{"id":16860,"date":"2026-09-19T11:49:03","date_gmt":"2026-09-19T11:49:03","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=16860"},"modified":"2026-09-19T11:49:03","modified_gmt":"2026-09-19T11:49:03","slug":"isaca-aaism-practice-test-questions-and-exam-dumps-part8-q141-160","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/isaca-aaism-practice-test-questions-and-exam-dumps-part8-q141-160\/","title":{"rendered":"Isaca AAISM Practice Test Questions and Exam Dumps Part8 Q141-160"},"content":{"rendered":"

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View Full <\/b>Isaca AAISM Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/p>\n

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Question 141. Which activity best supports effective AI governance across an organization’s business units?<\/b><\/h3>\n
    \n
  1. <\/b> Allow each business unit to establish unrelated AI requirements<\/span><\/li>\n
  2. <\/b> Centralize every AI decision with the development team<\/span><\/li>\n
  3. <\/b> Establish consistent enterprise principles while allowing appropriate business-unit responsibilities<\/span><\/li>\n
  4. <\/b> Require internal audit to operate every AI system<\/span><\/li>\n<\/ol>\n

    Correct Answer: 3. Establish consistent enterprise principles while allowing appropriate business-unit responsibilities<\/b><\/p>\n

    Explanation:<\/b><\/p>\n

    Enterprise AI governance should provide consistent principles, minimum requirements, and accountability while recognizing that individual business units may have different operational needs and risk profiles. Centralizing every decision can create bottlenecks and may prevent subject-matter experts from addressing business-specific risks. Conversely, allowing each unit to establish unrelated requirements can create inconsistent controls and gaps in oversight. A federated or appropriately distributed governance model can establish enterprise-wide expectations while assigning operational responsibilities to qualified teams. Governance should clearly define which decisions require enterprise approval and which can be handled locally. This approach supports consistency, accountability, scalability, and practical implementation without unnecessarily removing responsibility from business owners.<\/span><\/p>\n

    Question 142. What is the primary purpose of clearly assigning AI risk ownership?<\/b><\/h3>\n
      \n
    1. <\/b> To ensure that identified risks have an accountable person responsible for assessment, treatment, monitoring, and escalation<\/span><\/li>\n
    2. <\/b> To transfer all AI risks to the security department<\/span><\/li>\n
    3. <\/b> To eliminate the need for risk assessments<\/span><\/li>\n
    4. <\/b> To ensure that vendors accept all organizational risks<\/span><\/li>\n<\/ol>\n

      Correct Answer: 1. To ensure that identified risks have an accountable person responsible for assessment, treatment, monitoring, and escalation<\/b><\/p>\n

      Explanation:<\/b><\/p>\n

      Clear risk ownership is essential because unidentified or ambiguous accountability can result in risks being overlooked or unresolved. An AI risk owner should understand the relevant business process and have sufficient authority to coordinate assessment, treatment, monitoring, and escalation. Ownership does not necessarily mean that one individual performs every risk-management activity. Security, privacy, legal, compliance, technical, and business teams may contribute specialized expertise. However, a clearly identified accountable owner helps ensure that actions are coordinated and decisions are documented. Risk ownership should also define escalation paths when residual exposure exceeds the owner’s authority or approved tolerance. This structure strengthens governance by connecting identified risks to specific responsibilities and decision-making authority.<\/span><\/p>\n

      Question 143. What should an organization do when a proposed AI policy exception creates risk beyond the approval authority of the requester?<\/b><\/h3>\n
        \n
      1. <\/b> Approve it automatically because the business needs the system<\/span><\/li>\n
      2. <\/b> Delete the policy requirement<\/span><\/li>\n
      3. <\/b> Ignore the additional risk if the AI model performs well<\/span><\/li>\n
      4. <\/b> Escalate the exception to the appropriate authorized governance or risk authority<\/span><\/li>\n<\/ol>\n

        Correct Answer: 4. Escalate the exception to the appropriate authorized governance or risk authority<\/b><\/p>\n

        Explanation:<\/b><\/p>\n

        Policy exceptions should be approved only by individuals or bodies with appropriate authority and accountability. When an exception introduces risk beyond the requester’s authority, the matter should follow a defined escalation path. The higher authority can evaluate the business justification, affected systems, compensating controls, duration, and residual risk before deciding whether the exception is acceptable. Automatic approval can undermine governance, while deleting a policy because it creates inconvenience removes the control rather than addressing the underlying risk. Strong exception management ensures that deviations remain visible, justified, time-bound, and accountable. Escalation also provides management with information about recurring exceptions that may indicate weaknesses or unrealistic requirements in the existing governance framework.<\/span><\/p>\n

        Question 144. Why should organizations maintain an enterprise inventory of AI initiatives?<\/b><\/h3>\n
          \n
        1. <\/b> To prevent business units from using AI<\/span><\/li>\n
        2. <\/b> To provide visibility into AI systems, owners, purposes, risk levels, dependencies, and governance status<\/span><\/li>\n
        3. <\/b> To replace all system documentation<\/span><\/li>\n
        4. <\/b> To track only AI systems developed by external vendors<\/span><\/li>\n<\/ol>\n

          Correct Answer: 2. To provide visibility into AI systems, owners, purposes, risk levels, dependencies, and governance status<\/b><\/p>\n

          Explanation:<\/b><\/p>\n

          An enterprise AI inventory provides a centralized view of the organization’s AI landscape. Without such visibility, management may not know which systems are deployed, who owns them, what data they process, which vendors support them, or what governance requirements apply. An inventory can record information such as business purpose, system owner, model type, data categories, risk classification, lifecycle status, dependencies, approval status, and review dates. This information supports risk assessment, regulatory analysis, monitoring, incident response, and resource planning. The inventory should not replace detailed system documentation; instead, it provides an enterprise-level reference that allows governance teams to identify relationships and prioritize oversight across the organization’s complete AI portfolio.<\/span><\/p>\n

          Question 145. Which factor should influence the priority assigned to an AI governance review?<\/b><\/h3>\n
            \n
          1. <\/b> The color used in the system’s user interface<\/span><\/li>\n
          2. <\/b> The number of developers assigned to the project<\/span><\/li>\n
          3. <\/b> The model’s programming language<\/span><\/li>\n
          4. <\/b> Potential impact, risk exposure, regulatory obligations, data sensitivity, and changes since the previous review<\/span><\/li>\n<\/ol>\n

            Correct Answer: 4. Potential impact, risk exposure, regulatory obligations, data sensitivity, and changes since the previous review<\/b><\/p>\n

            Explanation:<\/b><\/p>\n

            AI governance reviews should be risk-based so that resources are directed toward systems where oversight can provide the greatest value. Factors such as potential impact, sensitivity of processed information, regulatory obligations, decision significance, security exposure, and material changes can influence review priority. A system affecting important decisions or processing sensitive information may require more frequent or detailed review than a low-impact internal application. Changes to the model, data, purpose, vendor, or operating environment can also trigger reassessment. Developer count, programming language, or interface design may matter operationally but generally should not determine governance priority by themselves. A structured prioritization process promotes consistent oversight and helps organizations focus governance resources on material risks.<\/span><\/p>\n

            Question 146. What is a key governance consideration when an AI initiative depends on several interconnected systems?<\/b><\/h3>\n
              \n
            1. <\/b> Assess dependencies and understand how failures or changes in one component could affect the broader AI process<\/span><\/li>\n
            2. <\/b> Treat every system as completely independent<\/span><\/li>\n
            3. <\/b> Remove all documentation concerning system interfaces<\/span><\/li>\n
            4. <\/b> Allow each supplier to define its own organizational risk tolerance<\/span><\/li>\n<\/ol>\n

              Correct Answer: 1. Assess dependencies and understand how failures or changes in one component could affect the broader AI process<\/b><\/p>\n

              Explanation:<\/b><\/p>\n

              AI systems frequently depend on multiple components, including data pipelines, model providers, application interfaces, cloud services, identity systems, monitoring tools, and external APIs. Governance should therefore consider dependencies and the potential impact of changes or failures across the complete workflow. A weakness in one component may affect the confidentiality, integrity, availability, accuracy, or compliance of the overall AI service. Dependency mapping can support risk assessments, business continuity planning, incident response, vendor management, and change control. Treating each component as completely independent can hide systemic risks. Governance should establish ownership and monitoring responsibilities for important dependencies and ensure that significant changes are evaluated for their potential effects on connected AI processes.<\/span><\/p>\n

              Question 147. Which requirement is most appropriate for an AI procurement process involving sensitive organizational data?<\/b><\/h3>\n
                \n
              1. <\/b> Select the provider based only on advertised model accuracy<\/span><\/li>\n
              2. <\/b> Require assessment of data handling, security, privacy, retention, access, and contractual protections before approval<\/span><\/li>\n
              3. <\/b> Permit unrestricted vendor reuse of organizational data<\/span><\/li>\n
              4. <\/b> Avoid reviewing subcontractors used by the provider<\/span><\/li>\n<\/ol>\n

                Correct Answer: 2. Require assessment of data handling, security, privacy, retention, access, and contractual protections before approval<\/b><\/p>\n

                Explanation:<\/b><\/p>\n

                When sensitive organizational data will be processed by an external AI provider, procurement should evaluate how that information is collected, transmitted, stored, accessed, retained, deleted, and potentially reused. Security and privacy controls should be assessed alongside contractual provisions governing confidentiality, subcontractors, incident notification, data ownership, permitted processing, and termination. Model accuracy may be important for business performance, but it does not address the full set of risks created by external data processing. Organizations should also understand whether the provider uses submitted information for model training or service improvement and whether appropriate restrictions can be established. A documented procurement assessment helps ensure that sensitive data is not exposed to unmanaged third-party risks.<\/span><\/p>\n

                Question 148. What should an organization verify before approving an AI vendor that uses subcontractors to process organizational information?<\/b><\/h3>\n
                  \n
                1. <\/b> Only the subcontractor’s marketing materials<\/span><\/li>\n
                2. <\/b> Whether the subcontractor has the largest available AI model<\/span><\/li>\n
                3. <\/b> Relevant security, privacy, contractual, geographic, and oversight requirements applicable to the subcontractor relationship<\/span><\/li>\n
                4. <\/b> Whether the subcontractor can eliminate all organizational governance requirements<\/span><\/li>\n<\/ol>\n

                  Correct Answer: 3. Relevant security, privacy, contractual, geographic, and oversight requirements applicable to the subcontractor relationship<\/b><\/p>\n

                  Explanation:<\/b><\/p>\n

                  Subcontractors can introduce additional risks because organizational information may be processed outside the direct control of the primary vendor. Governance should establish visibility into material subcontractors and evaluate whether their activities are consistent with contractual, security, privacy, regulatory, and geographic requirements. Depending on the service, organizations may require prior notification or approval of material subcontractor changes, flow-down security obligations, incident notification requirements, and appropriate audit or assurance rights. Geographic considerations can also matter when data crosses jurisdictions. The primary vendor should remain accountable for meeting contractual obligations rather than shifting responsibility entirely to the subcontractor. Understanding the extended service chain helps organizations identify risks that might otherwise remain hidden during vendor due diligence.<\/span><\/p>\n

                  Question 149. Which contractual provision can best support organizational control over AI-related security incidents involving a service provider?<\/b><\/h3>\n
                    \n
                  1. <\/b> A requirement that the provider notify the organization within defined timeframes and cooperate with investigation and remediation<\/span><\/li>\n
                  2. <\/b> A statement that the provider is never responsible for incidents<\/span><\/li>\n
                  3. <\/b> A prohibition on reporting security events<\/span><\/li>\n
                  4. <\/b> An agreement that incident information will only be disclosed after contract termination<\/span><\/li>\n<\/ol>\n

                    Correct Answer: 1. A requirement that the provider notify the organization within defined timeframes and cooperate with investigation and remediation<\/b><\/p>\n

                    Explanation:<\/b><\/p>\n

                    Incident notification and cooperation provisions are important when an organization relies on an external AI provider. Contracts should establish expectations for notifying the organization about relevant security or privacy incidents within defined timeframes and provide sufficient information to support assessment and response. Depending on the risk, the provider may also be expected to preserve relevant evidence, cooperate with investigations, support containment and remediation, and provide updates until resolution. Without contractual requirements, the organization may have limited visibility into provider incidents and may struggle to meet its own reporting or regulatory responsibilities. These provisions should align with the organization’s incident response procedures and the criticality of the AI service being provided.<\/span><\/p>\n

                    Question 150. Why should AI governance evaluate geographic and jurisdictional considerations for external AI services?<\/b><\/h3>\n
                      \n
                    1. <\/b> Geographic location has no effect on AI governance<\/span><\/li>\n
                    2. <\/b> Jurisdiction can affect data protection obligations, contractual requirements, regulatory exposure, and where information may be processed<\/span><\/li>\n
                    3. <\/b> Geographic assessment is required only for low-risk AI systems<\/span><\/li>\n
                    4. <\/b> Jurisdiction determines whether an AI model is accurate<\/span><\/li>\n<\/ol>\n

                      Correct Answer: 2. Jurisdiction can affect data protection obligations, contractual requirements, regulatory exposure, and where information may be processed<\/b><\/p>\n

                      Explanation:<\/b><\/p>\n

                      External AI services may process organizational or personal information across different countries or legal jurisdictions. The location of processing, storage, support operations, and subcontractors can affect applicable privacy laws, regulatory requirements, contractual obligations, government-access considerations, and cross-border transfer requirements. Governance should therefore identify relevant processing locations and evaluate whether they are consistent with organizational requirements and applicable law. This consideration is especially important for sensitive or regulated information. Geographic risk should be addressed during procurement and periodically reassessed when providers change infrastructure or subcontractors. Jurisdictional analysis does not determine model accuracy, but it can materially affect the organization’s legal, privacy, compliance, and operational risk associated with the service.<\/span><\/p>\n

                      Question 151. What is an important governance requirement for AI models obtained from external sources?<\/b><\/h3>\n
                        \n
                      1. <\/b> Assume external models require no validation<\/span><\/li>\n
                      2. <\/b> Deploy the model immediately if the provider has a strong reputation<\/span><\/li>\n
                      3. <\/b> Evaluate provenance, security, licensing, suitability, limitations, and validation requirements before production use<\/span><\/li>\n
                      4. <\/b> Remove all monitoring after deployment<\/span><\/li>\n<\/ol>\n

                        Correct Answer: 3. Evaluate provenance, security, licensing, suitability, limitations, and validation requirements before production use<\/b><\/p>\n

                        Explanation:<\/b><\/p>\n

                        Externally obtained AI models should not automatically be trusted simply because they are supplied by a recognized provider or community. Governance should establish requirements for understanding the model’s provenance, licensing terms, intended use, known limitations, security characteristics, dependencies, and applicable validation requirements. The organization should determine whether the model is suitable for the intended business purpose and risk level. Testing may need to evaluate performance, robustness, bias, security, and behavior under relevant conditions. Documentation should also record the model version and source so that future changes can be traced. External models remain part of the organization’s AI environment once deployed, meaning they should be subject to appropriate lifecycle governance and monitoring.<\/span><\/p>\n

                        Question 152. Which practice best reduces the risk of undocumented AI model changes in production?<\/b><\/h3>\n
                          \n
                        1. <\/b> Permit developers to replace models without notification<\/span><\/li>\n
                        2. <\/b> Maintain version control, change approvals, deployment records, and traceability for production model changes<\/span><\/li>\n
                        3. <\/b> Delete previous model versions immediately<\/span><\/li>\n
                        4. <\/b> Allow vendors to change models without contractual notification<\/span><\/li>\n<\/ol>\n

                          Correct Answer: 2. Maintain version control, change approvals, deployment records, and traceability for production model changes<\/b><\/p>\n

                          Explanation:<\/b><\/p>\n

                          Undocumented model changes can make it difficult to determine why system behavior changed, whether validation remains applicable, or who authorized the modification. Version control provides a record of the specific model or configuration deployed at a particular point in time. Change approvals and deployment records add accountability, while traceability allows teams to connect changes with testing and business impacts. For externally managed services, contracts may also need provisions concerning material model changes and notification. Previous versions should generally remain available according to appropriate retention requirements rather than being deleted immediately. These controls support reproducibility, incident investigation, compliance evidence, and controlled lifecycle management, especially when AI outputs influence important business processes.<\/span><\/p>\n

                          Question 153. What governance concern arises when an AI provider automatically changes a production model without customer control?<\/b><\/h3>\n
                            \n
                          1. <\/b> The organization may experience behavior changes without completing its own required assessment, testing, or approval process<\/span><\/li>\n
                          2. <\/b> Automatic changes always improve model performance<\/span><\/li>\n
                          3. <\/b> Automatic updates eliminate the need for monitoring<\/span><\/li>\n
                          4. <\/b> Provider updates cannot affect downstream business processes<\/span><\/li>\n<\/ol>\n

                            Correct Answer: 1. The organization may experience behavior changes without completing its own required assessment, testing, or approval process<\/b><\/p>\n

                            Explanation:<\/b><\/p>\n

                            Automatic provider updates can introduce changes in model behavior, performance, security characteristics, data handling, or system dependencies. If the organization is unaware of material changes, it may be unable to complete required validation or determine whether existing controls remain effective. Governance should therefore establish mechanisms for identifying significant provider changes, understanding their potential impact, and determining whether reassessment or approval is necessary. Contracts may include notification requirements, version information, testing opportunities, or controls over material changes. Continuous monitoring can also help detect unexpected behavioral changes after updates. The objective is not necessarily to prohibit all automatic updates but to ensure that significant changes remain visible and are managed according to the organization’s risk-based change process.<\/span><\/p>\n

                            Question 154. Which control helps demonstrate that an AI governance decision was properly authorized?<\/b><\/h3>\n
                              \n
                            1. <\/b> An undocumented verbal agreement<\/span><\/li>\n
                            2. <\/b> A developer’s personal notes<\/span><\/li>\n
                            3. <\/b> A public advertisement<\/span><\/li>\n
                            4. <\/b> A documented approval record identifying the decision, authority, date, scope, and relevant evidence<\/span><\/li>\n<\/ol>\n

                              Correct Answer: 4. A documented approval record identifying the decision, authority, date, scope, and relevant evidence<\/b><\/p>\n

                              Explanation:<\/b><\/p>\n

                              Governance decisions should be supported by reliable evidence showing what was approved, by whom, when, and under what conditions. An approval record can identify the AI system or use case, decision authority, applicable risk assessment, relevant supporting evidence, conditions of approval, and any required follow-up actions. This documentation supports accountability and allows organizations to demonstrate that appropriate governance processes were followed. Informal verbal agreements may be difficult to verify and can create ambiguity about authority or scope. Approval evidence should also be protected against unauthorized modification and retained according to applicable record-management requirements. Strong documentation enables later review, audit, incident investigation, and reassessment when the AI system or risk environment changes.<\/span><\/p>\n

                              Question 155. Why is AI governance particularly important when AI is integrated into an existing automated business process?<\/b><\/h3>\n
                                \n
                              1. <\/b> Integration automatically removes all existing process risks<\/span><\/li>\n
                              2. <\/b> AI integration may introduce new decision, data, security, performance, and accountability risks into an established process<\/span><\/li>\n
                              3. <\/b> Existing processes never require additional controls<\/span><\/li>\n
                              4. <\/b> AI integration makes human oversight unnecessary<\/span><\/li>\n<\/ol>\n

                                Correct Answer: 2. AI integration may introduce new decision, data, security, performance, and accountability risks into an established process<\/b><\/p>\n

                                Explanation:<\/b><\/p>\n

                                Adding AI to an established business process can change how decisions are made, how information is processed, and where responsibilities reside. Existing controls may have been designed for deterministic or manually reviewed activities and may not adequately address probabilistic model behavior, training-data risks, hallucinations, bias, model drift, or new security threats. Governance should therefore evaluate how AI changes the overall process rather than assuming that existing controls remain sufficient. Responsibilities should be clearly defined, including who reviews outputs, who handles exceptions, and who accepts residual risk. The integration should also be tested in its actual operating context. This ensures that AI-specific risks are incorporated into the broader business process instead of being treated as purely technical concerns.<\/span><\/p>\n

                                Question 156. What should trigger a governance reassessment of an existing AI use case?<\/b><\/h3>\n
                                  \n
                                1. <\/b> Only a change in the office location<\/span><\/li>\n
                                2. <\/b> Only a routine employee vacation<\/span><\/li>\n
                                3. <\/b> Material changes to purpose, data, model, risk level, users, regulations, vendors, or operating environment<\/span><\/li>\n
                                4. <\/b> A change in the application’s font<\/span><\/li>\n<\/ol>\n

                                  Correct Answer: 3. Material changes to purpose, data, model, risk level, users, regulations, vendors, or operating environment<\/b><\/p>\n

                                  Explanation:<\/b><\/p>\n

                                  AI governance should recognize that risk can change after initial approval. A material change in the system’s purpose may alter its impact, while new data sources can introduce privacy or quality concerns. Model changes can affect performance, security, fairness, or explainability. Changes in users, vendors, regulations, or operating environments may also create new obligations or exposures. Governance should therefore define reassessment triggers that identify when an existing approval is no longer sufficient. Not every minor change requires a complete governance review, so organizations should establish risk-based thresholds for determining what constitutes a material change. This approach helps maintain appropriate oversight throughout the AI lifecycle without creating unnecessary administrative burden.<\/span><\/p>\n

                                  Question 157. Which approach best supports governance of AI systems used across multiple geographic regions?<\/b><\/h3>\n
                                    \n
                                  1. <\/b> Apply one regional requirement without considering other jurisdictions<\/span><\/li>\n
                                  2. <\/b> Identify applicable jurisdictional requirements and establish consistent enterprise controls with documented regional variations where necessary<\/span><\/li>\n
                                  3. <\/b> Allow every region to operate without any enterprise oversight<\/span><\/li>\n
                                  4. <\/b> Ignore geographic differences because AI systems are digital<\/span><\/li>\n<\/ol>\n

                                    Correct Answer: 2. Identify applicable jurisdictional requirements and establish consistent enterprise controls with documented regional variations where necessary<\/b><\/p>\n

                                    Explanation:<\/b><\/p>\n

                                    Multiregional AI deployments may be subject to different legal, regulatory, contractual, and cultural requirements. A strong governance approach identifies applicable requirements for each relevant jurisdiction while maintaining enterprise-level consistency wherever possible. Regional variations should be documented so that teams understand why different controls or processes apply. This approach avoids two extremes: applying one rule without considering local obligations or allowing each region to operate completely independently. Governance should also consider where data is stored and processed, which users can access the system, and whether vendors or subcontractors operate across borders. A structured framework helps the organization maintain consistent accountability while adapting controls to legitimate jurisdiction-specific requirements.<\/span><\/p>\n

                                    Question 158. Which metric is most useful for monitoring whether AI governance actions are being completed as required?<\/b><\/h3>\n
                                      \n
                                    1. <\/b> Number of AI-related logos displayed internally<\/span><\/li>\n
                                    2. <\/b> Average length of AI system names<\/span><\/li>\n
                                    3. <\/b> Number of employees who have viewed an AI presentation<\/span><\/li>\n
                                    4. <\/b> Percentage of required governance actions completed on time, including assessments, reviews, remediation, and approvals<\/span><\/li>\n<\/ol>\n

                                      Correct Answer: 4. Percentage of required governance actions completed on time, including assessments, reviews, remediation, and approvals<\/b><\/p>\n

                                      Explanation:<\/b><\/p>\n

                                      Governance metrics should provide meaningful information about whether required controls and oversight activities are actually being performed. The percentage of required assessments, reviews, remediation actions, approvals, and other governance tasks completed on time can indicate whether the governance process is operating as intended. Additional measures can examine overdue high-risk actions, recurring exceptions, unresolved findings, review frequency, and control effectiveness. Simple activity counts, such as the number of presentations viewed, may indicate engagement but do not necessarily demonstrate effective governance. Metrics should be tied to defined requirements and interpreted in context. Reliable governance reporting allows management to identify weaknesses, allocate resources, and track whether corrective actions are progressing appropriately.<\/span><\/p>\n

                                      Question 159. What should management do when governance metrics reveal repeated overdue high-risk AI remediation actions?<\/b><\/h3>\n
                                        \n
                                      1. <\/b> Investigate the underlying causes, assign accountability, prioritize remediation, and escalate significant unresolved exposure<\/span><\/li>\n
                                      2. <\/b> Remove the overdue items from reporting<\/span><\/li>\n
                                      3. <\/b> Reduce the risk classification of affected systems<\/span><\/li>\n
                                      4. <\/b> Stop measuring remediation performance<\/span><\/li>\n<\/ol>\n

                                        Correct Answer: 1. Investigate the underlying causes, assign accountability, prioritize remediation, and escalate significant unresolved exposure<\/b><\/p>\n

                                        Explanation:<\/b><\/p>\n

                                        Repeated overdue high-risk remediation actions can indicate more than isolated administrative delays. They may reveal insufficient resources, unclear ownership, unrealistic deadlines, ineffective escalation, or weaknesses in the governance process itself. Management should investigate the underlying causes and ensure that each material action has a clearly accountable owner and an appropriate target date. High-risk issues should receive suitable priority, and significant unresolved exposure should be escalated through established governance channels. Removing items from reports or changing their classification would reduce visibility rather than address the risk. Trend analysis is particularly useful because repeated delays can identify systemic problems that require process improvement, additional resources, or stronger management oversight.<\/span><\/p>\n

                                        Question 160. Which characteristic best indicates that an AI governance program is becoming sustainable?<\/b><\/h3>\n
                                          \n
                                        1. <\/b> Governance activities occur only when auditors request evidence<\/span><\/li>\n
                                        2. <\/b> Policies exist but are not connected to operational processes<\/span><\/li>\n
                                        3. <\/b> Governance responsibilities, risk management, monitoring, training, assurance, and continuous improvement are integrated into normal AI operations<\/span><\/li>\n
                                        4. <\/b> All governance decisions are made by one individual<\/span><\/li>\n<\/ol>\n

                                          Correct Answer: 3. Governance responsibilities, risk management, monitoring, training, assurance, and continuous improvement are integrated into normal AI operations<\/b><\/p>\n

                                          Explanation:<\/b><\/p>\n

                                          A sustainable AI governance program is embedded into the organization’s normal operating model rather than treated as a temporary compliance exercise. Responsibilities should be clearly assigned, risk assessments should occur at appropriate lifecycle stages, controls should be monitored, employees should receive relevant training, and assurance activities should provide independent insight where appropriate. Governance should also support continuous improvement by using incidents, metrics, assessments, and lessons learned to refine policies and controls. A program that activates only during audits may produce documentation without creating effective day-to-day oversight. Sustainable governance therefore connects policy with operational processes and management decisions, enabling the organization to manage AI risks consistently as systems, business objectives, regulations, and external conditions evolve.<\/span><\/p>\n

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                                          View Full Isaca AAISM Exam Dumps and Practice Test Dumps   Question 141. Which activity best supports effective AI governance across an organization’s business units? Allow each business unit to establish unrelated AI requirements Centralize every AI decision with the development team Establish consistent enterprise principles while allowing appropriate business-unit responsibilities Require internal audit to […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16860"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=16860"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16860\/revisions"}],"predecessor-version":[{"id":16929,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/16860\/revisions\/16929"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=16860"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=16860"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=16860"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}