--- sourceDocument: Australia Governance, Risk, and Compliance sourceDocumentLink: https://www.servicenow.com/docs/r/australia/governance-risk-compliance Release : - australia ft:locale : - en-US ft:publication_title : - Australia Governance, Risk, and Compliance ft:clusterId : - grc bundleId : - grc workflow : - Technology --- # Offboard AI assets # Offboarding AI assets review {#ariaid-title1} Release version: Australia Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 5 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Offboarding AI assets review Offboarding AI assets---including systems, models, and datasets---addresses governance, risk, and compliance requirements through assessment, preparation, and retirement phases. This process ensures that organizations manage impact, residual risks, documentation, data handling, and audit traceability effectively before fully retiring AI assets. Offboarding responsibilities are shared between AI Risk and Compliance (AIRC) and AI Control Tower (AICT), where AIRC handles governance reviews and documentation, and AICT manages technical life cycle workflows. Show full answer Show less ## Governance Activities in AI Risk and Compliance The offboarding process begins with an offboarding request submitted by an AI asset owner or steward, which triggers governance reviews in the AI Risk and Compliance Workspace. Governance activities focus on: * Assessing the impact of retiring AI assets on users, business processes, and dependent systems * Confirming compliance with regulatory and policy requirements * Reviewing residual risks such as past decisions or bias * Documenting risk, impact, and compliance considerations These reviews ensure that all governance requirements are addressed before retirement, with no new ongoing monitoring introduced after offboarding completion. The status update to "retired" signifies completion once all reviews and issue resolutions are finalized. ## Life Cycle Tasks During Offboarding Offboarding includes a series of governance and technical tasks designed to prepare AI assets for retirement and ensure compliance: * **Assess:** Evaluate and justify retirement, identifying affected users and workflows to avoid disruptions. * **Confirm accountability:** Assign clear ownership and independent oversight for the offboarding process to meet compliance expectations. * **Pre-offboarding risk assessment:** Identify residual legal, ethical, and safety risks after AI shutdown. * **Capture documentation:** Preserve technical details, logs, and decision explanations for auditability and future accountability. * **Prepare users:** Communicate AI shutdown and ensure safe continuation of decisions or processes without the AI asset. * **Data management:** Decide on retention, deletion, or anonymization of data in compliance with privacy and legal standards. * **Disable access:** Technically prevent AI systems and models from running or influencing decisions post-retirement. * **Archive artifacts:** Retain models and metadata to maintain traceability for audits or investigations. * **Update inventory and close risks:** Confirm full retirement status and closure of high or critical risks to maintain audit-ready records. Roles such as AI asset owners and stewards have specific permissions to take assessments, move tasks through review states, and update statuses as part of these tasks. ## Practical Benefits for ServiceNow Customers Implementing this structured offboarding process within ServiceNow's AI Risk and Compliance and AI Control Tower enables customers to: * Maintain stringent governance and compliance throughout the AI asset life cycle * Mitigate risks associated with retiring AI technologies effectively * Ensure transparency and accountability with preserved audit trails and documentation * Coordinate cross-team responsibilities efficiently with role-based task management * Safely transition business processes and data handling when AI assets are retired This approach supports regulatory requirements and internal policies, helping organizations retire AI assets confidently without operational disruption or compliance gaps. Offboarding AI systems, models, and datasets addresses governance, risk, and compliance requirements throughout assessment, preparation, and retirement. Activities include impact evaluation, residual risk management, documentation preservation, data-handling decisions, and audit traceability. ## Offboarding responsibilities across AI Risk and Compliance and AI Control Tower {#airc-offboarding-ai-assets__section_gh3_wvk_gjc} Offboarding AI system, models, and datasets involves both governance activities and technical life cycle actions. These responsibilities are typically shared between AI Risk and Compliance (AIRC) and AI Control Tower (AICT), with each product addressing different aspects of the offboarding process. During offboarding, governance review may include assessing how retiring an AI asset affects users, business processes, and dependent systems. The review also determines whether continued use, reuse, or retirement of related models or datasets is appropriate. Additionally, assessments performed during offboarding confirm that governance requirements associated with the AI asset are fully addressed before retirement. These assessments do not introduce new ongoing monitoring or post‑retirement requirements. AI Risk and Compliance is used to perform and document governance reviews associated with offboarding. AI Control Tower can be used to manage technical life-cycle workflows for AI assets based on governance outcomes. Offboarding is initiated when an AI asset owner (sn_ai_asset_mgmt.ai_asset_owner) or steward (sn_ai_governance.ai_steward) submits an offboarding request in the control, which then drives governance review activities in the AI Risk and Compliance Workspace. For more information, see [Create offboarding requests for AI assets](https://www.servicenow.com/docs/access?context=create-ai-asset-offboarding-request&version=australia&pubname=australia-intelligent-experiences&ft:locale=en-US). ## Governance activities in AI Risk and Compliance during offboarding {#airc-offboarding-ai-assets__section_hh3_wvk_gjc} AI Risk and Compliance supports the governance aspects of AI asset offboarding. Through AIRC, organizations review and document risk, impact, and compliance considerations related to retiring AI models or datasets. When AI models or datasets servers are associated with a governed AI system, offboarding activities may be reviewed and tracked at the AI system level. This helps ensure complete governance coverage. You can view AI assets and their governance status on the Operations tab in the AI Risk and Compliance Workspace. For more information, see [Operations tab](https://www.servicenow.com/docs/nuQ07Q5u8A3nSqv20t67EQ "The Operations tab on the AI Risk and Compliance workspace provides an overview of AI systems by state, along with metrics for assessments, control assurance, issues, policy exceptions, and AI cases."). Figure 1. Operations tab For more information about the AI governance life cycle and how offboarding fits into the broader AI asset life cycle, see [AI governance life cycle](https://www.servicenow.com/docs/NGgy5rh6vyahb~eJihuSOw "Learn more about how organizations can govern AI from initial demand through deployment and ongoing governance monitoring by using the AI Control Tower and AI Risk and Compliance applications together.") and [AI asset lifecycle](https://www.servicenow.com/docs/access?context=ai-asset-lifecycle&version=australia&pubname=australia-intelligent-experiences&ft:locale=en-US). In AIRC, practitioners can perform activities such as reviewing regulatory risk classifications and completing or updating impact assessments. They can also conduct conformity or policy-alignment reviews as part of the AI life cycle. For more information, see [Perform impact assessment on an AI use case](https://www.servicenow.com/docs/EXhJaJXhdCUM_FIqPpFrOw "Perform an impact assessment of an AI use case to identify risks like copyright issues, bias, privacy breaches, misinformation, or surveillance, enabling improved oversight and risk management.") and [Initiate risk assessment on AI asset](https://www.servicenow.com/docs/39tz7ucci~8Nti9owhzwZw "Initiate risk assessment to enable the risk assessor to perform risk assessments on AI systems, evaluating the likelihood and impact of potential risks."). Offboarding is considered complete when required governance reviews are finished, related issues are resolved or formally accepted, and the AI asset life-cycle status is updated to retired. For more information about resolving governance issues before asset retirement, see [Remediate an issue in AI Risk and Compliance](https://www.servicenow.com/docs/8uHU7LrrJVwTnjptWNxO5A "After an issue has been created, reported, identified, triaged, and investigated, you can remediate it."). ## Life cycle tasks executed during AI asset offboarding {#airc-offboarding-ai-assets__offboarding-lifecycle-tasks} Governance life cycle tasks support review, preparation, and retirement of AI systems, models, and datasets during offboarding. These tasks help ensure governance requirements are addressed and documented before an AI asset is fully retired. {#airc-offboarding-ai-assets__table_qhc_scj_fkc__entry__5} | Task name | Purpose | Associated action | Role-specific action | Applicable to AI asset | |-|-|-|-|-| | Assess ||||| | Decide and justify AI System retirement | Identifies users, workflows, and decisions that will be affected when the AI stops. To understand how turning off the AI will affect users, workflows, and decisions. This avoids disruption, unsafe manual workarounds, or gaps in critical decision-making processes. | * Take assessment * View assessment {#airc-offboarding-ai-assets__ul_cy3_l3j_fkc} | * AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\]: Can take assessments * AI Steward \[sn_ai_governance.ai_steward\]: Can view response of the assessment {#airc-offboarding-ai-assets__ul_l1d_p3j_fkc} | * AI system * AI model * Dataset {#airc-offboarding-ai-assets__ul_kjp_s2j_fkc} | | Confirm accountability and oversight | Ensures clear ownership and independent oversight for the offboarding process. To clearly assign responsibility and independent oversight for the offboarding process. This ensures nothing is missed and that compliance expectations are met. | * Accept Work * Ready for review {#airc-offboarding-ai-assets__ul_h4l_w3j_fkc} | AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\]: * Can move the task to In progress state * Can move task to review state {#airc-offboarding-ai-assets__ul_izt_3nj_fkc} | * AI system * AI model * Dataset {#airc-offboarding-ai-assets__ul_qv5_s2j_fkc} | | Pre-offboarding ||||| | Assess residual legal, ethical, and safety risks | Identifies risks that may still exist after the AI is turned off (e.g., past decisions, bias). To identify risks that may still exist after the AI is turned off, such as past decisions, bias impacts, or legal exposure. This ensures risks are documented and managed, not ignored. | - | AI Risk \& Compliance Analyst \[sn_grc_ai_gov.ai_risk_and_compliance_analyst\]: Can update the state of the record | * AI system * AI model * Dataset {#airc-offboarding-ai-assets__ul_vgz_s2j_fkc} | | Capture final technical and decision documentation | Preserves explanations, logs, and documentation for audits or investigations. To preserve explanations, logs, and documentation showing how the AI worked. This supports audits, investigations, and future accountability even after the AI is gone. | * Accept Work * Ready for review {#airc-offboarding-ai-assets__ul_hnc_vjj_fkc} | AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\]: * Can move the task to In progress state * Can move task to review state {#airc-offboarding-ai-assets__ul_lc3_wjj_fkc} | * AI system * AI model * Dataset {#airc-offboarding-ai-assets__ul_fx3_t2j_fkc} | | Prepare users and teams for AI shutdown | Ensures people know the AI is going away and how decisions will continue safely. To ensure people understand the AI is being retired and how decisions or processes will continue safely without it. | * Accept Work * Ready for review {#airc-offboarding-ai-assets__ul_og4_lkj_fkc} | AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\]: * Can move the task to In progress state * Can move task to review state {#airc-offboarding-ai-assets__ul_l22_mkj_fkc} | * AI system * AI model * Dataset {#airc-offboarding-ai-assets__ul_opp_t2j_fkc} | | Prepare data retention, deletion, or anonymization plan | Ensures data is kept or deleted in line with privacy and legal requirements. To decide what data must be kept, deleted, or anonymized after retirement. This prevents unlawful data retention and reduces privacy and misuse risks. | * Accept Work * Ready for review {#airc-offboarding-ai-assets__ul_xws_nkj_fkc} | AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\]: * Can move the task to In progress state * Can move task to review state {#airc-offboarding-ai-assets__ul_pf2_4kj_fkc} | Dataset | | Offboard ||||| | Disable AI system and model access | Ensures the AI can no longer run, respond, or influence decisions. To technically ensure the AI can no longer run, respond, or influence decisions. This prevents accidental or unauthorized continued use. | * Accept Work * Ready for review {#airc-offboarding-ai-assets__ul_jnw_1lj_fkc} | AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\]: * Can move the task to In progress state * Can move task to review state {#airc-offboarding-ai-assets__ul_rw4_blj_fkc} | * AI system * AI model * Dataset {#airc-offboarding-ai-assets__ul_vh2_52j_fkc} | | Archive AI artifacts and metadata | Preserves traceability of how the AI worked in the past. To retain models and records needed to explain past AI behavior to regulators, auditors, or internal reviewers. | * Accept Work * Ready for review {#airc-offboarding-ai-assets__ul_fjz_flj_fkc} | AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\]: * Can move the task to In progress state * Can move task to review state {#airc-offboarding-ai-assets__ul_iyb_hlj_fkc} | AI model | | Update AI inventory and formally close risks (If High/Critical/unacceptable risk classification) | Confirms the AI is fully retired, risks are closed, and records are audit-ready. To confirm the AI is fully retired, risks are closed, and organizational records accurately reflect its end of life. | * Take assessment * View assessment {#airc-offboarding-ai-assets__ul_or1_mlj_fkc} | * AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\]: Can take assessments * AI Steward \[sn_ai_governance.ai_steward\]: Can view response of the assessment {#airc-offboarding-ai-assets__ul_vwz_mlj_fkc} | * AI system * AI model * Dataset {#airc-offboarding-ai-assets__ul_hst_52j_fkc} | [Table 1. AI asset offboarding tasks] {#airc-offboarding-ai-assets__table_qhc_scj_fkc} **Related concepts** * [AI governance life cycle](https://www.servicenow.com/docs/NGgy5rh6vyahb~eJihuSOw "Learn more about how organizations can govern AI from initial demand through deployment and ongoing governance monitoring by using the AI Control Tower and AI Risk and Compliance applications together.") **Related tasks** * [Remediate an issue in AI Risk and Compliance](https://www.servicenow.com/docs/8uHU7LrrJVwTnjptWNxO5A "After an issue has been created, reported, identified, triaged, and investigated, you can remediate it.") * [Perform impact assessment on an AI use case](https://www.servicenow.com/docs/EXhJaJXhdCUM_FIqPpFrOw "Perform an impact assessment of an AI use case to identify risks like copyright issues, bias, privacy breaches, misinformation, or surveillance, enabling improved oversight and risk management.")