{"title":"AI Usage, Spend & Value (VAL) Requirements — 9 Workforce AI Security Controls","description":"Measure adoption, spend and business value so AI investment decisions are evidence-led. 9 vendor evaluation requirements with acceptance criteria, verification tests and framework mappings.","updated":"2026-10-21","count":9,"requirements":[{"id":"SPM-008","section":"AI Security Posture Management","requirement":"Distinguish personal, free and guest accounts from sanctioned enterprise tenants","acceptance":"Report a reliable account-type split at the point of use and allow policy to vary depending on account plan.","tier":"AT0, AT1","whyItMatters":"Contractual protections attach to the enterprise tenant, not the domain. A personal account reaches the same URL with none of them.","verificationMethod":"LT","verificationTest":"Enterprise, personal and unauthenticated use of the same tool are each identified and handled per policy.","formerly":"DIS-006","alsoIn":["value"],"sort":7,"adoptionContext":"Personal-account and vendor-assistant contexts make account ownership and plan distinctions important; tier alone does not establish contractual safeguards.","mappings":[{"frameworks":["rmf"],"explanation":"The account-plan distinction helps identify the third-party service context and supplier terms needing risk review. A plan label alone does not establish the applicable relationship or complete its assessment.","kind":"framework","references":{"rmf":["MAP 4.1"]},"relation":null,"conditions":[]},{"frameworks":["soc2","iso27001"],"explanation":"Reliable account-tier context can inform permitted data destinations, access restrictions and enterprise-cloud or supplier rules. The policy linkage must work in practice; classification alone does not enforce those rules.\nISO/IEC 27001: Account identification does not manage the identity lifecycle.","kind":"framework","references":{"soc2":["CC6.7","CC9.2"],"iso27001":["A.8.3","A.5.23"]},"relation":null,"conditions":[]},{"frameworks":["csf"],"explanation":"The observed personal, free, guest or enterprise account context supports monitoring employee technology use. An account-plan label does not manage identities or credentials or enforce access.","kind":"framework","references":{"csf":["DE.CM-03"]},"relation":null,"conditions":[]}],"area":"AI Security Posture Management","areaSlug":"SPM","url":"/r/SPM-008","frameworkRefs":{"ISO27001":["A.8.3","A.5.23"],"NIST-CSF":["DE.CM-03"],"NIST-AI-RMF":["MAP 4.1"],"SOC2":["CC6.7","CC9.2"]}},{"id":"IDR-003","section":"AI Detection & Response","requirement":"Identify use cases leading to elevated risk behavior.","acceptance":"Surface the use cases and teams that require additional training on safe usage principles.","tier":"AT0–AT4","whyItMatters":"Repeat exposure and under-use point to where risk reduction or enablement will pay off.","verificationMethod":"POV","verificationTest":"High-risk use cases and the associated users are identified in a pilot.","formerly":"VAL-005","alsoIn":["value"],"sort":34,"adoptionContext":"Risky use and repeat exposure patterns can appear across the listed employee AI settings; low use is not itself proof of risk or failure.","mappings":[{"frameworks":["csf","rmf","soc2","iso27001"],"explanation":"Team-level risky-use and recurring-exposure patterns provide findings for usage monitoring, AI-risk tracking and evaluation against responsible-use/security objectives. Under-use alone is an enablement signal rather than a security anomaly; assessment and intervention remain necessary beyond surfacing patterns.\nISO/IEC 27001: Confirm the need for monitoring and choose the interventions to apply.","kind":"framework","references":{"csf":["DE.CM-03"],"rmf":["MEASURE 3.1"],"soc2":["CC7.2"],"iso27001":["Cl. 9.1","A.8.16"]},"relation":null,"conditions":[]},{"frameworks":["iso"],"explanation":"Observed risky-use and recurring-exposure patterns provide ongoing operational-monitoring findings for in-scope AI systems or controls. Under-use alone is an enablement signal; surfacing patterns does not establish responsible-use objectives or complete assessment and intervention.","kind":"framework","references":{"iso":["A.6.2.6"]},"relation":null,"conditions":[]}],"area":"AI Detection & Response","areaSlug":"AIDR","url":"/r/IDR-003","frameworkRefs":{"ISO27001":["Cl. 9.1","A.8.16"],"ISO42001":["A.6.2.6"],"NIST-CSF":["DE.CM-03"],"NIST-AI-RMF":["MEASURE 3.1"],"SOC2":["CC7.2"]}},{"id":"VAL-001","section":"AI Usage, Spend & Value","requirement":"Measure AI usage and adoption by tool, team, function and workflow","acceptance":"Report volumes, active users, trends and the sanctioned/unsanctioned split at privacy-appropriate aggregation.","tier":"AT0–AT4","whyItMatters":"Usage is the denominator for every cost, risk and value figure.","verificationMethod":"POV","verificationTest":"Usage by tool, team and function from pilot data, at privacy-appropriate aggregation.","formerly":null,"alsoIn":[],"sort":45,"adoptionContext":"Adoption reporting spans the listed employee AI contexts so sanctioned usage does not hide shadow or agent-assisted activity; privacy limits still govern the telemetry.","mappings":[{"frameworks":["csf","soc2","iso27001"],"explanation":"Workforce usage and the sanctioned/unsanctioned split provide technology-monitoring context and inputs to security-performance and acceptable-use review. Adoption volumes and active-user counts alone are business analytics; security conclusions need relevant analysis and follow-up.","kind":"framework","references":{"csf":["DE.CM-03"],"soc2":["CC7.2"],"iso27001":["Cl. 9.1","A.5.36"]},"relation":null,"conditions":[]},{"frameworks":["iso"],"explanation":"The sanctioned and unsanctioned usage split provides policy-performance evidence when included in AI-management-system evaluation. Define the relevant objectives and evaluation method; pure activity volumes do not establish responsible-use objectives or effectiveness.","kind":"framework","references":{"iso":["Cl. 9.1"]},"relation":null,"conditions":[]}],"area":"AI Usage, Spend & Value","areaSlug":"VAL","url":"/r/VAL-001","frameworkRefs":{"ISO27001":["Cl. 9.1","A.5.36"],"ISO42001":["Cl. 9.1"],"NIST-CSF":["DE.CM-03"],"SOC2":["CC7.2"]}},{"id":"VAL-002","section":"AI Usage, Spend & Value","requirement":"Classify AI activity into business-relevant use cases","acceptance":"Turn raw telemetry into recognizable tasks, workflows and functions.","tier":"Tier-independent","whyItMatters":"Tool-level data cannot tell a team drafting contracts from one summarizing public research.","verificationMethod":"POV","verificationTest":"Top use cases by function from pilot data, with personal conversations excluded.","formerly":null,"alsoIn":[],"sort":46,"adoptionContext":"Business-use classification can describe activity from any adoption tier; a deployment pattern alone does not identify the business purpose.","mappings":[{"frameworks":["rmf","iso"],"explanation":"Recognizable tasks and workflows provide context for documenting intended purposes and beneficial uses, and allow actual activity to be compared with intended use. Observed activity does not itself determine that purpose or demonstrate conformity with it.","kind":"framework","references":{"rmf":["MAP 1.1"],"iso":["A.9.4"]},"relation":null,"conditions":[]}],"area":"AI Usage, Spend & Value","areaSlug":"VAL","url":"/r/VAL-002","frameworkRefs":{"ISO42001":["A.9.4"],"NIST-AI-RMF":["MAP 1.1"]}},{"id":"VAL-003","section":"AI Usage, Spend & Value","requirement":"Measure spend, waste and portfolio decisions","acceptance":"Report licenses, tokens, provider cost, model efficiency, redundant tools and cost per task or outcome, and produce the evidence behind a decision to scale, pause, optimize or retire a tool.","tier":"Tier-independent","whyItMatters":"License waste and duplicate tools are often the fastest way to fund the control.","verificationMethod":"POV + W","verificationTest":"Licensed versus active use for one enterprise AI tool, and the cost method explained.","formerly":null,"alsoIn":[],"sort":47,"adoptionContext":"Portfolio cost, overlap and retirement decisions can compare AI services across adoption tiers rather than treating a tier as a measure of value.","mappings":[{"frameworks":["rmf"],"explanation":"Cost and outcome evidence can inform a decision to retire an AI tool. The relationship is an input to decommissioning decisions; financial optimization does not establish safe retirement or treatment of residual risks.","kind":"framework","references":{"rmf":["GOVERN 1.7"]},"relation":null,"conditions":[]},{"frameworks":["iso"],"explanation":"Token use and model efficiency provide resource-consumption information for identified in-scope AI services. Cost or ROI alone is insufficient, and these metrics establish neither capacity adequacy nor responsible-AI supplier alignment.","kind":"framework","references":{"iso":["A.4.5"]},"relation":null,"conditions":[]}],"area":"AI Usage, Spend & Value","areaSlug":"VAL","url":"/r/VAL-003","frameworkRefs":{"ISO42001":["A.4.5"],"NIST-AI-RMF":["GOVERN 1.7"]}},{"id":"VAL-004","section":"AI Usage, Spend & Value","requirement":"Measure productivity and business-value signals","acceptance":"Identify time saved, throughput, completion and quality indicators with stated assumptions.","tier":"Tier-independent","whyItMatters":"Risk without value is a brake. Value without risk is a blind spot.","verificationMethod":"POV","verificationTest":"Value indicators for pilot functions, shown with their assumptions.","formerly":null,"alsoIn":[],"sort":48,"adoptionContext":"Productivity and value evidence can be assessed across adoption tiers; the workflow, baseline and assumptions determine meaningful comparisons.","mappings":[{"frameworks":["rmf"],"explanation":"Time saved, throughput and quality indicators with stated assumptions inform benefit assessment and context-relevant AI performance evaluation against chosen responsible-use objectives. Their meaning and validity need evaluation; reporting business-value metrics alone establishes neither realized benefit nor security effectiveness.","kind":"framework","references":{"rmf":["MAP 3.1","MEASURE 4.3"]},"relation":null,"conditions":[]}],"area":"AI Usage, Spend & Value","areaSlug":"VAL","url":"/r/VAL-004","frameworkRefs":{"NIST-AI-RMF":["MAP 3.1","MEASURE 4.3"]}},{"id":"VAL-005","section":"AI Usage, Spend & Value","requirement":"Generate targeted training and enablement recommendations","acceptance":"Turn observed usage and detection telemetry into prioritized interventions, templates, coaching and sanctioned-tool adoption, aimed at the teams and behaviors that actually generate risk, and measure whether behavior changes.","tier":"","whyItMatters":"Training aimed at the teams and behaviors that actually create risk works better than blanket training.","verificationMethod":"POV","verificationTest":"Prioritized enablement recommendations from pilot data, and a measure of whether behavior changed.","formerly":null,"alsoIn":[],"sort":49,"adoptionContext":"Training recommendations should reflect observed needs across the listed employee AI contexts instead of assuming that adoption tier determines competence.","mappings":[{"frameworks":["csf","rmf","iso","soc2"],"explanation":"Usage and detection telemetry can target AI/security training and coaching at relevant teams and roles, while behavior changes provide evaluation evidence. Recommendations do not establish delivered training or required competence; effectiveness measures must relate to the awareness or risk-control objective being evaluated.","kind":"framework","references":{"csf":["PR.AT-01","PR.AT-02"],"rmf":["GOVERN 2.2"],"iso":["Cl. 7.2","Cl. 7.3"],"soc2":["CC1.4","CC2.2","CC4.1"]},"relation":null,"conditions":[]},{"frameworks":["iso27001"],"explanation":"Measured changes in risky behavior provide security-effectiveness evidence; targeted coaching can support awareness when it reaches employees. Define the relevant objective, baseline, responsibility, measurement cadence and evaluation. Recommendations alone are not delivered training and do not determine or assess required competence.","kind":"framework","references":{"iso27001":["Cl. 9.1","Cl. 7.3","A.6.3"]},"relation":null,"conditions":[]}],"area":"AI Usage, Spend & Value","areaSlug":"VAL","url":"/r/VAL-005","frameworkRefs":{"ISO27001":["Cl. 9.1","Cl. 7.3","A.6.3"],"ISO42001":["Cl. 7.2","Cl. 7.3"],"NIST-CSF":["PR.AT-01","PR.AT-02"],"NIST-AI-RMF":["GOVERN 2.2"],"SOC2":["CC1.4","CC2.2","CC4.1"]}},{"id":"VAL-006","section":"AI Usage, Spend & Value","requirement":"Measure employee self-remediation","acceptance":"Record whether a warned user cancels, edits, redirects or proceeds, and calculate a remediation rate where privacy allows.","tier":"AT0–AT4","whyItMatters":"Self-remediation shows whether coaching changes behavior or just records it.","verificationMethod":"POV","verificationTest":"Share of coached pilot interventions where the employee edits or cancels, and the trend over the pilot.","formerly":null,"alsoIn":[],"sort":50,"adoptionContext":"Shadow, embedded-assistant and integrated-platform interactions are primary contexts for observing whether employees cancel, edit or proceed after a warning.","mappings":[{"frameworks":["csf","iso"],"explanation":"Post-warning cancel, edit, redirect and proceed behavior provides evidence for evaluating AI/security awareness interventions and identifying improvements. A remediation rate does not prove competence, safe final content or an effective overall program; meaningful denominators and privacy-appropriate collection are needed.","kind":"framework","references":{"csf":["ID.IM-01","PR.AT-01"],"iso":["Cl. 7.3","Cl. 9.1"]},"relation":null,"conditions":[]},{"frameworks":["rmf"],"explanation":"Cancel, edit, redirect and proceed telemetry can inform context-sensitive evaluation of measurements. It complements rather than replaces consultation with end users and domain experts.","kind":"framework","references":{"rmf":["MEASURE 4.1"]},"relation":null,"conditions":[]},{"frameworks":["soc2"],"explanation":"Responses to a security warning and the remediation rate provide evidence for evaluating that intervention. Define the denominator and sampling window and use privacy-appropriate collection; a click or edit does not prove safe final content or an effective overall program.","kind":"framework","references":{"soc2":["CC4.1"]},"relation":null,"conditions":[]},{"frameworks":["iso27001"],"explanation":"Responses to a security warning and a defined remediation rate evaluate a specific security intervention. The training link applies when the metric evaluates a delivered awareness intervention. Define the objective, denominator, sampling window, responsibility, cadence and evaluation; a click or edit does not prove safe content, competence or understanding of ISMS duties.","kind":"framework","references":{"iso27001":["Cl. 9.1","A.6.3"]},"relation":null,"conditions":[]}],"area":"AI Usage, Spend & Value","areaSlug":"VAL","url":"/r/VAL-006","frameworkRefs":{"ISO27001":["Cl. 9.1","A.6.3"],"ISO42001":["Cl. 7.3","Cl. 9.1"],"NIST-CSF":["ID.IM-01","PR.AT-01"],"NIST-AI-RMF":["MEASURE 4.1"],"SOC2":["CC4.1"]}},{"id":"VAL-007","section":"AI Usage, Spend & Value","requirement":"Provide role-based dashboards and reporting, up to board level","acceptance":"Tailor views for Security, IT, GRC, Legal, executives and business units, filter by unit, tool, environment, owner and risk, and summarize adoption, risk, policy, incidents and recommendations with trends and supporting evidence.","tier":"AT0–AT2","whyItMatters":"Each audience, from SOC to board, needs its own view of the same data.","verificationMethod":"W","verificationTest":"The same pilot data shown through executive, function and security views.","formerly":"GOV-009","alsoIn":[],"sort":51,"adoptionContext":"Role-based governance reporting can combine workforce AI evidence from any adoption tier while limiting the detail each audience receives.","mappings":[{"frameworks":["iso","soc2","iso27001"],"explanation":"Evidence-backed risk, policy and incident trends provide information for performance evaluation, oversight and management review. Responsible people must receive, review and act on it; a dashboard is not a completed review, and adoption metrics alone are not security-effectiveness evidence.","kind":"framework","references":{"iso":["Cl. 9.1","Cl. 9.3"],"soc2":["CC2.1","CC2.2","CC4.1"],"iso27001":["Cl. 9.1","Cl. 9.3.2"]},"relation":null,"conditions":[]},{"frameworks":["rmf"],"explanation":"Role-tailored reporting helps communicate AI-risk information to people with governance responsibilities across teams. Providing views does not itself assign those responsibilities or establish accountability.","kind":"framework","references":{"rmf":["GOVERN 2.1"]},"relation":null,"conditions":[]},{"frameworks":["csf"],"explanation":"Evidence-backed risk, policy and incident trends provide inputs to cybersecurity performance oversight. Responsible people must review and use the reporting; a dashboard is not a completed review, and adoption alone is not security-effectiveness evidence.","kind":"framework","references":{"csf":["GV.OV-03"]},"relation":null,"conditions":[]}],"area":"AI Usage, Spend & Value","areaSlug":"VAL","url":"/r/VAL-007","frameworkRefs":{"ISO27001":["Cl. 9.1","Cl. 9.3.2"],"ISO42001":["Cl. 9.1","Cl. 9.3"],"NIST-CSF":["GV.OV-03"],"NIST-AI-RMF":["GOVERN 2.1"],"SOC2":["CC2.1","CC2.2","CC4.1"]}}]}