobe Firefly, OpenAI\u2019s DALL-E 3 and Sora, Bing Image Creator, and photos taken with Google Pixel 10. Google has also started showing provenance information in Search and Chrome.<\/p>\n The important point here is how different this is from a traditional watermark.<\/p>\n A visible watermark burned into an image can be removed by cropping or recreating the image. In other words, we are moving from an era of judging whether an image \u201clooks AI-generated\u201d to an era of checking whether provenance is recorded in the data.<\/p>\n The first step is to make visible which images are currently being used on your websites.<\/p>\n You do not need to start by managing the provenance of every single image perfectly. For example, start with:<\/p>\n The information you record should not be too complicated.<\/p>\n Start with fields such as:<\/p>\n At this stage, the important thing is to identify images where the details are unclear.<\/p>\n If you cannot immediately tell where an image came from, that image needs to be checked. With MONJI+, website operation rules and confirmation histories can be stored in one place. This makes it easier for teams to share rules and notes about image usage, even when the person in charge changes. Next, avoid treating image provenance checks as a one-time audit. Even if Content Credentials are embedded in the image itself, it is risky to rely on them completely.<\/p>\n That is because provenance data can disappear.<\/p>\n For example:<\/p>\n This means that an image is not automatically safe just because it has provenance data. Provenance embedded in an image should be treated as one useful clue.<\/p>\n On top of that, it is important to keep a record outside the image itself.<\/p>\n For example, you can keep a simple note together with the image:<\/p>\n Even notes at this level can make a major difference when someone asks about the image later.<\/p>\n Instead of asking only whether the image itself contains provenance data, it is better to ask:<\/p>\n \u201cCan our team explain how this image is being used?\u201d<\/p>\n That standard is much easier to apply in real operations.<\/p>\n Image provenance management does not have to start with detailed rules.<\/p>\n In fact, if the rules are too complicated from the beginning, they may not last.<\/p>\n A more realistic approach is to first record what kinds of images your team actually handles over several months. For example:<\/p>\n These rules should be placed somewhere that everyone on the production or operations team can access.<\/p>\n New team members and people asked to replace images should be able to check that place first. Using a website operations platform like MONJI+ makes it easier to store asset handling rules and confirmation histories together with other website operations information. The key is not to treat image management as a separate task, but to include it naturally within website operations.<\/p>\n After we started recording image provenance, the biggest change was the speed of decision-making around images.<\/p>\n Previously, when someone asked about an image, we had to trace:<\/p>\n But when the source and usage conditions are recorded together with the image, the starting point for confirmation becomes clear.<\/p>\n We can answer:<\/p>\n \u201cThis image was AI-generated.\u201d Being able to say this makes communication with clients much smoother.<\/p>\n We also came to believe that AI-generated images do not necessarily need to be hidden.<\/p>\n It may not always be necessary to state on the website that an image was AI-generated. That alone reduces anxiety when someone unexpectedly asks later:<\/p>\n \u201cWas this made with AI?\u201d<\/p>\n Rather than hiding it, it is stronger to be able to answer openly.<\/p>\n That, we believe, leads to trust when using AI-generated images.<\/p>\n Image provenance management is not a perfect form of proof.<\/p>\n Even when an image has Content Credentials, metadata can be lost during conversion or compression. So, the existence of provenance data does not mean everything can be trusted unconditionally.<\/p>\n At the same time, it is not realistic to say that every image without provenance data is unusable. The important thing is to treat provenance not as complete proof, but as one piece of information for judgment.<\/p>\n Then, keep records outside the image as well. This two-layer approach will become increasingly important for website operations.<\/p>\n Images and text can now be prepared quickly with AI. Precisely because of that, what matters more is being able to keep track of:<\/p>\n \u201cWhere did this image come from?\u201d MONJI+ is a website operations platform that helps teams centralize website operation rules, asset handling, and confirmation histories.<\/p>\n In an era where content can be created quickly, keeping a record of where things came from quietly supports the trustworthiness of a website.<\/p>\n \u25bcAbout MONJI+ \u25bcStart for free
\nIt is based on the C2PA industry standard and works somewhat like an ingredient label for digital content, showing how an image was created or edited.<\/p>\n\n
\nBy checking it through a dedicated viewer, anyone can review the image\u2019s provenance. If the data has been altered, that can also be detected.<\/p>\n
\nContent Credentials, on the other hand, are designed to carry provenance as part of the image data.<\/p>\nSteps to Start Managing Image Provenance<\/h2>\n
STEP 1: Make the Current Situation Visible<\/h3>\n
\nA more realistic approach is to begin with key images used on client sites or your own site.<\/p>\n\n
\n
\nSimply making unclear assets visible in a list can reduce a lot of operational uncertainty.<\/p>\n
\nLearn more about MONJI+<\/a><\/p>\nSTEP 2: Record and List Image Information as Part of Monthly Operations<\/h3>\n
\nInstead, make them part of your regular monthly website operations.<\/p>\n\n
\nAt the same time, an image is not automatically suspicious just because it does not have provenance data.<\/p>\n\n
STEP 3: Turn Several Months of Records into Operational Rules<\/h3>\n
\nThen, turn repeated patterns into operational rules.<\/p>\n\n
\nThis helps prevent image confirmation work from becoming dependent on a single person\u2019s memory.<\/p>\nWhat Changed in Practice<\/h2>\n
\n
\n\u201cThis photo is a purchased stock asset.\u201d
\n\u201cThis logo was provided by the client.\u201d
\n\u201cThis background was photographed in-house.\u201d<\/p>\n
\nHowever, within the team and between the team and the client, it is better to be clear about which images were made with AI.<\/p>\nNotes and Limitations<\/h2>\n
\nMany images still do not support the system.
\nIt is also possible to intentionally remove only the provenance data.<\/p>\n
\nMany client-provided images, older in-house photos, and long-used assets may not have provenance information embedded in the image file.<\/p>\n
\nRecord the source, usage conditions, whether AI was used, and whether editing is allowed in a place the team can check.<\/p>\nMONJI+: A Website Operations Platform Built from Real Production Work<\/h2>\n
\nThat has already become normal.<\/p>\n
\n\u201cWhat is the basis for publishing it?\u201d<\/p>\n
\nhttps:\/\/monji.tech\/plus\/<\/a><\/p>\n
\nYou can try all features with a 30-day free trial. A free plan is also available.
\nhttps:\/\/monji.tech\/plus\/trial\/<\/a><\/p>\n