When and How to Assess AI Vendors through TPRM (Third-Party Risk Management) under the EU AI Act

What is TPRM and why it's essential for AI vendors under the EU AI Act in 2026

Third-Party Risk Management (TPRM) represents a structured approach to identify, assess, and mitigate risks associated with external vendors and partners. In 2026, with the explosion of artificial intelligence use in business operations, this practice has become absolutely critical for organizations that depend on third-party AI solutions.

When a company contracts an AI vendor for data processing, predictive analysis, or process automation, it is essentially transferring part of its operations to an external entity. This creates a dependency chain that can expose the organization to:

Operational risks
Security vulnerabilities
Regulatory compliance issues
Reputational damage

The Unique Complexity of AI Systems

The complexity of AI systems amplifies these challenges. Unlike traditional software, AI algorithms can exhibit unpredictable behaviors, unintentional biases, and specific vulnerabilities that require specialized assessment.

In 2026, regulations like the European AI Act and similar frameworks have made due diligence on AI vendors not just a best practice, but a legal obligation.

For companies that have not yet implemented robust TPRM processes for AI, the time to act is now. The question is not whether you will need to assess your AI vendors, but when and how to do it effectively.

Main risks from AI vendors that require TPRM assessment

AI vendors present unique risks that go far beyond traditional outsourcing challenges. In 2026, we observe that these risks have become even more complex with the accelerated evolution of artificial intelligence technologies.

Algorithmic Opacity

The first major risk is algorithmic opacity. Many AI vendors operate with proprietary models that function as "black boxes," making it impossible to fully understand how decisions are made. This creates compliance vulnerabilities, especially in regulated sectors like financial services and healthcare.

Data Security and Privacy Risks

Dependence on sensitive data represents another critical point. AI vendors frequently process large volumes of personal and corporate information to train and operate their models. Any failure in protecting this data can result in massive breaches and privacy violations.

Bias and Discrimination

Bias and discrimination risks also demand special attention. Poorly trained algorithms can perpetuate prejudices, generating unfair decisions that expose your company to lawsuits and significant reputational damage.

Technological Instability

Finally, technological instability is a growing concern. Trends in 2026 show that smaller AI vendors may face financial difficulties or sudden changes in their business models, leaving clients without critical support for essential operations.

When to implement TPRM assessment for AI vendors

The implementation of TPRM for AI vendors should follow specific criteria that consider both the risk level and operational impact. In 2026, the most mature organizations establish clear triggers for when to initiate these assessments.

Access to Sensitive Data

The first criterion is access to sensitive data. Whenever an AI vendor will process personal, financial, or strategic data, TPRM assessment becomes mandatory. This includes:

Chatbot systems that interact with customers
Predictive analysis platforms that use internal data
Automation tools that access critical systems

Process Criticality

Process criticality also determines the need for TPRM. Vendors whose solutions directly impact essential operations require rigorous assessment before implementation. Examples include:

Fraud detection systems
Automated decision-making platforms
Mission-critical operational tools

Contract Value and Duration

Contract value is another decisive factor. Contracts above a certain financial threshold, generally defined by the company's internal policy, automatically trigger the TPRM process. In 2026, many organizations set this limit at relatively low values for AI, recognizing the unique risks of this technology.

Finally, the duration and scope of the partnership influence timing. Long-term contracts or those involving deep integration with internal systems demand more detailed prior assessment, while pilot tests can follow simplified processes with subsequent review.

Structured framework for assessing AI vendors via TPRM under the EU AI Act

A structured TPRM framework for AI vendors should address five fundamental pillars:

1. Data Governance

The first pillar is data governance, where you assess how the vendor collects, stores, and processes information. Verify if there are clear privacy policies and if data is encrypted both in transit and at rest.

2. Algorithmic Transparency

The second pillar focuses on algorithmic transparency. Require documentation on how AI models make decisions, especially in cases that directly impact your business. In 2026, regulations like the European AI Act make this transparency even more critical for compliance.

3. Cybersecurity

The third pillar examines the vendor's cybersecurity. Request:

SOC 2 reports
ISO 27001 certifications
Evidence of regular penetration testing

AI vendors are attractive targets for cyberattacks due to the value of the data they process.

4. Operational Continuity

The fourth pillar evaluates operational continuity. Question about:

Redundancies
Disaster recovery plans
Guaranteed SLAs
Vendor's financial stability

The AI market still presents volatility that requires careful evaluation.

5. Regulatory Compliance

The fifth pillar analyzes regulatory compliance. Verify if the vendor meets the specific regulations of your sector and geography under the EU AI Act. Establish a quarterly review schedule to monitor changes and updates in risk controls.

Specific assessment criteria for AI vendors under the EU AI Act

Assessing AI vendors requires specific criteria that go beyond traditional third-party analyses. In 2026, organizations need to focus on unique aspects of artificial intelligence technology that can significantly impact operational and compliance risks under the EU AI Act.

Algorithmic Transparency

The first fundamental criterion is algorithmic transparency. Assess whether the vendor can explain how their models make decisions, especially in critical cases. Vendors that offer explainable AI demonstrate greater maturity and reduce risks of undetected bias.

Data Governance Standards

Data governance deserves special attention. Examine how the vendor:

Collects, processes, and stores training data
Implements clear policies about using proprietary data
Prevents sensitive information leaks between different clients

Model Robustness and Reliability

Also evaluate model robustness and reliability. Request:

Performance metrics
Error rates
Evidence of testing in adverse scenarios

A good vendor should demonstrate how their systems behave in situations not anticipated during training.

Update and Versioning Capabilities

Finally, consider update and versioning capabilities. In 2026, AI models evolve rapidly, so it's crucial that the vendor has structured processes to implement improvements without compromising operational stability. Verify if there are rollback capabilities and adequate regression testing.

TPRM tools and methodologies for AI vendors

Effective implementation of TPRM for AI vendors requires specialized tools that go beyond traditional third-party management solutions. In 2026, we observe significant evolution in platforms that integrate algorithmic risk assessment with compliance analysis.

Continuous Monitoring Platforms

Main tools include continuous monitoring platforms that use APIs to verify AI model performance in real-time. Solutions like MetricStream, ServiceNow, and Resolver have incorporated specific modules to assess:

Algorithmic transparency
Data drift
Biases in machine learning models

Assessment Methodologies

The most adopted methodology combines structured questionnaires with automated testing:

Questionnaires address data governance, algorithm explainability, and the vendor's internal audit processes
Automated tests verify output consistency, response time, and adherence to fairness standards

Risk Assessment Frameworks

Frameworks like the AI Risk Assessment Matrix (AIRAM) establish quantitative criteria to classify vendors into risk categories. This approach allows prioritizing due diligence resources on the most critical vendors.

SaaS Solutions for Smaller Organizations

For smaller organizations, SaaS solutions like Prevalent and BitSight offer specific templates for AI assessment, significantly reducing implementation time. Investment in adequate tools represents substantial savings compared to the costs of remediating incidents related to poorly assessed AI vendors.

Continuous monitoring and re-assessment of AI vendors

Continuous monitoring represents one of the most critical pillars of TPRM for AI vendors in 2026. Unlike traditional products, AI systems constantly evolve through algorithm updates, new training data, and performance adjustments, making systematic and proactive monitoring essential.

Performance Indicators and Alerts

Establish specific performance indicators for each vendor, including:

Accuracy metrics
Bias measurements
Response time
Availability rates

Configure automatic alerts for significant deviations in AI behavior patterns, such as sudden drops in accuracy or increases in processing time. These signals may indicate technical problems or even security compromises.

Re-assessment Triggers

Formal re-assessment should occur at least semi-annually, but some scenarios require immediate reviews:

Regulatory changes, such as new EU AI Act guidelines implemented in 2026
Significant changes in vendor algorithms
Security incidents in the market

Documentation and Communication

Document all interactions and changes in a centralized registry. Maintain regular communication with vendors through quarterly governance meetings, where technological roadmaps, compliance updates, and contingency plans are discussed.

This proactive approach allows anticipating risks and adjusting strategies before problems materialize into operational impacts.

Next steps to implement TPRM for AI vendors under the EU AI Act

Effective implementation of TPRM for AI vendors in 2026 requires a structured and gradual approach.

Initial Assessment and Mapping

Start by mapping all current AI vendors in your organization, categorizing them by:

Criticality level
Type of data processed

This initial inventory will be the basis for prioritizing assessment efforts.

Implementation Strategy

Establish a realistic implementation timeline, starting with the highest-risk vendors. Key steps include:

Develop AI-specific questionnaires that address algorithmic transparency, biases, data security, and regulatory compliance under the EU AI Act
Consider hiring AI specialists to support the most complex assessments
Invest in training procurement and compliance teams about AI-specific risks

Technology and Process Investment

Implement continuous monitoring tools that can detect changes in vendors' AI models. Regulations like the European AI Act make this knowledge essential for 2026.

Key Takeaway

Remember: TPRM for AI is not a one-time project, but a continuous process that evolves with technology. Start small, learn from each assessment, and gradually refine your criteria and processes.

Protecting your organization against AI risks begins with the first properly assessed vendor.