{
  "standard": "The Temper Standard",
  "version": "0.1",
  "published": "2026-08-03",
  "lastVerified": "2026-08-03",
  "license": "CC BY 4.0",
  "licenseUrl": "https://creativecommons.org/licenses/by/4.0/",
  "maintainer": "Temper AI LLC",
  "contact": "m@temper.ai",
  "summary": "A canonical, openly licensed question set for assessing an AI system or AI vendor. Answer it once, publish the answers as a trust profile, and reuse it in every deal instead of completing a different 300-question spreadsheet for each buyer.",
  "howToUse": "Buyers: adopt this in place of writing your own AI questionnaire, and ask vendors for the answers with evidence dates. Vendors: answer it once and publish. Every question maps to at least one recognised framework, so the answers do double duty as evidence for NIST AI RMF, ISO/IEC 42001 and EU AI Act work you are doing anyway.",
  "limits": "v0.1 is a starting point published for use and criticism, not a finished standard. It has not yet been tested against a real enterprise questionnaire end to end, the section weighting is a judgement call, and no question here is a substitute for legal advice. Mappings are to framework clauses and articles, not an assertion that answering a question satisfies that clause.",
  "questionCount": 55,
  "frameworks": [
    {
      "id": "nist-ai-rmf",
      "name": "NIST AI RMF 1.0",
      "label": "NIST",
      "sourceUrl": "https://www.nist.gov/itl/ai-risk-management-framework",
      "note": "Subcategory identifiers, validated against the published Playbook."
    },
    {
      "id": "iso-42001",
      "name": "ISO/IEC 42001:2023",
      "label": "ISO 42001",
      "sourceUrl": "https://www.iso.org/standard/42001",
      "note": "Management-system clauses 4–10 and Annex A control objectives A.2–A.10 (38 controls)."
    },
    {
      "id": "eu-ai-act",
      "name": "EU AI Act (Reg. (EU) 2024/1689)",
      "label": "EU AI Act",
      "sourceUrl": "https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng",
      "note": "Article numbers. Most high-risk articles apply from Dec 2, 2027; Art. 50 applies now."
    },
    {
      "id": "omb-m25-22",
      "name": "OMB M-25-22",
      "label": "M-25-22",
      "sourceUrl": "https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-22-Driving-Efficient-Acquisition-of-Artificial-Intelligence-in-Government.pdf",
      "note": "Referenced by requirement topic rather than clause number, so nothing is cited that we have not read."
    },
    {
      "id": "csa-aicm",
      "name": "CSA AI Controls Matrix v1.0",
      "label": "AICM",
      "sourceUrl": "https://cloudsecurityalliance.org/research/working-groups/ai-controls-matrix",
      "note": "Two-letter control domain codes."
    }
  ],
  "sections": [
    {
      "id": "GOV",
      "name": "Governance and accountability",
      "purpose": "Who owns AI risk here, and what happens when something goes wrong."
    },
    {
      "id": "INV",
      "name": "What the system is",
      "purpose": "A buyer cannot assess what they cannot describe. This is the transparency-card layer."
    },
    {
      "id": "DATA",
      "name": "Data and rights",
      "purpose": "Where the training and inference data came from, and what you are allowed to do with it."
    },
    {
      "id": "MDL",
      "name": "Development and testing",
      "purpose": "Evidence that the thing was evaluated before it reached a customer."
    },
    {
      "id": "FAIR",
      "name": "Fairness and impact",
      "purpose": "The questions that decide employment, credit and healthcare litigation."
    },
    {
      "id": "HUM",
      "name": "Human oversight",
      "purpose": "Whether a person can see, understand and stop the system."
    },
    {
      "id": "SEC",
      "name": "Security",
      "purpose": "AI-specific security, not a re-run of a SOC 2 questionnaire."
    },
    {
      "id": "SUP",
      "name": "Third parties and supply chain",
      "purpose": "Almost every AI vendor is itself an AI buyer. This is where that is disclosed."
    },
    {
      "id": "MON",
      "name": "Monitoring and incidents",
      "purpose": "What happens after deployment, which is where most AI failure actually lives."
    },
    {
      "id": "LEG",
      "name": "Legal and regulatory standing",
      "purpose": "Which laws reach this system and what the vendor claims under each."
    },
    {
      "id": "PROC",
      "name": "Procurement, contracts and exit",
      "purpose": "The terms that decide whether a buyer is locked in — the heart of M-25-22."
    }
  ],
  "questions": [
    {
      "id": "TS-GOV-01",
      "section": "GOV",
      "question": "Who is accountable for AI risk in your organisation, by name and role?",
      "why": "A named owner is the difference between a governance programme and a slide. Buyers escalate to a person, not a department.",
      "answerType": "text",
      "evidenceExpected": "Org chart entry or role description naming the accountable owner",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 2.1"
        ],
        "iso-42001": [
          "5.3",
          "A.3"
        ],
        "eu-ai-act": [
          "Art. 17"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 2.1": "Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization."
      }
    },
    {
      "id": "TS-GOV-02",
      "section": "GOV",
      "question": "Do you have a written AI policy that applies to this system?",
      "why": "Establishes that the controls below are organisational commitments rather than one team's habits.",
      "answerType": "boolean",
      "evidenceExpected": "The AI policy document, with approval date",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 1.2"
        ],
        "iso-42001": [
          "5.2",
          "A.2"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 1.2": "The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures."
      }
    },
    {
      "id": "TS-GOV-03",
      "section": "GOV",
      "question": "How is AI risk tolerance defined, and who approved it?",
      "why": "Without a stated tolerance, every risk decision is ad hoc and unreviewable after an incident.",
      "answerType": "text",
      "evidenceExpected": "Risk appetite statement with approval record",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 1.3"
        ],
        "iso-42001": [
          "6.1"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 1.3": "Processes and procedures are in place to determine the needed level of risk management activities based on the organization's risk tolerance."
      }
    },
    {
      "id": "TS-GOV-04",
      "section": "GOV",
      "question": "Do staff who build or operate this system receive AI-specific training?",
      "why": "The EU AI Act makes AI literacy a legal duty for providers and deployers, in force since Feb 2025.",
      "answerType": "boolean",
      "evidenceExpected": "Training records or curriculum",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 2.2"
        ],
        "iso-42001": [
          "7.2",
          "A.4"
        ],
        "eu-ai-act": [
          "Art. 4"
        ],
        "csa-aicm": [
          "HRS"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 2.2": "The organization’s personnel and partners receive AI risk management training to enable them to perform their duties and responsibilities consistent with related policies, procedures, and agreements."
      }
    },
    {
      "id": "TS-GOV-05",
      "section": "GOV",
      "question": "Is there a documented process for decommissioning an AI system?",
      "why": "Rarely asked, frequently painful. Covers model deprecation, data deletion and customer notice.",
      "answerType": "boolean",
      "evidenceExpected": "Decommissioning procedure",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 1.7"
        ],
        "iso-42001": [
          "A.6"
        ],
        "csa-aicm": [
          "CCC"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 1.7": "Processes and procedures are in place for decommissioning and phasing out of AI systems safely and in a manner that does not increase risks or decrease the organization’s trustworthiness."
      }
    },
    {
      "id": "TS-INV-01",
      "section": "INV",
      "question": "What does this system do, in one paragraph a non-specialist can follow?",
      "why": "If the vendor cannot explain it plainly, the buyer cannot govern it.",
      "answerType": "text",
      "evidenceExpected": "System or model card",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 1.1"
        ],
        "iso-42001": [
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 11",
          "Art. 13"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MAP 1.1": "Intended purpose, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes; uses and risks across the development or product AI lifecycle; TEVV and system metrics."
      }
    },
    {
      "id": "TS-INV-02",
      "section": "INV",
      "question": "What is the intended use, and what uses are explicitly out of scope?",
      "why": "Out-of-scope use is where liability concentrates. A stated prohibition is a real control.",
      "answerType": "text",
      "evidenceExpected": "Transparency card: intended and prohibited use",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 1.1",
          "MAP 3.4"
        ],
        "iso-42001": [
          "A.9"
        ],
        "eu-ai-act": [
          "Art. 13"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MAP 1.1": "Intended purpose, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes; uses and risks across the development or product AI lifecycle; TEVV and system metrics.",
        "MAP 3.4": "Processes for operator and practitioner proficiency with AI system performance and trustworthiness – and relevant technical standards and certifications – are defined, assessed and documented."
      }
    },
    {
      "id": "TS-INV-03",
      "section": "INV",
      "question": "What model or models does it use, including version and provider?",
      "why": "Determines whose incident becomes your incident. Also the first question in any supply-chain review.",
      "answerType": "text",
      "evidenceExpected": "Model inventory with versions",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 4.1"
        ],
        "iso-42001": [
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 53"
        ],
        "csa-aicm": [
          "MDS",
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MAP 4.1": "Approaches for mapping AI technology and legal risks of its components – including the use of third-party data or software – are in place, followed, and documented, as are risks of infringement of a third-party’s intellectual property or other rights."
      }
    },
    {
      "id": "TS-INV-04",
      "section": "INV",
      "question": "Do you maintain an inventory of all AI systems, with version control?",
      "why": "The first thing any auditor asks for, and the thing most vendors cannot produce.",
      "answerType": "boolean",
      "evidenceExpected": "AI system inventory",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 1.6"
        ],
        "iso-42001": [
          "A.6"
        ],
        "csa-aicm": [
          "AA"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 1.6": "Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities."
      }
    },
    {
      "id": "TS-INV-05",
      "section": "INV",
      "question": "Do you publish a transparency card, model card or system card for this system?",
      "why": "Turns every answer here into something the buyer can re-read without another meeting.",
      "answerType": "boolean",
      "evidenceExpected": "The published card",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 5.1"
        ],
        "iso-42001": [
          "A.8"
        ],
        "eu-ai-act": [
          "Art. 11",
          "Art. 13"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MAP 5.1": "Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documented."
      }
    },
    {
      "id": "TS-DATA-01",
      "section": "DATA",
      "question": "Do you train models on customer data?",
      "why": "The single most-asked question in AI procurement, and the one M-25-22 makes non-negotiable for federal buyers.",
      "answerType": "boolean",
      "evidenceExpected": "Contractual term or written policy",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 4.1"
        ],
        "iso-42001": [
          "A.7"
        ],
        "eu-ai-act": [
          "Art. 10"
        ],
        "omb-m25-22": [
          "No training on non-public government data without consent"
        ],
        "csa-aicm": [
          "DSP"
        ]
      },
      "nistSubcategoryText": {
        "MAP 4.1": "Approaches for mapping AI technology and legal risks of its components – including the use of third-party data or software – are in place, followed, and documented, as are risks of infringement of a third-party’s intellectual property or other rights."
      }
    },
    {
      "id": "TS-DATA-02",
      "section": "DATA",
      "question": "If you do, can a customer opt out, and is opt-out the default?",
      "why": "Opt-out that exists but is off by default is a dark pattern, and buyers increasingly treat it as one.",
      "answerType": "enum",
      "options": [
        "No training on customer data at all",
        "Opt-out available, off by default",
        "Opt-out available, on by default",
        "No opt-out"
      ],
      "evidenceExpected": "Contract clause or product setting",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 4.1"
        ],
        "iso-42001": [
          "A.7"
        ],
        "omb-m25-22": [
          "Consent for data use"
        ],
        "csa-aicm": [
          "DSP"
        ]
      },
      "nistSubcategoryText": {
        "MAP 4.1": "Approaches for mapping AI technology and legal risks of its components – including the use of third-party data or software – are in place, followed, and documented, as are risks of infringement of a third-party’s intellectual property or other rights."
      }
    },
    {
      "id": "TS-DATA-03",
      "section": "DATA",
      "question": "What are the sources of your training data, and what licence covers each?",
      "why": "Copyright exposure and the EU AI Act's training-data summary duty both land here.",
      "answerType": "text",
      "evidenceExpected": "Training-data documentation",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 2.3"
        ],
        "iso-42001": [
          "A.7"
        ],
        "eu-ai-act": [
          "Art. 10",
          "Art. 53"
        ],
        "csa-aicm": [
          "DSP",
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MAP 2.3": "Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and construct validation."
      }
    },
    {
      "id": "TS-DATA-04",
      "section": "DATA",
      "question": "Does the system process personal data, and under what legal basis?",
      "why": "Determines whether GDPR, CCPA and sectoral privacy law are in play at all.",
      "answerType": "text",
      "evidenceExpected": "Privacy notice and record of processing",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 4.1"
        ],
        "iso-42001": [
          "A.7"
        ],
        "eu-ai-act": [
          "Art. 10"
        ],
        "csa-aicm": [
          "DSP"
        ]
      },
      "nistSubcategoryText": {
        "MAP 4.1": "Approaches for mapping AI technology and legal risks of its components – including the use of third-party data or software – are in place, followed, and documented, as are risks of infringement of a third-party’s intellectual property or other rights."
      }
    },
    {
      "id": "TS-DATA-05",
      "section": "DATA",
      "question": "Where is data stored and processed, and by whom?",
      "why": "Residency and subprocessor questions that block deals in regulated sectors and in the EU.",
      "answerType": "text",
      "evidenceExpected": "Data-flow diagram and subprocessor list",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 4.1"
        ],
        "iso-42001": [
          "A.7",
          "A.10"
        ],
        "csa-aicm": [
          "DCS",
          "DSP"
        ]
      },
      "nistSubcategoryText": {
        "MAP 4.1": "Approaches for mapping AI technology and legal risks of its components – including the use of third-party data or software – are in place, followed, and documented, as are risks of infringement of a third-party’s intellectual property or other rights."
      }
    },
    {
      "id": "TS-DATA-06",
      "section": "DATA",
      "question": "What is your data retention and deletion schedule for AI inputs and outputs?",
      "why": "Prompts and outputs are often retained far longer than customers assume.",
      "answerType": "text",
      "evidenceExpected": "Retention schedule",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 4.1"
        ],
        "iso-42001": [
          "A.7"
        ],
        "csa-aicm": [
          "DSP"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 4.1": "Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management."
      }
    },
    {
      "id": "TS-MDL-01",
      "section": "MDL",
      "question": "How was this system evaluated before release, and against what benchmarks?",
      "why": "M-25-22 requires pre-award performance testing. Most vendors have something; few have it written down.",
      "answerType": "text",
      "evidenceExpected": "Evaluation report with date and method",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.3"
        ],
        "iso-42001": [
          "8.3",
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 15"
        ],
        "omb-m25-22": [
          "Pre-award performance testing"
        ],
        "csa-aicm": [
          "MDS"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.3": "AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s). Measures are documented."
      }
    },
    {
      "id": "TS-MDL-02",
      "section": "MDL",
      "question": "What are the system's known limitations and failure modes?",
      "why": "A vendor who claims none has not looked. This answer is the strongest honesty signal in the set.",
      "answerType": "text",
      "evidenceExpected": "Transparency card: known limitations",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.6",
          "MAP 3.5"
        ],
        "iso-42001": [
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 13"
        ],
        "csa-aicm": [
          "MDS"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.6": "AI system is evaluated regularly for safety risks – as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and can fail safely, particularly if made to operate beyond its knowledge limits. Safety metrics implicate system reliability and robustness, real-time monitoring, and response times for AI system failures.",
        "MAP 3.5": "Processes for human oversight are defined, assessed, and documented in accordance with organizational policies from GOVERN function."
      }
    },
    {
      "id": "TS-MDL-03",
      "section": "MDL",
      "question": "How do you measure accuracy, and what is current measured performance?",
      "why": "Turns \"highly accurate\" into a number a buyer can hold you to — the FTC's AI-washing line.",
      "answerType": "text",
      "evidenceExpected": "Performance metrics with measurement date",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.3"
        ],
        "iso-42001": [
          "9.1"
        ],
        "eu-ai-act": [
          "Art. 15"
        ],
        "csa-aicm": [
          "MDS"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.3": "AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s). Measures are documented."
      }
    },
    {
      "id": "TS-MDL-04",
      "section": "MDL",
      "question": "Is the evaluation repeated on a defined cadence, or only at release?",
      "why": "Model drift makes a one-off evaluation a historical document.",
      "answerType": "enum",
      "options": [
        "Continuous",
        "Quarterly or more often",
        "Annually",
        "At release only",
        "No formal evaluation"
      ],
      "evidenceExpected": "Testing cadence and most recent run",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 4.1"
        ],
        "iso-42001": [
          "9.1"
        ],
        "eu-ai-act": [
          "Art. 72"
        ],
        "csa-aicm": [
          "MDS",
          "AA"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 4.1": "Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and other end users. Approaches are documented."
      }
    },
    {
      "id": "TS-MDL-05",
      "section": "MDL",
      "question": "Do you have a change-management process for model updates?",
      "why": "Silent model swaps are the most common cause of a buyer's validated system quietly becoming unvalidated.",
      "answerType": "boolean",
      "evidenceExpected": "Change-control procedure and customer-notice policy",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 4.1"
        ],
        "iso-42001": [
          "8.1",
          "A.6"
        ],
        "csa-aicm": [
          "CCC"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 4.1": "Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management."
      }
    },
    {
      "id": "TS-FAIR-01",
      "section": "FAIR",
      "question": "Has this system been tested for disparate impact across protected groups?",
      "why": "NYC LL 144 mandates it for hiring tools. Illinois, Colorado and Title VII litigation make it defensibility elsewhere.",
      "answerType": "boolean",
      "evidenceExpected": "Bias audit or disparate-impact analysis with date",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.11"
        ],
        "iso-42001": [
          "A.5"
        ],
        "eu-ai-act": [
          "Art. 10",
          "Art. 15"
        ],
        "csa-aicm": [
          "MDS"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.11": "Fairness and bias – as identified in the MAP function – are evaluated and results are documented."
      }
    },
    {
      "id": "TS-FAIR-02",
      "section": "FAIR",
      "question": "Who performed that testing — an internal team or an independent party?",
      "why": "LL 144 requires independence. Buyers increasingly ask even where the law does not.",
      "answerType": "enum",
      "options": [
        "Independent third party",
        "Internal team, independent of the builders",
        "The team that built it",
        "Not tested"
      ],
      "evidenceExpected": "Auditor identity and independence statement",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.11",
          "MEASURE 3.2"
        ],
        "iso-42001": [
          "9.2"
        ],
        "csa-aicm": [
          "AA"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.11": "Fairness and bias – as identified in the MAP function – are evaluated and results are documented.",
        "MEASURE 3.2": "Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement techniques or where metrics are not yet available."
      }
    },
    {
      "id": "TS-FAIR-03",
      "section": "FAIR",
      "question": "Have you conducted an impact assessment for this system?",
      "why": "ISO 42005 gives the method; the EU AI Act requires a fundamental-rights assessment for some deployers.",
      "answerType": "boolean",
      "evidenceExpected": "Impact assessment document",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 5.1"
        ],
        "iso-42001": [
          "A.5"
        ],
        "eu-ai-act": [
          "Art. 27"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "MAP 5.1": "Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed the AI system, or other data are identified and documented."
      }
    },
    {
      "id": "TS-FAIR-04",
      "section": "FAIR",
      "question": "Does the system make or materially inform decisions about people?",
      "why": "The trigger question. If yes, most of the world's AI law applies; if no, very little does.",
      "answerType": "boolean",
      "evidenceExpected": "Use-case description",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 1.1"
        ],
        "iso-42001": [
          "A.5"
        ],
        "eu-ai-act": [
          "Art. 6",
          "Annex III"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "MAP 1.1": "Intended purpose, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes; uses and risks across the development or product AI lifecycle; TEVV and system metrics."
      }
    },
    {
      "id": "TS-FAIR-05",
      "section": "FAIR",
      "question": "If so, in which domains — employment, credit, housing, education, healthcare, insurance or public services?",
      "why": "These are the high-risk categories under Annex III and the sectors with the most active US enforcement.",
      "answerType": "text",
      "evidenceExpected": "Deployment context documentation",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 1.1"
        ],
        "eu-ai-act": [
          "Annex III"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "MAP 1.1": "Intended purpose, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes; uses and risks across the development or product AI lifecycle; TEVV and system metrics."
      }
    },
    {
      "id": "TS-HUM-01",
      "section": "HUM",
      "question": "Can a human review, override or reverse an output before it takes effect?",
      "why": "Meaningful human review is the remedy Colorado SB 26-189, GDPR Art. 22 and the EU AI Act all converge on.",
      "answerType": "boolean",
      "evidenceExpected": "Human-oversight design documentation",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 2.3"
        ],
        "iso-42001": [
          "A.9"
        ],
        "eu-ai-act": [
          "Art. 14"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 2.3": "Procedures are followed to respond to and recover from a previously unknown risk when it is identified."
      }
    },
    {
      "id": "TS-HUM-02",
      "section": "HUM",
      "question": "What training or context does the human reviewer receive?",
      "why": "Guards against rubber-stamp oversight, which regulators increasingly treat as no oversight.",
      "answerType": "text",
      "evidenceExpected": "Reviewer guidance or training material",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 2.3"
        ],
        "iso-42001": [
          "7.2",
          "A.9"
        ],
        "eu-ai-act": [
          "Art. 14"
        ],
        "csa-aicm": [
          "HRS"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 2.3": "Procedures are followed to respond to and recover from a previously unknown risk when it is identified."
      }
    },
    {
      "id": "TS-HUM-03",
      "section": "HUM",
      "question": "Are affected people told an AI system was involved?",
      "why": "Disclosure duties are live now under EU AI Act Art. 50, Illinois HB 3773, Utah SB 149 and NYC LL 144.",
      "answerType": "boolean",
      "evidenceExpected": "Notice text and where it appears",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 5.2"
        ],
        "iso-42001": [
          "A.8"
        ],
        "eu-ai-act": [
          "Art. 50"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MAP 5.2": "Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented."
      }
    },
    {
      "id": "TS-HUM-04",
      "section": "HUM",
      "question": "Is AI-generated or manipulated content marked as such, including machine-readably?",
      "why": "EU AI Act Art. 50(2) and California SB 942 both require provenance marking, not just a visible label.",
      "answerType": "boolean",
      "evidenceExpected": "Provenance and watermarking approach",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.7"
        ],
        "eu-ai-act": [
          "Art. 50"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.7": "AI system security and resilience – as identified in the MAP function – are evaluated and documented."
      }
    },
    {
      "id": "TS-HUM-05",
      "section": "HUM",
      "question": "Is there a route for an affected person to contest an outcome?",
      "why": "Appeal rights are the practical remedy regulators check for after a complaint.",
      "answerType": "boolean",
      "evidenceExpected": "Appeals or redress procedure",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 4.3"
        ],
        "iso-42001": [
          "A.8"
        ],
        "eu-ai-act": [
          "Art. 86"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 4.3": "Incidents and errors are communicated to relevant AI actors including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented."
      }
    },
    {
      "id": "TS-SEC-01",
      "section": "SEC",
      "question": "Has the system been tested against adversarial attacks such as prompt injection?",
      "why": "The OWASP LLM Top 10 and MITRE ATLAS define the scope; a generic pen test does not cover it.",
      "answerType": "boolean",
      "evidenceExpected": "AI red-team or adversarial test report",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.7"
        ],
        "iso-42001": [
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 15"
        ],
        "csa-aicm": [
          "MDS",
          "TVM"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.7": "AI system security and resilience – as identified in the MAP function – are evaluated and documented."
      }
    },
    {
      "id": "TS-SEC-02",
      "section": "SEC",
      "question": "What controls prevent the model from disclosing training data or system prompts?",
      "why": "Data leakage through the model is the AI-specific breach path a standard security questionnaire misses.",
      "answerType": "text",
      "evidenceExpected": "Output filtering and testing evidence",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.7"
        ],
        "iso-42001": [
          "A.6"
        ],
        "csa-aicm": [
          "MDS",
          "DSP"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.7": "AI system security and resilience – as identified in the MAP function – are evaluated and documented."
      }
    },
    {
      "id": "TS-SEC-03",
      "section": "SEC",
      "question": "Is multi-factor authentication enforced on all accounts with access to the system or its data?",
      "why": "Baseline. Asked because the answer is \"not quite\" far more often than vendors expect.",
      "answerType": "boolean",
      "evidenceExpected": "Access-control policy and enforcement evidence",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 2.2"
        ],
        "iso-42001": [
          "A.4"
        ],
        "csa-aicm": [
          "IAM"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 2.2": "Mechanisms are in place and applied to sustain the value of deployed AI systems."
      }
    },
    {
      "id": "TS-SEC-04",
      "section": "SEC",
      "question": "Are AI inputs and outputs logged, and are those logs tamper-resistant?",
      "why": "EU AI Act Art. 12 requires logging for high-risk systems; without it no incident can be reconstructed.",
      "answerType": "boolean",
      "evidenceExpected": "Logging design and retention period",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.8"
        ],
        "iso-42001": [
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 12"
        ],
        "csa-aicm": [
          "LOG"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.8": "Risks associated with transparency and accountability – as identified in the MAP function – are examined and documented."
      }
    },
    {
      "id": "TS-SEC-05",
      "section": "SEC",
      "question": "How is the system protected against data or model poisoning?",
      "why": "Poisoning is the supply-chain attack unique to AI, and the one with no clean detection story.",
      "answerType": "text",
      "evidenceExpected": "Data provenance and integrity controls",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 2.7"
        ],
        "iso-42001": [
          "A.7"
        ],
        "csa-aicm": [
          "MDS",
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 2.7": "AI system security and resilience – as identified in the MAP function – are evaluated and documented."
      }
    },
    {
      "id": "TS-SUP-01",
      "section": "SUP",
      "question": "Which third-party AI providers, APIs or models do you depend on?",
      "why": "Nearly every AI vendor is also an AI buyer. Undisclosed dependency is undisclosed risk.",
      "answerType": "text",
      "evidenceExpected": "Subprocessor and model-dependency list",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 4.1"
        ],
        "iso-42001": [
          "A.10"
        ],
        "eu-ai-act": [
          "Art. 25"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MAP 4.1": "Approaches for mapping AI technology and legal risks of its components – including the use of third-party data or software – are in place, followed, and documented, as are risks of infringement of a third-party’s intellectual property or other rights."
      }
    },
    {
      "id": "TS-SUP-02",
      "section": "SUP",
      "question": "What due diligence did you perform on those providers?",
      "why": "Passing the buck upstream only works if someone checked upstream.",
      "answerType": "text",
      "evidenceExpected": "Vendor assessment records",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 6.1"
        ],
        "iso-42001": [
          "A.10"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 6.1": "Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third party’s intellectual property or other rights."
      }
    },
    {
      "id": "TS-SUP-03",
      "section": "SUP",
      "question": "Do your upstream contracts let you meet the commitments you make to customers?",
      "why": "Where back-to-back terms fail, the vendor in the middle carries the gap personally.",
      "answerType": "boolean",
      "evidenceExpected": "Flow-down contract terms",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 6.2"
        ],
        "iso-42001": [
          "A.10"
        ],
        "omb-m25-22": [
          "Flow-down of data and IP terms"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 6.2": "Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk."
      }
    },
    {
      "id": "TS-SUP-04",
      "section": "SUP",
      "question": "How would you be notified if an upstream provider changed or retired a model?",
      "why": "The upstream deprecation notice a vendor never received is a recurring root cause.",
      "answerType": "text",
      "evidenceExpected": "Provider notification terms",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 4.1"
        ],
        "iso-42001": [
          "A.10"
        ],
        "csa-aicm": [
          "CCC",
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 4.1": "Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management."
      }
    },
    {
      "id": "TS-MON-01",
      "section": "MON",
      "question": "How is the system monitored in production, and by whom?",
      "why": "Post-deployment is where AI actually fails, and where most governance programmes stop.",
      "answerType": "text",
      "evidenceExpected": "Monitoring plan and ownership",
      "mappings": {
        "nist-ai-rmf": [
          "MEASURE 4.1",
          "MANAGE 4.1"
        ],
        "iso-42001": [
          "9.1",
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 72"
        ],
        "omb-m25-22": [
          "Continuous monitoring"
        ],
        "csa-aicm": [
          "LOG"
        ]
      },
      "nistSubcategoryText": {
        "MEASURE 4.1": "Measurement approaches for identifying AI risks are connected to deployment context(s) and informed through consultation with domain experts and other end users. Approaches are documented.",
        "MANAGE 4.1": "Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management."
      }
    },
    {
      "id": "TS-MON-02",
      "section": "MON",
      "question": "Do you have a documented AI incident response process?",
      "why": "AI incidents are not security incidents and rarely fit an existing runbook.",
      "answerType": "boolean",
      "evidenceExpected": "Incident response procedure covering AI failures",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 4.3"
        ],
        "iso-42001": [
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 73"
        ],
        "csa-aicm": [
          "SEF"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 4.3": "Incidents and errors are communicated to relevant AI actors including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented."
      }
    },
    {
      "id": "TS-MON-03",
      "section": "MON",
      "question": "Within what time would you notify a customer of a serious AI incident?",
      "why": "A number, not an intention. The EU AI Act sets reporting deadlines for serious incidents.",
      "answerType": "text",
      "evidenceExpected": "Notification commitment in contract or policy",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 4.3"
        ],
        "iso-42001": [
          "A.6"
        ],
        "eu-ai-act": [
          "Art. 73"
        ],
        "csa-aicm": [
          "SEF"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 4.3": "Incidents and errors are communicated to relevant AI actors including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented."
      }
    },
    {
      "id": "TS-MON-04",
      "section": "MON",
      "question": "Have you had an AI-related incident in the last 24 months, and what changed after it?",
      "why": "The answer matters less than whether the vendor will answer at all.",
      "answerType": "text",
      "evidenceExpected": "Incident register or written statement",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 4.3"
        ],
        "iso-42001": [
          "10.1"
        ],
        "csa-aicm": [
          "SEF"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 4.3": "Incidents and errors are communicated to relevant AI actors including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented."
      }
    },
    {
      "id": "TS-MON-05",
      "section": "MON",
      "question": "How do users or affected people report a problem with the system?",
      "why": "A feedback route is the cheapest early-warning system that exists.",
      "answerType": "text",
      "evidenceExpected": "Feedback or reporting channel",
      "mappings": {
        "nist-ai-rmf": [
          "MANAGE 4.1"
        ],
        "iso-42001": [
          "A.8"
        ],
        "csa-aicm": [
          "SEF"
        ]
      },
      "nistSubcategoryText": {
        "MANAGE 4.1": "Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management."
      }
    },
    {
      "id": "TS-LEG-01",
      "section": "LEG",
      "question": "Which AI-specific laws apply to this system, and what is your standing under each?",
      "why": "The core of a trust profile: not \"are you compliant\" but \"with what, and how do you know\".",
      "answerType": "text",
      "evidenceExpected": "Regulatory applicability analysis",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 1.1"
        ],
        "iso-42001": [
          "4.2",
          "6.1"
        ],
        "eu-ai-act": [
          "Art. 6"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 1.1": "Legal and regulatory requirements involving AI are understood, managed, and documented."
      }
    },
    {
      "id": "TS-LEG-02",
      "section": "LEG",
      "question": "Do you claim alignment with NIST AI RMF, and can you show the mapping?",
      "why": "Documented alignment is a statutory affirmative defence under Texas TRAIGA — but only if the mapping exists.",
      "answerType": "boolean",
      "evidenceExpected": "Completed and approved NIST AI RMF mapping",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 1.2"
        ],
        "iso-42001": [
          "4.4"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 1.2": "The characteristics of trustworthy AI are integrated into organizational policies, processes, and procedures."
      }
    },
    {
      "id": "TS-LEG-03",
      "section": "LEG",
      "question": "Do you hold any AI-related certification from an accredited body?",
      "why": "Separates certified from aligned. Only an accredited body's certificate counts.",
      "answerType": "enum",
      "options": [
        "ISO/IEC 42001 certified",
        "Other AI certification",
        "Aligned but not certified",
        "None"
      ],
      "evidenceExpected": "Certificate, certification body and expiry date",
      "mappings": {
        "iso-42001": [
          "9.2"
        ],
        "csa-aicm": [
          "AA"
        ]
      },
      "nistSubcategoryText": {}
    },
    {
      "id": "TS-LEG-04",
      "section": "LEG",
      "question": "Does the system fall within EU AI Act Annex III, and have you assessed that?",
      "why": "Annex III classification drives the December 2027 obligations. Assess now, not then.",
      "answerType": "boolean",
      "evidenceExpected": "Risk classification analysis",
      "mappings": {
        "nist-ai-rmf": [
          "MAP 1.1"
        ],
        "eu-ai-act": [
          "Art. 6",
          "Annex III"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "MAP 1.1": "Intended purpose, potentially beneficial uses, context-specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: specific set or types of users along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes; uses and risks across the development or product AI lifecycle; TEVV and system metrics."
      }
    },
    {
      "id": "TS-LEG-05",
      "section": "LEG",
      "question": "Who is your point of contact for regulatory or compliance questions?",
      "why": "Named contact, because \"compliance@\" is where procurement questions go to die.",
      "answerType": "text",
      "evidenceExpected": "Named contact and role",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 2.1"
        ],
        "iso-42001": [
          "5.3"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 2.1": "Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization."
      }
    },
    {
      "id": "TS-PROC-01",
      "section": "PROC",
      "question": "Who owns the intellectual property in outputs the customer generates?",
      "why": "A live commercial question with no settled default, and the one buyers discover too late.",
      "answerType": "text",
      "evidenceExpected": "Contract IP clause",
      "mappings": {
        "iso-42001": [
          "A.10"
        ],
        "omb-m25-22": [
          "IP and data rights"
        ],
        "csa-aicm": [
          "STA"
        ]
      },
      "nistSubcategoryText": {}
    },
    {
      "id": "TS-PROC-02",
      "section": "PROC",
      "question": "Can a customer export their data and outputs in a usable format on exit?",
      "why": "M-25-22 makes portability an explicit federal procurement requirement, not a nice-to-have.",
      "answerType": "boolean",
      "evidenceExpected": "Export capability and format documentation",
      "mappings": {
        "iso-42001": [
          "A.10"
        ],
        "omb-m25-22": [
          "Data portability and avoiding vendor lock-in"
        ],
        "csa-aicm": [
          "IPY"
        ]
      },
      "nistSubcategoryText": {}
    },
    {
      "id": "TS-PROC-03",
      "section": "PROC",
      "question": "What happens to customer data when the contract ends?",
      "why": "Deletion timelines and certification, which almost no vendor states without being asked.",
      "answerType": "text",
      "evidenceExpected": "Termination and deletion terms",
      "mappings": {
        "iso-42001": [
          "A.7",
          "A.10"
        ],
        "omb-m25-22": [
          "Data rights on termination"
        ],
        "csa-aicm": [
          "DSP"
        ]
      },
      "nistSubcategoryText": {}
    },
    {
      "id": "TS-PROC-04",
      "section": "PROC",
      "question": "Will you accept contractual audit or monitoring rights?",
      "why": "M-25-22 requires agencies to secure continuous monitoring rights; enterprise buyers are copying it.",
      "answerType": "boolean",
      "evidenceExpected": "Standard contract terms on audit rights",
      "mappings": {
        "nist-ai-rmf": [
          "GOVERN 6.1"
        ],
        "iso-42001": [
          "A.10"
        ],
        "omb-m25-22": [
          "Continuous monitoring rights"
        ],
        "csa-aicm": [
          "AA",
          "STA"
        ]
      },
      "nistSubcategoryText": {
        "GOVERN 6.1": "Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third party’s intellectual property or other rights."
      }
    },
    {
      "id": "TS-PROC-05",
      "section": "PROC",
      "question": "Do you carry insurance covering AI-related liability?",
      "why": "A growing procurement ask, and the mechanism that converts governance into a priced risk.",
      "answerType": "boolean",
      "evidenceExpected": "Certificate of insurance",
      "mappings": {
        "iso-42001": [
          "6.1"
        ],
        "csa-aicm": [
          "GRC"
        ]
      },
      "nistSubcategoryText": {}
    }
  ]
}