# The RUAI Standard (2026 edition): responsible use of AI > Responsible use of AI means using AI to improve work while people stay in charge of it: they decide what it may do, give it reliable sources, check what it produces and answer for the result. The RUAI Standard defines what responsible use of AI means in 2026 for anyone who uses AI at work: one definition and ten principles, each with a key question, recommended practices, prompt examples (before, after and the step a person takes) and cited evidence. Published by Founderz AI, S.L. (Founderz), developed in collaboration with Microsoft. Text licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Cite as: Founderz (2026). What responsible use of AI means in 2026. The RUAI Standard, 2026 edition. Developed by Founderz in collaboration with Microsoft. https://responsibleai.founderz.com/toolkit/home ## The standard - [Toolkit contents](https://responsibleai.founderz.com/toolkit/home): every part of the toolkit on one page: principles, prompt examples, scenarios, case studies, team tools and reference - [The full standard in Markdown](https://responsibleai.founderz.com/standard.md): every principle with its practices, evidence and sources, in one file - [Principle 1: Human oversight of AI](https://responsibleai.founderz.com/toolkit/principles/human-oversight): People decide what AI may do on its own, where a person reviews the work and which decisions remain human. Key question: If this goes wrong, can it be undone, and who would it affect? - [Principle 2: A single source of truth for AI](https://responsibleai.founderz.com/toolkit/principles/single-source-of-truth): AI works from approved, current sources, and every important fact has one owned and dated home. Key question: Where does this fact live, and who keeps it current? - [Principle 3: Confidentiality of information shared with AI](https://responsibleai.founderz.com/toolkit/principles/confidentiality): Information is shared with AI only in approved tools, for an approved purpose and no more than the task needs. Key question: Is this mine to share, in this tool? - [Principle 4: Limited permissions for AI agents](https://responsibleai.founderz.com/toolkit/principles/agent-permissions): AI agents get only the access their task requires, and anything irreversible needs a person’s confirmation. Key question: What’s the worst this agent could do with the access it has? - [Principle 5: Independent judgment and skills](https://responsibleai.founderz.com/toolkit/principles/independent-judgment): People form their own view before relying on AI, and keep practicing the skills their work depends on. Key question: What do I think? - [Principle 6: Challenge and bias awareness](https://responsibleai.founderz.com/toolkit/principles/challenge-and-bias-awareness): AI is used to test ideas as well as develop them, and its pull toward agreement is actively countered. Key question: What’s the strongest case against this? - [Principle 7: Verification proportionate to the stakes](https://responsibleai.founderz.com/toolkit/principles/proportionate-verification): The depth of checking matches the cost of being wrong, and every source relied on is opened and confirmed. Key question: What would it cost if this were wrong? - [Principle 8: Quality of AI-assisted work](https://responsibleai.founderz.com/toolkit/principles/quality-of-ai-assisted-work): AI-assisted work is edited for its reader before it’s shared: the point first, no longer than needed and with the author’s own judgment. Key question: Would I want to receive this? - [Principle 9: Transparency about the use of AI](https://responsibleai.founderz.com/toolkit/principles/transparency): People are told when AI played a part they would reasonably want to know about, and AI is never used to deceive. Key question: Would they feel misled if they knew how this was made? - [Principle 10: Accountability for AI-assisted results](https://responsibleai.founderz.com/toolkit/principles/accountability): The people and organizations that use AI answer for the results, and problems are reported early. Key question: Am I ready to put my name to it? ## Practice - [Prompt examples](https://responsibleai.founderz.com/toolkit/prompt-examples): prompts before and after improvement, with the step a person takes outside the prompt - [Scenario: Your agent is ready to send 40 emails](https://responsibleai.founderz.com/toolkit/workplace-scenarios/agent-ready-to-send-forty-emails): An agent has drafted personalized follow-ups to a webinar list and asks whether to send them all. - [Scenario: The assistant quoted last year’s price](https://responsibleai.founderz.com/toolkit/workplace-scenarios/assistant-quoted-last-years-price): A client needs a price fast. The assistant answers in seconds, from a presentation you suspect is out of date. - [Scenario: You want AI to review a client contract](https://responsibleai.founderz.com/toolkit/workplace-scenarios/ai-review-of-a-client-contract): A long contract, a deadline and a free AI tool one click away. - [Scenario: An email asks your agent to change a supplier’s bank details](https://responsibleai.founderz.com/toolkit/workplace-scenarios/email-asks-agent-to-change-bank-details): Your inbox agent wants to update a payment record because an email told it to. - [Scenario: The finance director asks for an urgent transfer on a video call](https://responsibleai.founderz.com/toolkit/workplace-scenarios/urgent-transfer-on-a-video-call): A familiar face, a familiar voice and an urgent, confidential request. - [Scenario: The report cites a study you can’t find](https://responsibleai.founderz.com/toolkit/workplace-scenarios/report-cites-a-study-you-cannot-find): A polished AI-assisted report, a deadline and one reference that doesn’t seem to exist. - [Scenario: An assistant that agrees with every idea](https://responsibleai.founderz.com/toolkit/workplace-scenarios/assistant-that-agrees-with-every-idea): Three weeks, three versions of a plan and a lot of praise. Something feels too easy. - [Scenario: A six-page AI summary of a 30-minute meeting](https://responsibleai.founderz.com/toolkit/workplace-scenarios/six-page-summary-of-a-short-meeting): A colleague means well. The summary is longer than the meeting was. - [Scenario: You’re asked to rank job applicants with AI](https://responsibleai.founderz.com/toolkit/workplace-scenarios/ranking-job-applicants-with-ai): Two hundred applications, one week and a suggestion from above. - [Scenario: Your manager asks whether you wrote the report yourself](https://responsibleai.founderz.com/toolkit/workplace-scenarios/did-you-write-this-yourself): An honest question about a report you drafted with AI. - [Scenario: You spot a wrong figure in a report you sent last week](https://responsibleai.founderz.com/toolkit/workplace-scenarios/wrong-figure-in-a-sent-report): Page 4 says 21%. The source says 12%. - [Scenario: A junior colleague’s analysis looks perfect, but they can’t explain it](https://responsibleai.founderz.com/toolkit/workplace-scenarios/analysis-nobody-can-explain): Clean charts, confident conclusions and no clear idea of how they got there. - [Scenario: An AI note-taker joins your client call](https://responsibleai.founderz.com/toolkit/workplace-scenarios/ai-note-taker-joins-a-client-call): Someone’s assistant is recording, and nobody asked. - [Self-assessment](https://responsibleai.founderz.com/toolkit/self-assessment): ten questions, one per principle, evaluated in the browser ## Case studies - [Air Canada: liability for a chatbot’s answers](https://responsibleai.founderz.com/toolkit/case-studies/air-canada-chatbot-liability): A tribunal ruled that an airline was responsible for what its website chatbot told a grieving customer. - [Deloitte Australia: a report with invented references](https://responsibleai.founderz.com/toolkit/case-studies/deloitte-report-with-invented-references): A consulting firm repaid part of its fee after a government report was found to contain invented references and a made-up court quote. - [Invented case law in court filings](https://responsibleai.founderz.com/toolkit/case-studies/invented-case-law-in-court-filings): Lawyers were fined for citing cases an AI chatbot invented. A public database now lists more than 2,000 similar court and tribunal decisions. - [Arup: deepfake fraud on a video call](https://responsibleai.founderz.com/toolkit/case-studies/arup-deepfake-video-call-fraud): An employee transferred about HK$200 million after a video call with fake versions of senior colleagues. - [METR: perceived and measured productivity with AI](https://responsibleai.founderz.com/toolkit/case-studies/metr-perceived-and-measured-productivity): Experienced developers felt 20% faster with AI, but their tasks took 19% longer. A 2026 follow-up pointed the other way, and METR calls its results unreliable. - [Workslop: the cost of unedited AI output](https://responsibleai.founderz.com/toolkit/case-studies/workslop): In a survey of 1,150 US employees, 40% had received AI-generated work that looked polished but didn’t do the job. - [UK government: human review of AI-sorted consultation responses](https://responsibleai.founderz.com/toolkit/case-studies/uk-consult-human-review): An AI tool sorted public consultation responses into themes. People checked every one of its decisions, and the results were published. - [UK government: evaluating an AI assistant across departments](https://responsibleai.founderz.com/toolkit/case-studies/uk-government-ai-assistant-evaluations): A large trial found time saved and people who wanted to keep going. A smaller evaluation found that results varied by task. - [Founderz: one source of truth for people and AI](https://responsibleai.founderz.com/toolkit/case-studies/founderz-single-source-of-truth): Every document has an owner, a version and a date. AI assistants read the same knowledge people do, and drafts wait for a person. ## For teams - [Adoption workshop](https://responsibleai.founderz.com/toolkit/team-tools/adoption-workshop): A one-hour session plan for adopting the standard as a team: the definition, the ten key questions, the team charter and three prompt examples to try in the following week. - [Team AI charter](https://responsibleai.founderz.com/toolkit/team-tools/team-ai-charter): One page that records how your team applies the ten principles: approved tools, data classification, checkpoints, sources, agents and who to contact when something goes wrong. - [Human checkpoint map](https://responsibleai.founderz.com/toolkit/team-tools/human-checkpoint-map): For each AI task your team repeats: the level of oversight, who approves what, what they see and how to stop and undo. - [Single source of truth starter kit](https://responsibleai.founderz.com/toolkit/team-tools/single-source-of-truth-starter-kit): Give your team’s key facts one home, one owner and a date, so people and AI find the same right answer. - [Agent permission card](https://responsibleai.founderz.com/toolkit/team-tools/agent-permission-card): One card per shared agent: what it’s for, what it can reach and do, what needs approval, its limits and how to stop it. - [Verification levels](https://responsibleai.founderz.com/toolkit/team-tools/verification-levels): Agree which outputs get a glance, which get spot checks and which get full verification, so everyone checks in proportion. ## Reference - [Glossary](https://responsibleai.founderz.com/toolkit/glossary): definitions of human in the loop, single source of truth, automation bias, sycophancy, prompt injection, workslop and more - [Sources](https://responsibleai.founderz.com/toolkit/sources): every source, with what it establishes and its limits - [About the RUAI Standard](https://responsibleai.founderz.com/toolkit/about): Who publishes the standard, how it’s made and maintained, how to cite it and how to help. - [Where the law fits](https://responsibleai.founderz.com/toolkit/law): The standard describes good practice, and the law sets the floor. Here’s where the law most often meets everyday AI use at work.