Executive Summary
Organizations have spent years developing third-party risk management (TPRM) programs to evaluate suppliers, assess cybersecurity controls, protect sensitive information, monitor compliance and manage operational dependencies.
Artificial Intelligence introduces a new dimension to that responsibility.
Organizations are procuring AI-enabled SaaS platforms, integrating foundation models through APIs, using third-party AI agents, embedding AI capabilities into business applications, and relying on external providers for model development, training data, hosting, inference and monitoring.
This creates a fundamental challenge for Enterprise GRC:
How do you govern risks that originate in technology you do not fully own, operate or understand?
A conventional vendor assessment may establish that a supplier has an acceptable security posture. It may not establish whether its AI system produces unreliable outputs, introduces discriminatory outcomes, retains sensitive prompts, changes behaviour after a model update, or depends on undocumented third-party components.
The challenge becomes even more complex when an organization uses an AI-enabled service without realizing that AI is involved at all.
The National Institute of Standards and Technology (NIST), through its AI Risk Management Framework, explicitly identifies third-party AI risks and supply-chain issues as areas requiring organizational policies, controls and contingency processes. Its guidance recognizes that externally supplied AI technologies can introduce risks that are difficult to assess because of complexity, limited transparency and potentially misaligned risk tolerances. NIST AI RMF 1.0
This makes third-party AI governance more than an extension of a traditional vendor questionnaire.
It requires organizations to connect AI governance, TPRM, cybersecurity, privacy, procurement, legal, business ownership and continuous monitoring into a coherent operating model.
In this article, we explore how to do that practically, including an original framework for assessing third-party AI risk throughout the supplier lifecycle.
The central principle is:
You may outsource the technology. You cannot automatically outsource accountability for the risks your organization accepts by using it.
1. The New Reality of Third-Party AI Risk
Consider a few scenarios.
A business team introduces a generative AI application to summarize customer interactions. The supplier updates its underlying model, and the application begins producing materially different outputs.
A software provider embeds a third-party foundation model into its platform. The organization has assessed the software vendor but has limited visibility into the model provider, data-processing arrangements or downstream dependencies.
A recruitment platform introduces an AI-assisted candidate-ranking feature. The organization's original assessment covered the platform's cybersecurity controls, but not the potential impact of automated recommendations on applicants.
An employee connects an external AI service to internal documents. The service's data-retention and training practices have not been reviewed, and the organization cannot establish whether sensitive information is being handled according to its requirements.
In each case, the supplier relationship may appear acceptable from a conventional TPRM perspective.
Yet the AI introduces additional questions:
What is the system intended to do?
Which model, data sources and external components does it depend on?
How does it behave under expected and unexpected conditions?
What information does the provider retain or process?
Can the provider change the system without notifying the customer?
What happens when the system produces harmful or materially incorrect outputs?
Can the organization obtain sufficient evidence to assess and manage the risk?
Who can suspend its use if the risk becomes unacceptable?
These are not merely technical questions. They affect operational resilience, regulatory compliance, customer trust, intellectual property, privacy, security and business accountability.
The third-party AI problem is therefore not simply whether the vendor is trustworthy. It is whether the organization understands and governs the risks of the AI capability it is consuming.
2. Why Traditional TPRM Is Necessary but Not Sufficient
Traditional third-party risk management remains essential.
Organizations should continue assessing suppliers for information security, privacy, business continuity, financial stability, regulatory compliance, subcontractor management and contractual obligations.
The issue is not that traditional TPRM is inadequate in general.
It is that AI introduces additional risk dimensions that may not be covered by an existing supplier assessment.
Consider the difference:
| Traditional TPRM focus | Additional AI-specific questions |
|---|---|
| Is the supplier's security program adequate? | Are the model, prompts, interfaces and AI-specific controls appropriately secured? |
| Does the supplier protect customer data? | How are prompts, outputs, embeddings and other AI-related data processed, retained and potentially reused? |
| Does the supplier meet availability commitments? | What happens if the model becomes unavailable, changes behaviour or produces unreliable outputs? |
| Does the supplier have change management? | Are model, version, data and capability changes identified, assessed and communicated? |
| Does the supplier comply with contractual requirements? | Do the contract and operating arrangements address AI-specific responsibilities, evidence and incident cooperation? |
| Does the supplier manage subcontractors? | Can the organization identify material downstream model, hosting, data and service dependencies? |
| Does the service meet business requirements? | Has the AI been evaluated for its intended use, limitations, foreseeable misuse and potential impact? |
| Does the supplier support incident response? | Can AI-related failures be investigated, explained sufficiently, escalated and remediated? |
The distinction is important.
A supplier can have a mature cybersecurity program and still provide an AI capability that is unsuitable for a particular business purpose.
Likewise, a capable AI model does not compensate for inadequate access controls, poor data handling or unclear contractual responsibilities.
AI risk assessment must complement—not replace—traditional third-party risk assessment.
3. The AI Supply Chain Is More Than One Vendor
One of the most significant blind spots in third-party AI governance is the assumption that the contracted supplier represents the entire risk boundary.
In reality, an AI-enabled service may depend on multiple organizations and technical components.
Consider a simplified AI supply chain:
Your organization
↓
AI-enabled business application
↓
Third-party model provider
↓
Cloud infrastructure and inference service
↓
External data, software and supporting service providers
The actual architecture may be more complicated. Some components may be operated by the primary supplier, while others are provided through subcontractors, APIs or embedded services.
Each dependency can introduce different risks.
For example:
A model provider may change the model or its operating characteristics.
A cloud provider may experience a regional outage.
A data provider may introduce inaccurate or improperly licensed information.
An embedded AI component may process data differently from what the organization expects.
A downstream service may introduce additional retention, access or security concerns.
A supplier may discontinue a model version on which a critical business process depends.
This is where fourth-party risk becomes particularly relevant.
Fourth-party risk refers to risk arising from a supplier's own suppliers and dependencies. In an AI context, this can include upstream model providers, cloud infrastructure, external datasets, evaluation services and other components that materially influence the capability being consumed.
However, organizations do not need complete visibility into every technical dependency for every use case.
They need risk-proportionate visibility into dependencies that could materially affect their business, their data, their obligations or the people affected by their AI systems.
NIST's AI RMF supports this approach. Its Govern function addresses third-party policies and supply-chain risks, while Map includes identifying risks associated with third-party AI technologies, software and data. NIST AI RMF Core
The practical question is not simply, "Who is our vendor?"
It is:
"Which external components materially influence the AI capability we depend on, and how would a failure or change in those components affect us?"
4. The AI Vendor Risk Classification Framework
Not every supplier using AI requires the same level of scrutiny.
A vendor using AI internally to improve its own administrative processes presents a different risk profile from a vendor supplying an AI system that evaluates employees, processes confidential information or supports safety-critical decisions.
Similarly, a general-purpose AI assistant used for low-risk brainstorming should not automatically receive the same assessment burden as an AI agent authorized to access sensitive systems and execute business transactions.
A practical AI vendor risk classification should consider at least six dimensions.
1. Business criticality
How dependent is the organization on the AI capability?
Could an outage or withdrawal materially disrupt business operations?
2. Data sensitivity
Will the supplier process confidential business information, personal data, regulated information, source code, intellectual property or customer records?
3. Decision impact
Could the AI influence decisions affecting employees, customers, financial outcomes, safety, access to services or other significant interests?
4. Technical autonomy
Does the AI merely provide suggestions, or can it initiate actions, call tools, access systems and execute workflows?
5. Transparency and control
Can the organization understand the intended use, limitations, material changes and operating characteristics of the system sufficiently to manage the risk?
6. Dependency and substitutability
Can the organization replace the provider or revert to a safe alternative if the service becomes unavailable or unacceptable?
These dimensions should be considered alongside traditional supplier risk factors, contractual requirements and applicable regulations.
A resulting classification could look like this:
| Risk tier | Illustrative scenario | Governance approach |
|---|---|---|
| Low | AI feature used for non-sensitive, low-impact productivity tasks | Standard supplier controls with proportionate AI-use restrictions |
| Moderate | AI service processing internal business information | AI-specific due diligence, data-handling review and defined monitoring |
| High | AI supporting material customer, employee or business decisions | Enhanced assessment, documented validation, human oversight where appropriate, contractual safeguards and ongoing assurance |
| Critical | AI agent with privileged access or authority to execute high-impact actions | Executive risk review, strong technical restrictions, tested fallback arrangements, explicit decision rights and continuous risk monitoring |
These tiers are illustrative, not universal regulatory categories.
Organizations should define their own thresholds and escalation criteria based on risk appetite, use cases, business impact and applicable obligations.
The governing principle is proportionality: the higher the potential impact, the stronger the evidence and oversight should be.
5. The Third-Party AI Due Diligence Framework
Traditional vendor questionnaires often concentrate on whether controls exist.
AI due diligence needs to go further by evaluating the capability being acquired, how it will be used, and whether the organization can govern it throughout its lifecycle.
I propose a practical Third-Party AI Due Diligence Framework built around eight assessment domains.
Domain 1 — AI capability and intended use
Establish what the supplier is actually providing.
Questions include:
Is AI embedded in the service, or is it an optional feature?
Which business functions will use it?
Is the system generative, predictive, classificatory or agentic?
What uses does the provider support or prohibit?
What are the documented limitations?
Is the proposed use consistent with the supplier's intended-use guidance?
A vendor's general claim that its product uses "responsible AI" is not sufficient evidence of suitability for a particular deployment.
Domain 2 — Data governance and privacy
Understand the full data-processing lifecycle.
Assess:
What information is sent to the AI service?
Are prompts and outputs retained?
Can customer information be used to train or improve models?
Where is data processed and stored?
Are retention and deletion commitments documented?
How are personal data and confidential information protected?
Are cross-border transfers or additional subprocessors involved?
Can the organization obtain evidence of relevant data-handling commitments?
Contractual promises should be validated against the technical and operational arrangements wherever feasible.
Domain 3 — Model and output reliability
Evaluate the system against its intended use.
Questions may include:
Has the supplier documented relevant performance limitations?
Are evaluations available for the proposed use case?
How are accuracy, reliability and harmful outputs assessed?
What testing is performed for bias or inappropriate outcomes where relevant?
Are known failure modes documented?
How are significant performance changes communicated?
Can the organization conduct its own testing?
A model's published benchmark performance does not automatically demonstrate suitability for a specific enterprise workflow.
Domain 4 — Cybersecurity and access controls
Assess security across the complete AI integration.
This may include:
Authentication and authorization
Least-privilege access
API key management
Encryption
Network restrictions
Logging and monitoring
Vulnerability management
Secure development
Prompt-injection and tool-abuse risks
Data-exfiltration pathways
Segregation of environments
Controls over connected systems and tools
For AI agents, the assessment should examine not only what the model can generate, but what the surrounding application permits it to do.
Domain 5 — Transparency and change management
Determine how much operational visibility the provider offers.
Assess whether the supplier can communicate material changes to:
Model versions
Underlying providers
Data-processing arrangements
Supported capabilities
Safety or security controls
System limitations
Service availability
Terms of use
Not every model update requires the same governance response. Organizations should establish change thresholds based on potential impact.
A change to a low-risk summarization feature may require routine review. A change to an AI system supporting consequential decisions may require renewed validation and approval.
Domain 6 — Regulatory, contractual and intellectual-property considerations
Determine which obligations apply to the organization, the provider and the intended use.
Review:
Applicable privacy and AI regulations
Intellectual-property and licensing conditions
Ownership and permitted use of inputs and outputs
Audit and evidence rights
Data-processing terms
Incident notification
Cooperation with investigations
Restrictions on subcontracting
Records and documentation
Customer and regulatory support
Regulatory responsibility depends on the specific law, the parties' roles and the system's intended purpose. A contract cannot simply transfer away obligations that the law places on an organization.
Domain 7 — Incident management and resilience
Understand how the supplier handles failure.
Questions include:
How are AI-specific incidents defined and reported?
Will the supplier notify the organization of material incidents and relevant changes?
Can the supplier provide logs and other evidence needed for investigation?
Are incident responsibilities and escalation paths documented?
Can the AI capability be disabled independently?
Is there a fallback process?
What happens if the model or provider is withdrawn?
Can the business continue operating safely without the AI capability?
For material AI dependencies, resilience planning should address both service availability and the possibility that the system remains available but becomes unsafe or unsuitable.
Domain 8 — Ongoing assurance
Finally, establish how the supplier's risk profile will be monitored.
Possible mechanisms include:
Periodic reassessment
Material-change notifications
AI control attestations
Targeted evidence reviews
Independent assessment reports
Performance and incident reporting
Subprocessor change notifications
Contractual reviews
Risk-based testing
A completed onboarding assessment is a starting point, not a permanent guarantee.
6. The Contractual Challenge: What Happens When the Model Changes?
AI services evolve faster than many traditional procurement and vendor-management processes.
Providers may update models, introduce new capabilities, modify retention practices, change subprocessors or retire previously supported versions.
An organization that assessed a service six months ago may be relying on a materially different capability today.
This creates a governance challenge that contracts and operating procedures should address.
For material AI services, organizations should consider negotiating terms covering:
Notification of material changes. Define which changes must be communicated, within what timeframe, and through which channel.
Data usage and retention. Specify permitted processing, retention, deletion, training use and relevant restrictions.
Evidence and assurance. Establish appropriate access to documentation, control evidence, assessment reports and other assurance materials.
Incident cooperation. Define notification, investigation, evidence preservation, remediation and escalation responsibilities.
Subcontractor transparency. Establish expectations for notifying the organization of material changes to relevant downstream dependencies.
Performance and service expectations. Include measurable requirements where appropriate, while recognizing that conventional availability commitments do not fully capture AI reliability.
Suspension and termination rights. Define circumstances in which the organization may restrict use, suspend the service or terminate the relationship.
Exit and portability. Address data retrieval, deletion, migration, alternative providers and continuity arrangements.
Not every supplier will agree to every requested clause. Some AI services, particularly standardized APIs, may offer limited negotiation flexibility.
In those situations, the organization should assess whether alternative controls, restricted use, architectural safeguards or a different supplier are necessary.
The objective is not to secure an ideal contract at any cost.
It is to understand which risks remain outside the organization's control, what evidence is available, and whether the residual risk is acceptable.
7. The Shared Responsibility Model for Third-Party AI
One of the most persistent mistakes in AI governance is assuming that the provider is responsible for everything related to the AI system.
Responsibility is usually distributed.
The provider may control model development, training methods, infrastructure and certain technical safeguards.
The customer may control the business purpose, input data, user permissions, deployment context, human oversight and decisions made using the outputs.
The exact division depends on the service architecture, contractual arrangements and applicable law.
A practical responsibility model might look like this:
| Activity | Provider's typical role | Customer organization's typical role |
|---|---|---|
| Model development | Develop and evaluate the supplied model or system | Assess suitability for the intended use |
| Data processing | Implement agreed processing and security arrangements | Determine permitted inputs and assess applicable obligations |
| Technical security | Secure provider-controlled components | Secure integrations, identities, access and customer-controlled systems |
| AI validation | Supply relevant documentation and test information | Conduct use-case-specific evaluation where necessary |
| Human oversight | Provide instructions and appropriate capabilities where applicable | Define operational oversight and decision authority |
| Monitoring | Monitor provider-controlled components and communicate material issues | Monitor the deployed use, outcomes and business risks |
| Incident response | Investigate provider-side issues and cooperate as agreed or required | Manage business impact, escalation and internal response |
| Regulatory compliance | Meet obligations applicable to the provider | Meet obligations applicable to the organization in its role |
| Exit | Support agreed transition and data-handling arrangements | Plan replacement, continuity and safe decommissioning |
This is an illustrative allocation, not a universal legal responsibility matrix.
The important distinction is between responsibility for performing an activity and accountability for the organization's decision to use the AI capability.
A supplier can be accountable for its contractual commitments while the customer remains responsible for deciding whether and how the capability should be used in its own business context.
8. Regulatory Perspective: The EU AI Act and the AI Value Chain
The EU AI Act illustrates why roles and responsibilities across the AI value chain matter.
The Act distinguishes between different actors, including providers and deployers, and establishes obligations based on the system, its intended use and the relevant role.
Article 25 addresses responsibilities along the AI value chain. Under specified circumstances, an organization that rebrands a high-risk AI system, substantially modifies it, or changes its intended purpose in a way that makes it high-risk may assume provider responsibilities. The exact conditions and exceptions matter. EU AI Act — Article 25
Article 26 establishes obligations for deployers of high-risk AI systems, including relevant requirements concerning use according to instructions, human oversight, monitoring, input data and incident escalation. EU AI Act — Article 26
These provisions demonstrate an important principle:
The party supplying the technology and the party deploying it may have different, complementary obligations.
Organizations should therefore avoid assuming that a supplier's compliance statement resolves their own regulatory responsibilities.
A practical assessment should establish:
What role does the organization occupy?
What role does the supplier occupy?
What is the AI system's intended purpose?
Is the system within the scope of the relevant legislation?
Which obligations apply to each party?
What evidence is required to demonstrate compliance?
How will responsibilities be coordinated in operation?
The EU AI Act is not applicable to every AI deployment in the same way, and not every AI-enabled supplier relationship creates a high-risk AI system. Applicability must be assessed against the relevant legal provisions and facts.
For organizations operating across jurisdictions, AI-related obligations should be mapped into the existing regulatory and third-party risk-management processes rather than managed as an isolated legal exercise.
9. Continuous Monitoring: From Vendor Reviews to AI Risk Signals
Traditional TPRM programs often rely on periodic reassessment.
That remains useful, but it may not be enough for a critical AI dependency whose behaviour, model version or upstream architecture can change between reviews.
A more mature approach combines periodic reviews with event-driven reassessment.
Examples of potential triggers include:
A material model or service change
A new AI capability being enabled
A change in data-retention or training practices
A new subprocessor or upstream model provider
A significant AI incident
A sustained deterioration in output quality
A security vulnerability affecting the integration
A change in regulatory applicability
A contract renewal or service expansion
A change in the business purpose or impact of the AI use case
Not every trigger should automatically suspend the service. It should initiate the appropriate review based on severity, impact and established escalation criteria.
Organizations can integrate these signals into their existing GRC platforms and TPRM workflows.
For example, a material provider change could trigger a reassessment task, route the issue to the business owner and relevant control functions, update the risk register, and require documented approval before the expanded capability is used.
This makes third-party AI risk management more responsive to the actual operating environment.
The goal is not to monitor every supplier continuously in the same way. It is to detect material changes before they become unmanaged business risks.
10. The Third-Party AI Governance Lifecycle
To translate these principles into practice, I propose an original framework for managing AI-related supplier risk across the relationship lifecycle.
Identify → Classify → Assess → Contract → Approve → Monitor → Reassess → Exit
1. Identify
Discover suppliers and services that provide or materially depend on AI capabilities.
Include embedded AI, external APIs, AI-enabled SaaS, foundation models and AI agents.
2. Classify
Determine the potential business impact, data sensitivity, decision significance, autonomy and dependency risk.
3. Assess
Conduct proportionate AI-specific due diligence alongside traditional cybersecurity, privacy, operational and compliance assessments.
4. Contract
Establish appropriate responsibilities, data-handling commitments, change notifications, evidence rights, incident cooperation and exit arrangements.
5. Approve
Obtain documented approval from the appropriate business and risk owners before the AI capability is deployed or materially expanded.
6. Monitor
Track relevant provider changes, incidents, performance signals, exceptions and contractual obligations.
7. Reassess
Revisit the risk when the system, supplier, use case, regulatory context or risk profile changes.
8. Exit
Ensure the organization can safely withdraw the capability, manage dependencies, retrieve or delete data as required, and maintain business continuity.
This lifecycle should connect directly to the AI inventory, enterprise risk register, third-party register, control framework, incident-management process and change-management workflow.
The framework is designed to complement existing TPRM practices—not create a parallel process for every AI-enabled supplier.
11. Integrating Third-Party AI Risk Into Enterprise GRC
The most effective approach is to integrate AI supplier risk into the organization's existing governance architecture.
A mature operating model should connect:
AI Inventory
Which AI capabilities are being used, including those provided by third parties?
Third-Party Register
Which suppliers provide, host, maintain or materially influence those capabilities?
AI Risk Register
What risks arise from the use case, the technology and its external dependencies?
Control Framework
Which controls are required, and who owns them?
Contract Repository
What commitments, restrictions, rights and obligations govern the relationship?
Monitoring and Incident Management
What signals or incidents could trigger reassessment or escalation?
Issue and Remediation Management
How are identified weaknesses assigned, tracked and validated?
Executive Reporting
Which material dependencies, unresolved risks and exceptions require management attention?
This integration creates a more complete view of risk.
For example, a critical AI service could be linked to its business owner, supplier, underlying model provider where known, data classification, risk rating, contractual commitments, monitoring requirements, open issues and exit strategy.
Leadership would then be able to understand not only whether the vendor has passed an assessment, but also which business capabilities depend on that supplier, what risks remain, and what happens if the relationship fails.
This is where AI governance becomes a practical extension of Enterprise GRC.
12. What Should the Board and Executive Leadership Ask?
Boards do not need a technical inventory of every foundation model.
They do need a credible view of material AI dependencies and the risks they introduce.
A focused executive discussion could include the following questions.
Visibility: Do we know which material AI capabilities are supplied by external providers?
Concentration: Are critical business processes dependent on a small number of AI or cloud providers?
Data: Do we understand how suppliers process, retain and potentially reuse our information?
Assurance: What evidence do we have that material third-party AI controls are operating effectively?
Change: How would we learn that an important model, capability or underlying provider had changed?
Accountability: Who owns the business risk when a third-party AI system produces an unacceptable outcome?
Resilience: Can we continue operating safely if a critical AI provider becomes unavailable or must be suspended?
Exit: Can we replace or withdraw a critical AI capability without unacceptable disruption?
Regulatory exposure: Have we identified the obligations that apply to our use of externally supplied AI?
Residual risk: Which material risks remain because the supplier cannot provide sufficient transparency, evidence or contractual assurance?
These questions move the discussion beyond vendor onboarding statistics.
They help leadership understand the organization's dependency, exposure, resilience and ability to exercise control.
13. A Practical Starting Point for GRC and TPRM Leaders
Organizations do not need to redesign their entire supplier-risk program to begin addressing third-party AI risk.
A pragmatic starting sequence is:
Step 1 — Establish visibility. Identify known AI-enabled suppliers, external AI APIs, embedded AI features and material model dependencies.
Step 2 — Prioritize by risk. Start with AI capabilities that process sensitive information, influence consequential decisions, support critical operations or exercise meaningful autonomy.
Step 3 — Extend existing assessments. Add targeted AI questions to the current TPRM process instead of creating a disconnected questionnaire.
Step 4 — Define minimum evidence. Determine what documentation, test results, data-handling commitments and monitoring information are required for each risk tier.
Step 5 — Clarify responsibilities. Document which activities belong to the supplier, which belong to the organization, and who owns the residual risk.
Step 6 — Strengthen contracts and approvals. Address material change, incident cooperation, evidence access, data usage, termination and exit where appropriate.
Step 7 — Introduce event-driven reassessment. Establish triggers for material provider, model, data, use-case and regulatory changes.
Step 8 — Integrate reporting. Connect third-party AI risk to the AI inventory, risk register, issues, incidents and executive dashboards.
Step 9 — Test the exit plan. For critical dependencies, validate that the organization can restrict or withdraw the AI capability and continue operating safely.
The objective is not to eliminate every uncertainty associated with third-party AI.
It is to ensure that uncertainty is understood, managed, monitored and accepted by the appropriate authority.
Final Thoughts
Third-party risk management has always been about understanding the risks an organization inherits through its business relationships.
AI makes that responsibility more complex because the supplier relationship may include models, data, infrastructure, APIs, autonomous capabilities and dependencies that change over time.
A conventional supplier assessment remains necessary, but it cannot answer every question about an AI capability.
Organizations need to understand what they are consuming, how it affects their business, what information it processes, which external dependencies matter, what evidence is available, and how the organization will respond when something changes.
Most importantly, they need to distinguish between the supplier's responsibility for delivering a service and their own responsibility for deciding how that service is used.
The strongest AI governance programs will not treat third-party AI as an isolated procurement concern. They will connect it to Enterprise GRC, security, privacy, operational resilience, legal oversight and business accountability.
Because the ultimate question is not whether the supplier has a reassuring questionnaire response.
It is whether the organization has sufficient visibility, evidence and control to manage the risks of the AI capability it depends on.
You may not own the model. You may not control every dependency. But you must understand the risks you are accepting—and know what you will do when those risks change.
Technology may accelerate innovation. Trust determines whether that innovation endures.
Looking Ahead
In the next instalment of GRC Insights | Volume I – Responsible AI Governance, we will bring these governance principles closer to the executive decision-making table.
We have explored AI data stewardship, risk identification, maturity, measurement, incident response, assurance and third-party dependencies.
The next question is how these elements come together at the highest level of the organization.
Who owns AI risk at the enterprise level, what should the Board oversee, and how can executives distinguish responsible AI adoption from uncontrolled experimentation?
Next: AI Governance in the Boardroom — From Executive Accountability to Strategic Oversight
Because responsible AI governance cannot succeed as a technology initiative alone. It needs clear accountability, informed oversight and decisions aligned with the organization's risk appetite and business objectives.
Sources & Further Reading
1. NIST — Artificial Intelligence Risk Management Framework (AI RMF 1.0)
A foundational reference for managing AI risks, including third-party entities, supply-chain risks and external AI components.
2. NIST — AI RMF Core
Describes the Govern, Map, Measure and Manage functions. The Core includes specific outcomes addressing third-party AI technologies, software, data and supply-chain risks.
3. NIST — AI RMF Playbook
Practical guidance for organizations implementing the AI RMF, including suggested actions related to governance, risk mapping, measurement and management.
4. ISO/IEC 42001:2023 — Artificial Intelligence Management Systems
An international standard specifying requirements for establishing, implementing, maintaining and continually improving an Artificial Intelligence Management System.
5. European Union — Artificial Intelligence Act, Article 25: Responsibilities Along the AI Value Chain
Relevant to understanding how provider responsibilities can arise or change under specified circumstances involving high-risk AI systems.
6. European Union — Artificial Intelligence Act, Article 26: Obligations of Deployers of High-Risk AI Systems
Addresses specified obligations of deployers, including operational use, human oversight, monitoring and relevant incident escalation.
Important note
The Third-Party AI Due Diligence Framework, AI Vendor Risk Classification Framework and Third-Party AI Governance Lifecycle presented in this article are original practical models developed for the GRC Insights | Responsible AI Governance series. They are not official NIST, ISO/IEC or regulatory frameworks.
This article is intended for general governance and risk-management education, not legal advice. Regulatory applicability depends on the relevant jurisdiction, AI system, intended use and roles of the parties involved.
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