AI Vendor Risk Assessment

This assessment audits the Data, Model, and Output to expose hidden red flags and ensure your AI adoption is resilient, compliant, and secure.

Assessment by our team of Artificial Intelligence experts
Proven track record in identifying AI opportunities for businesses
Menno and Nick in a meeting room

How the Vendor Risk Assessment Works

At DataNorth, our Vendor Risk Assessment ensures your AI adoption is secure and compliant through a four-step methodology: we validate your specific security requirements and data sensitivity, identify the vendor’s technical architecture and sub-processor dependencies, perform a rigorous risk audit of their data lineage and model transparency, and provide a final consultation with a risk-adjusted roadmap.

Let’s unlock your organisations potential in becoming AI-first!

validate & research

Validate & Research

  • Interviews: Engaging with technical leads and business owners to define specific functional requirements.
  • Capability auditing: Assessing your internal team’s expertise and your current data infrastructure.

Identify & Map

  • Architectural mapping: Deconstructing the vendor’s tech stack to identify “Shadow AI” and hidden third-party sub-processors.

  • Requirement matching: Evaluating how a vendor’s model performance aligns with your specific operational and scalability goals.

identfiy & map
rigorous risk audit

Rigorous Risk Audit

  • Data lineage review: Investigating the source of training data to ensure it is free from PII and copyrighted material.

  • Model stress testing: Auditing the vendor’s “Red Teaming” results for bias, hallucinations, and resistance to prompt injection.

Final reporting & Consultation

  • Risk-adjusted roadmap: Providing a final breakdown of vendor viability vs. long-term compliance liabilities.

  • Implementation guidance: Delivering clear, actionable insights to ensure your chosen AI architecture supports sustainable growth.

final reporting and consultation

"The assessment sparked creative ideas, resulting in 55 actionable AI use cases."

Gielis Dijk

IT Manager @ Omrin

Gielis Dijk Omrin

Why Choose DataNorth?

We are the AI partner that empowers organizations like yours to harness the capabilities of Artificial Intelligence.

9+ Years of AI Experience

DataNorth has over 9 years of experience in the field of AI. By developing SaaS to fully custom AI solutions.

Highly Educated AI Experts

The AI Experts of DataNorth have at least a BSc. in AI. Besides doing assessments they develop custom AI solutions for our clients.

We give 100% Honest Advice

At DataNorth we’re non-biased, non-dependant and have no partnerships. This to make sure we give 100% honest advice.

Get Your Vendor Risk Assessment

We eliminate the guesswork, balancing rapid AI adoption against long-term compliance to ensure you invest in a transparent architecture built for sustainable growth.

€ 3.000

Also available in the USA at $3,300
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20-hour Consultancy Package

Our AI experts are available for 20 hours to address your questions

Experts available in Dutch, English, and German

Both on-location and digital options available

€ 15.000

Also available in the USA at $16,200
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Most Popular!

The DataNorth Vendor Risk Assessment

Within 2 weeks, receive an extensive report about your Vendor Risks

Receive a risk and compliance breakdown of your AI vendor ecosystem.

Gain insight into the data sovereignty, model transparency, and long-term security liabilities of your AI vendor landscape.

€ 2.900

Also available in the USA at $3,200
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AI Vendor Risk Training & Workshop

AI Vendor Risk training & workshop for 10 employees. Tailored to your organization

Both on-location and digital options available

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Frequently Asked Questions

  • How often should we update this Vendor Risk Assessment?

    AI models degrade and “drift” over time. We recommend a bi-annual review or a new assessment whenever the vendor pushes a “major” model update (e.g., moving from version 3.5 to 4.0), as the risk profile can shift overnight.

  • What is the "Human-in-the-Loop" (HITL) requirement?

    For high-stakes decisions like hiring, lending, or legal analysis—total AI autonomy is a liability. Our assessment identifies where a human must review or override an AI output to ensure accountability and prevent automated discrimination.

  • How do we verify if a vendor is "Retraining" on our data?

    Don’t rely on verbal promises. We look for “Zero Retention” clauses in the Data Processing Agreement (DPA) and technical confirmation of “Opt-Out” settings. If a vendor cannot provide a technical architecture diagram showing data isolation, it’s a red flag.

  • How do we assess a vendor that uses "Black Box" proprietary models?

    When a vendor (like OpenAI or Anthropic) won’t reveal their internal weights, we audit their Model Card and System Prompts. We look for documented safety benchmarks, third-party audit reports, and “Red Teaming” results to verify the model’s reliability without needing to see the secret sauce.

  • Do you have alternative Assessments services?