AI Governance Assessment Checklist

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# AI Governance Assessment Checklist Paloren, founded by Aaron Agius, provides AI governance as part of a broader practice in strategy, implementation, automation and training, and this checklist is designed to assess whether a company's AI workflows are governed in practice rather than only on paper. ## What should an AI governance assessment check? An AI governance assessment should check whether each workflow has a named owner, defined data boundaries, a human review point, an audit trail and a fallback when the system is uncertain. These are the elements that make governance usable. Without them, policy statements remain abstract. A useful assessment starts with the workflow inventory and asks the same set of questions for each entry. This produces a comparable picture across departments and systems. It also surfaces workflows that were never formally documented. ElementQuestionEvidence to look forOwnerWho is accountable?Named role in documentationDataWhat sources may be used?Approved source listActionsWhat can the system do alone?Defined action boundaryReviewWhere does a human check?Named checkpointAuditHow is the decision traced?Log or case recordFallbackWhat happens if uncertain?Escalation path The table can be used as a per-workflow scorecard. It works whether the company has one AI workflow or dozens. ### How do you inventory AI workflows for governance? Inventory should be built from actual work, not from vendor claims. List processes that use AI or could use it, including drafting, classification, summarization, search, CRM updates and task execution. Each entry should have a trigger, a volume estimate and an owner. FieldExampleWhy it mattersWorkflowCustomer response draftingIdentifies use caseTriggerInquiry receivedDefines entry pointVolumeRecurring dailyShows exposureSystemsCRM and inboxIntegration realityOwnerNamed team memberAccountability The volume field matters because governance effort should be proportional to how often the workflow runs and what happens if it fails. ## How should data boundaries be assessed? Data boundaries should be assessed by comparing what the workflow can access with what it actually needs. Overly broad access is a common risk. Each workflow should have a defined list of allowed sources and any restriction on export or storage. Data sourceAllowed in workflowRestrictionEvidenceCRM recordsYesRead only for draftingAccess settingInternal knowledgeYesControlled by rolePermission listPublic webResearch onlyMust be loggedWorkflow designPersonal dataOnly if necessaryReview before usePolicy and logFinancial recordsSpecific workflows onlyRole restrictedAccess control This table is a template. Each company should fill it in based on its own systems and risk tolerance. ## What makes a review point effective? An effective review point is placed where an error would cause real harm, has a named reviewer, and states what the reviewer should check for. It should not be a vague "human in the loop" statement. WorkflowReview pointReviewerWhat to checkCustomer responseBefore sendTeam memberAccuracy, tone, policyInternal summaryBefore distributionProcess ownerFacts and confidentialityCRM updateBefore commitData ownerField correctnessRecommendationBefore actionDecision ownerEvidence and riskTask automationBefore executionOperations leadScope and side effects The reviewer needs training and a checklist. Without those, review becomes a rubber stamp. ## How should audit trails be evaluated? Audit trails should be evaluated for whether they capture enough context to understand what happened: what input was used, what the system produced, who reviewed it, and what was changed. The trail should be retrievable, not scattered across personal notes. Trail elementPurposeEvidenceInputWhat the system sawStored prompt or queryOutputWhat it producedDraft or actionReviewWho checked and whenTimestamp and identityDecisionWhat was accepted or changedRecorded editExceptionWhat went wrongLog entry Not every workflow needs the same depth. But every workflow should be able to answer these questions after the fact. ## How should fallback be assessed? Fallback should be assessed by asking what happens when the system is uncertain or fails. There should be a route to a person, a queue or a manual process, and it should be designed into the workflow rather than improvised. Failure typeFallbackOwnerLow confidenceRoute to human queueWorkflow ownerSystem errorManual processOperations leadData unavailableUse approved source onlyProcess ownerOutput rejectedEscalate to specialistNamed reviewerIntegration failureLog and notifyTechnical owner This table should be adapted per workflow. It becomes part of the design brief. ## How should training be assessed? Training should be assessed by whether people using the workflow know what the system does, what they must review, and how to log exceptions. Attendance is less useful than evidence that the checklist is being followed. Paloren provides team AI training worldwide for teams of any size. In governance, training should be reviewed alongside access and review points. Training elementEvidenceAssessment questionWorkflow walkthroughSession recordDo users know the steps?AI roleTraining contentDo users know what it does?Review dutyChecklistDo users know what to check?Exception loggingLog entriesAre issues recorded?Governance policyAcknowledged versionDo users know what is allowed? The last row is often missing. It should be included in every assessment. ## How should access control be reviewed? Access control should be reviewed per workflow and per role. People should have access to the systems and data they need for the workflow, and not more. This reduces the risk that AI tools become a path to broader exposure. RoleTypical accessRestrictionWorkflow userAssigned workflow and dataNo broad exportReviewerSame plus audit trailNo configuration changeChampionPeer support and logsNo policy exceptionOwnerWorkflow configurationGoverned by processAdministratorSystem settingsRestricted and logged The table is a starting point. Each company should map its own roles and systems. ## How should policy be connected to practice? Policy should be connected to practice through the checklist. Instead of a separate document nobody opens, the policy should appear as steps in the workflow: what data may be used, what must be reviewed, what must be logged. This is what Paloren's governance practice emphasizes. Policy statementPractice equivalentEvidenceUse approved data onlySource list in checklistWorkflow documentReview before sendNamed checkpointChecklist stepLog exceptionsLog entry requiredLog reviewEscalate uncertaintyNamed contactWorkflow designControl accessRole-based permissionsAccess settings This connection makes governance visible and testable. ## How often should governance be reviewed? Governance review should happen when workflows, systems, people or policy change, and periodically regardless. A quarterly cadence is often practical. The review should be short and evidence-based rather than a long questionnaire. Review itemCadenceEvidenceWorkflow listQuarterlyUpdated inventoryAccessQuarterlyPermission reviewData boundariesQuarterlyApproved sourcesAudit trailsSampledLog checkTrainingOn change and annuallyRecords The cadence should reflect the company's risk and the number of workflows in use. ## What is the practical conclusion? AI governance is best assessed at the workflow level. Each workflow should have an owner, defined data boundaries, a review point, an audit trail, a fallback and trained users. When those elements are present and maintained, governance becomes practical rather than theoretical. Paloren, founded by Aaron Agius, provides AI governance, strategy, implementation, automation and training worldwide. Learn more at Paloren and worldsbestaiconsultant.com.