In this article
- Why Does AI Vendor Risk Management Matter Under the EU AI Act?
- AI Vendor Risk Management Software: Are You a Provider or a Deployer?
- What Does the EU AI Act Require From AI Vendors and Their Customers?
- Article 25(4): Written Agreements and Technical Cooperation
- Article 53(1)(b): General-Purpose AI Model Documentation
- Article 26: Deployer Responsibilities
- Article 17: Quality Management Systems
- How Can AI Compliance Software Support Vendor Risk Assessment?
- What Do ISO 42001 and ISO 27001 Add to AI Supplier Management?
- ISO/IEC 42001:2023 – AI Management System Controls
- ISO/IEC 27001:2022 – Information Security Supplier Controls
- How Should Organisations Classify and Prioritise AI Vendors?
- What Should an AI Vendor Due-Diligence Checklist Include?
- Technical and Regulatory Assessment
- Security and Privacy Assessment
- Contract and Governance Requirements
- How Should Organisations Manage AI Vendor Compliance Evidence?
- Four AI Vendor Risk Management Mistakes to Avoid
- How AnnexOps AI Compliance Software Helps Manage AI Vendor Governance
- Related Reading
- Explore AI Compliance Software for EU AI Act and GDPR Governance
- Author: Nitin Grover
AI vendors are becoming part of everyday product development. SaaS companies integrate third-party language models into applications, developers use external AI APIs, and enterprises introduce AI-powered features through existing business software. These integrations accelerate development, but they also introduce regulatory, operational, and data protection responsibilities.
Under the EU AI Act, outsourcing AI development does not automatically transfer legal accountability to the vendor. An organisation’s obligations depend on its role in the AI value chain, how it uses the system, and whether it qualifies as a provider or deployer.
AI vendor risk management therefore requires more than a supplier questionnaire. Organisations need visibility into their AI systems, a structured risk assessment process, access to technical documentation and reliable compliance evidence.
AI compliance software helps connect these activities within a central governance workflow. AnnexOps supports AI product teams, developers and compliance professionals by bringing AI system discovery, regulatory risk classification, obligation mapping, documentation management and evidence tracking together.
This guide explains how to determine your regulatory role, understand what Regulation (EU) 2024/1689 requires when working with AI vendors, and apply ISO 42001 and ISO 27001 controls to third-party AI relationships. It also includes a practical AI vendor due diligence and contract checklist.
For more detailed guidance, explore these related resources:
- ISO 27001 and ISO 42001: How to Integrate Both Standards
- Provider vs Deployer Under the EU AI Act: Which Are You?
- AI Vendor Due Diligence Questionnaire: What to Ask
- ISO 42001 Annex A Controls Explained
Managing AI vendors across multiple teams?
AnnexOps AI compliance software maintains a central AI system inventory and connects supporting evidence with shared controls. This helps organisations reuse relevant evidence across multiple frameworks instead of managing separate compliance records.
Why Does AI Vendor Risk Management Matter Under the EU AI Act?
Most organisations do not develop every AI capability internally. They may integrate a third-party general-purpose AI model, purchase an AI-powered recruitment tool or introduce an external chatbot into their customer support infrastructure.
Each integration can create dependencies involving model performance, personal data, technical documentation, security and regulatory responsibilities.
The EU AI Act addresses different responsibilities across the AI value chain. An organisation that develops an AI system using an external model may have different obligations from one that simply deploys a finished third-party solution.
The regulatory timeline also makes early preparation important:
| Regulatory milestone | Application date |
| Prohibited AI practices and AI literacy provisions | 2 February 2025 |
| GPAI model obligations | 2 August 2025 |
| General application and relevant enforcement provisions | 2 August 2026 |
| High-risk AI systems under Annex III | 2 December 2027 |
| High-risk AI systems under Annex I | 2 August 2028 |
The high-risk application dates reflect Regulation (EU) 2026/1744, the Digital Omnibus on AI, which entered into force on 27 July 2026.
These extended timelines do not eliminate the need to understand AI supply chains. Organisations still need to identify the systems they use, determine their regulatory roles and establish processes for managing documentation and supplier changes.
An EU AI Act compliance platform can help organisations coordinate these activities before the applicable obligations take effect.
AI Vendor Risk Management Software: Are You a Provider or a Deployer?
Choosing the right AI compliance management software starts with understanding the organisation’s regulatory role.
Under Article 3 of the EU AI Act, a provider develops an AI system or has one developed and places it on the market or puts it into service under its own name or trademark. A deployer uses an AI system under its authority for professional activities.
An organisation can hold different roles across its AI portfolio. Integrating a third-party model into a commercial product does not automatically make the organisation only a deployer.
| Business situation | Likely role | Regulatory consideration |
| Using a vendor’s finished AI tool as intended | Deployer | Deployer obligations may apply, particularly for high-risk systems |
| Building a commercial AI application using an external GPAI model | Provider of the AI system | The organisation has provider responsibilities for its own system |
| Placing an existing high-risk system on the market under its own name | Provider | Article 25 may transfer provider responsibilities |
| Substantially modifying a high-risk AI system | Potentially provider | Article 25 applies where the relevant conditions are met |
| Changing the intended purpose of a system so that it becomes high-risk | Provider | New provider obligations may arise under Article 25 |
The precise classification depends on the circumstances and the conditions in Article 25.
For technology companies, this distinction is important when building products around external models. A vendor’s documentation and certification do not automatically satisfy the downstream provider’s obligations.
An AI risk classification tool can help maintain visibility into systems and their regulatory context. Organisations should record the role they hold for each system in their AI inventory and reassess it when the system, deployment model or intended purpose changes.
What Does the EU AI Act Require From AI Vendors and Their Customers?
Several provisions are particularly relevant to AI vendor due diligence and third-party risk management.
Article 25(4): Written Agreements and Technical Cooperation
Article 25(4) addresses cooperation between providers of high-risk AI systems and third parties supplying AI systems, tools, services, components or processes used in or integrated into those systems.
The parties must establish a written agreement specifying the information, capabilities, technical access and assistance required for the provider to meet its obligations.
This requirement has an important practical consequence: AI suppliers must provide sufficient cooperation for downstream providers to manage their regulatory responsibilities.
The provision contains a specific exception for certain publicly available tools, services, processes and components released under free and open-source licences, excluding general-purpose AI models. Intellectual property, confidential business information and trade secrets must also be respected.
Article 53(1)(b): General-Purpose AI Model Documentation
Providers of general-purpose AI models must prepare and maintain technical documentation and make relevant information available to downstream providers intending to integrate their models.
Annex XII specifies the information required. It includes model capabilities, limitations, architecture, development information and integration requirements.
AI product teams should request and maintain this documentation as part of their AI vendor assessment process.
Certain free and open-source GPAI models benefit from the Article 53(2) exemption, subject to the conditions in that provision, including the exception for models presenting systemic risk.
Article 26: Deployer Responsibilities
Organisations deploying high-risk AI systems must take appropriate technical and organisational measures to use them according to the instructions for use.
Depending on the applicable requirements, deployers must also:
- Assign human oversight to people with the necessary competence, training and authority.
- Monitor system operation and address identified risks.
- Ensure input data under their control is relevant and sufficiently representative.
- Retain automatically generated logs under their control for at least six months, unless applicable Union or national law provides otherwise.
- Inform the provider and relevant authorities when the system presents a serious incident or risk, as required.
- Inform workers’ representatives and affected workers before workplace use of a high-risk AI system, where applicable.
Article 26 also addresses the use of relevant provider information in data protection impact assessments under the GDPR where applicable.
Article 17: Quality Management Systems
Providers of high-risk AI systems must establish and maintain a quality management system.
This system covers areas such as regulatory compliance, risk management, technical documentation, monitoring and corrective actions.
Third-party suppliers can form part of the processes that the provider needs to control. Vendor documentation, change notifications and technical cooperation therefore have an important role in the wider AI management system.
Together, these provisions show why AI vendor risk management needs a documented process rather than a one-time supplier approval.
How Can AI Compliance Software Support Vendor Risk Assessment?
AI vendor assessments often involve information from procurement, engineering, security, legal and compliance teams. When each department maintains separate records, organisations can lose visibility into which AI systems are in use and whether their documentation remains current.
An AI compliance software solution can connect system records, regulatory assessments and supporting evidence.
A structured AI vendor assessment workflow should include:
- AI system discovery: Identify internal AI applications, third-party models, external APIs and AI-enabled business tools.
- Regulatory role assessment: Determine whether the organisation acts as a provider, deployer or another relevant operator.
- Risk classification: Assess intended purpose, use case and applicable EU AI Act classification requirements.
- Vendor documentation review: Collect technical documentation, instructions for use, model information and relevant security evidence.
- Obligation mapping: Connect the system’s classification and organisational role with applicable regulatory requirements.
- Ongoing monitoring: Review changes to models, intended purposes, suppliers and deployment environments.
- Evidence management: Maintain assessment results, approvals, agreements, incident records and supporting documentation.
An EU AI Act compliance platform can help manage these activities through connected workflows instead of disconnected spreadsheets and document repositories.
For organisations also managing privacy obligations, connecting AI governance with GDPR processes can reduce duplicated documentation and make it easier to review the relationship between AI systems and personal data processing.
What Do ISO 42001 and ISO 27001 Add to AI Supplier Management?
Regulatory compliance and management-system certification serve different purposes. ISO standards can provide a structured approach to supplier governance, but they do not replace the EU AI Act.
ISO/IEC 42001:2023 – AI Management System Controls
Annex A.10 of ISO/IEC 42001 addresses third-party and customer relationships.
Its controls cover:
- A.10.2: Allocation of responsibilities.
- A.10.3: Suppliers.
- A.10.4: Customers.
These controls help organisations establish responsibilities across AI-related relationships and consider supplier services within their responsible AI management approach.
An AI governance software solution can help connect these requirements with supplier records, internal responsibilities and supporting evidence.
ISO/IEC 27001:2022 – Information Security Supplier Controls
ISO/IEC 27001:2022 includes controls 5.19 to 5.23, covering supplier relationships, supplier agreements, ICT supply chains, monitoring of supplier services and cloud service security.
These controls are particularly relevant when AI vendors process personal data, confidential business information or other sensitive organisational data.
Organisations with an existing Information Security Management System can use their supplier assessment processes as a foundation for AI vendor governance.
However, additional AI-specific questions remain necessary, including model provenance, intended purpose, technical limitations, human oversight and model changes.
An AI compliance management platform that supports multiple frameworks can help connect these requirements with shared evidence.
A vendor’s ISO 27001 or ISO 42001 certificate should be assessed according to its scope, validity, issuing certification body and covered services. Certification alone does not establish that a particular AI system complies with the EU AI Act.
How Should Organisations Classify and Prioritise AI Vendors?
Not every AI supplier presents the same level of risk. A practical vendor classification process helps organisations allocate assessment resources according to the system’s use, business importance and regulatory exposure.
The following model is a suggested operational approach, not a legal classification prescribed by the EU AI Act.
| Vendor category | Typical example | Suggested assessment |
| Critical | External model integrated into a commercial AI product or high-risk use case | Detailed technical assessment, documentation review, contractual safeguards and ongoing monitoring |
| Significant | AI software processing personal data or business-critical information | Security and privacy assessment, vendor questionnaire, contract review and periodic reassessment |
| Limited | Low-impact AI productivity tool with limited data access | Basic assessment, approved-tool listing and review when usage changes |
The classification should not replace the formal EU AI Act risk assessment. A commercially important vendor is not automatically a high-risk AI system, and a low-cost tool is not automatically low-risk.
Reassess vendors when they introduce new models, modify system functionality, change data processing arrangements or alter the intended purpose of their services.
What Should an AI Vendor Due-Diligence Checklist Include?
A structured AI vendor due-diligence process helps engineering, procurement and compliance teams collect relevant information before approving an AI integration.
Technical and Regulatory Assessment
- Which AI models, systems and external APIs does the vendor provide?
- What is the documented intended purpose?
- What role does the vendor hold under the EU AI Act?
- Has the vendor assessed the system’s regulatory classification?
- Which third-party models and sub-suppliers are involved?
- Can the vendor provide instructions for use and relevant technical documentation?
- For GPAI models, is the required Annex XII documentation available?
Security and Privacy Assessment
- Does the vendor process personal or confidential business data?
- What security controls protect data during storage and transmission?
- Does the vendor use customer data for model training?
- Where does the vendor process and store data?
- How does it manage security incidents and data breaches?
- What certifications or independent assurance reports can it provide?
Contract and Governance Requirements
- Have the responsibilities of each party been documented?
- Are relevant Article 25(4) agreements in place where required?
- Are model changes and intended-purpose changes communicated?
- Are incident notification procedures defined?
- Can the organisation access relevant logs and technical documentation?
- Are audit and evidence access rights addressed?
- Are subcontractors and underlying models disclosed where appropriate?
- Are exit, data deletion and transition arrangements documented?
Legal counsel should review contractual terms before execution. A due-diligence questionnaire supports the decision-making process but does not replace legal review.
How Should Organisations Manage AI Vendor Compliance Evidence?
AI vendor risk management does not end when a supplier passes an initial assessment. Organisations need to retain records that explain why a vendor was approved, what risks were identified and how those risks are monitored.
A central AI inventory and compliance evidence management solution can help maintain:
- AI vendor and system records.
- Provider and deployer role assessments.
- Risk classification results.
- Vendor questionnaires and assessment dates.
- Signed agreements and change notifications.
- Technical documentation and instructions for use.
- Security certifications and supporting reports.
- Monitoring records and incident documentation.
- Approval decisions and corrective actions.
Connecting this information with an AI compliance management platform can also make it easier to identify missing evidence and prepare for internal reviews or external audits.
The objective is not simply to collect more documents. It is to maintain traceability between AI systems, regulatory obligations, controls, and the evidence supporting governance decisions.
Four AI Vendor Risk Management Mistakes to Avoid
1. Assuming the vendor carries all regulatory responsibility.
An organisation’s obligations depend on its role under the EU AI Act. A supplier agreement cannot automatically remove statutory responsibilities.
2. Treating ISO certification as proof of AI Act compliance.
ISO 27001 and ISO 42001 provide management-system assurance within their respective scopes. They do not independently demonstrate that a specific AI system meets every applicable AI Act requirement.
3. Assessing vendors only once.
Models, APIs, data processing arrangements and intended purposes can change. Vendor assessments should include reassessment triggers and ongoing oversight.
4. Keeping third-party AI outside the AI inventory.
AI systems integrated through external APIs and business applications should not remain invisible to governance teams. Without an accurate inventory, organisations may overlook relevant risks and obligations.
How AnnexOps AI Compliance Software Helps Manage AI Vendor Governance
Managing AI vendor risk requires organisations to connect technical systems with regulatory responsibilities, risk assessments and supporting documentation.
AnnexOps is an AI compliance software solution designed for AI product teams, developers and compliance professionals managing EU AI Act and GDPR governance activities.
Rather than treating each compliance requirement as a separate documentation task, AnnexOps brings connected governance capabilities into one environment.
Its capabilities support AI vendor oversight through the following workflows:
AI System Inventory and Discovery
Maintain a structured inventory of AI systems, including applications that use third-party models and services. This gives governance teams a clearer view of their AI landscape.
AI Risk Classification
Use the AnnexOps Risk Classification Engine to assess AI systems against relevant EU AI Act requirements, including the eight Annex III high-risk categories. Classification decisions should be reviewed against the system’s actual intended purpose and applicable legal criteria.
Regulatory Obligation Mapping
Connect AI system roles and risk classifications with relevant regulatory obligations and controls. This helps teams understand which governance activities require attention.
Compliance Documentation Management
Generate and organise supporting compliance documentation, including relevant EU AI Act and GDPR records. This helps teams coordinate documentation across regulatory and management-system requirements.
Tamper-Evident Evidence Management
Maintain supporting records in the AnnexOps Evidence Vault. Its read-only, hash-chained evidence storage helps make unauthorised changes detectable and supports evidence traceability.
Multi-Framework Governance
The AnnexOps Common Control Library connects shared controls across frameworks. According to current product information, 50 of its 211 shared controls link to ISO 42001 requirements, helping organisations reuse relevant evidence across governance activities.
For organisations managing privacy and AI governance together, the AnnexOps DPO Command Center also supports coordination of GDPR and EU AI Act compliance activities.
AnnexOps helps organisations establish a more connected approach to AI governance, manage documentation and identify gaps across relevant frameworks. Its coverage indicators are intended to support compliance operations, not certify legal compliance or predict an audit outcome.
Related Reading
- AI Compliance Software
- EU AI Act Compliance Lifecycle
- GDPR and EU AI Act DPO Dashboard
- ISO 27001 and ISO 42001: How to Integrate Both Standards
- Provider vs Deployer Under the EU AI Act: Which Are You?
- AI Vendor Due Diligence Questionnaire: What to Ask
Explore AI Compliance Software for EU AI Act and GDPR Governance
Bring AI system inventory, risk classification, obligation mapping and compliance evidence into a connected governance workflow.
Author: Nitin Grover
Nitin Grover is an AI compliance strategist and writer focused on EU AI Act compliance, AI governance, Annex IV documentation, AI risk management, and AI compliance operations for AI startups, SaaS companies, and enterprise AI teams across Europe.
