The data are responses to a single question fielded on Ipsos’ KnowledgePanel Omnibus, a probability-based online panel that uses address-based sampling to choose respondents. We use two waves of the same question to compute the change. The June question was fielded June 12–14, 2026, with 1,021 respondents. The September question was fielded September 18–20, 2026, with 1,024 respondents. All estimates are weighted to be representative of the adult US population.

Both waves used the same multi-select question: “In the past 12 months, have any of the following happened to you? Please select all that apply.” Respondents could choose any of six incidents, “None of these”, or “Not sure”:

• Someone got into one of your online accounts without your permission
• Your personal information was leaked or stolen in a hack of a company, app, or website
• One of your devices was infected with a virus, malware, or spyware
• You were locked out of your files or device and asked to pay to get them back
• A service you depend on stopped working because it was attacked. Examples: bank app, medical portal, government website
• You received a spam message, call, or video that was unusually convincing or seemed personally aimed at you

We calculate the share reporting “Any of these incidents” as the share selecting at least one of the six experiences. Each person is counted only once in this combined measure, even if they selected several experiences. The individual incident shares can therefore sum to more than the combined share.

We exclude “Not sure” and “None of these” responses from the graph to make interpretation easier. These responses account for approximately 8% in June and 9% in September for “Not sure”, and 45% in both months for “None of these”. Respondents who selected either of these options could not select any other options.

All estimates are weighted to represent US adults aged 18 and over. We report 90% Wilson confidence intervals for each estimate computed on Kish’s effective sample size to account for unequal weighting.

We repeat the comparison for respondents who report frequent AI use (n = 233 in June, 215 in September). Frequent AI use comes from an Ipsos KnowledgePanel profile question, “How often do you use or interact with artificial intelligence (AI) tools (chatbots, recommendation systems, etc.)?”, and counts respondents who answered “Daily” or “Weekly”.

Each wave polls a random sample of respondents rather than tracking the same individuals, so the results reflect population-level changes rather than changes within the same people, although 40 respondents participated in both waves by chance.

We focus on the overall change in incidents for this analysis. However, when looking at individual incident types, smaller changes may go undetected due to lack of statistical power. The analysis also does not identify incidents caused by AI, and is not able to estimate the counterfactual effect of particular model releases. Both waves ask about incidents in the preceding 12 months, so their recall windows overlap.

As with all polls, non-respondents may differ from respondents in ways that the weighting does not correct for. Cyber incidents are self-reported and may be affected by recall error or misclassification.