Your legal team should verify several factors when approving an AI vendor. Strategic fit, data handling procedures, robustness, operational reliability, risk assessment, governance, compliance, whistleblower policy, anti-bribery policy, and ESG policy are major factors your legal team should consider. Companies like DataToBiz make it easier by openly sharing the required data and documents, ensuring transparency and accountability in their services. Here, we’ll discuss why due diligence is crucial when approving an AI vendor and the various aspects your legal team should verify before you sign the agreement.
With artificial intelligence becoming an essential part of most industries, every business, from a startup to a multinational enterprise, must adopt new technologies at various levels to streamline its operations. While you can build everything from scratch, it is highly expensive, time-consuming, and effort-intensive. A better alternative is to hire AI vendors and service providers.
However, you cannot hire any vendor without considering how the solutions would affect your business. One of the main factors is whether your data and systems will be safe and if the AI solutions adhere to global regulations.
Statistics show that 35.5% of breaches experienced by organizations happened with third-party access. It is a weak area that many cybercriminals target. Also, 75% of third-party breaches targeted the technology supply chain.
Such attacks doubled in 2024, indicating that the risks increased as more businesses implemented third-party solutions. These risks can be minimized by thoroughly assessing and verifying the AI vendor before integrating their solutions into your business operations. The legal team, C-suite, and other senior officers should run a comprehensive AI contract risk assessment before finalizing a service provider.
In this blog, we’ll look at the risks, reasons, and steps to consider when approving an AI vendor.
Why is the Risk of Hiring an AI Vendor Different from Traditional Risks?
Traditional risks are more about operational uptime/downtime, financial challenges (increasing expenses, hidden costs, etc.), and basic security concerns. However, the risks associated with AI technologies are wider and different. AI increases complexity in data and security compliance even though it streamlines and automates many operations.
According to TechAdvisors, only 0.1% of people can consistently identify deepfake content. At the same time, there has been a 2137% increase in deepfakes since 2022. The numbers are staggering. It shows how vigilant CEOs and CTOs have to be when introducing AI into their operations and training employees to discern between genuine and AI content.
When it comes to approving an AI vendor, the following are some differences in risk factors.
| Risk Factors | AI Vendor | Traditional Vendor |
|---|---|---|
| Data Privacy | Large and complex datasets, with increasing concerns about data exposure, misuse, or non-compliance. | Usually handle limited operational data and have clearer boundaries on usage. |
| Bias and Fairness | Concerns over bias and fairness are common when using AI models. | Human processes that also have bias, but in a different way. |
| Transparency | AI algorithm can be a black box where you don’t know how the decision-making process works. | Provide documentation for their procedures, though there will be some opacity. |
| Regulatory Compliance | Rapidly evolving AI regulations means you have to stay up to date at all times. | Established regulatory frameworks. |
| Technology Dependence | Heavy reliance on proprietary AI models or platforms can cause lock-in and limit flexibility. | Standardized tools and processes, reducing lock-in risk. |
| Security | More vulnerable to cyberattacks. The scope is also wide, making it vital to have a robust security system. | Mostly conventional cybersecurity risks. |
| Ethical Responsibility | Must address ethical concerns like surveillance, autonomy, and unintended consequences. | Limited to contractual and operational ethics. |
What is AI Vendor Due Diligence?
Legal due diligence of AI vendors is the process of reviewing and verifying if the products and services offered by the company comply with various industry-wide and global regulations. This includes privacy and security settings, operational and human-created risks, contractual constraints, financial obligations, and sustainability concerns.
The due diligence for approving AI vendors goes beyond a regular review. Questions about SOC 2 security, data encryption, privacy policy, access controls, backup and recovery, and data retention are common. For AI vendors, you should also include specific questions about the AI and ML models, the data used to train the algorithms, and more. A few such questions to include in your due diligence are listed below:
- Is the AI system the vendor’s proprietary model or is it built on a third-party open-source model?
- What data does the AI system process?
- Was sensitive data used in training the algorithms? If yes, how does it affect the outcomes?
- Was customer data used for training? If yes, are there permissions to use that data?
- Does the AI vendor support audit logs?
- Does the AI system influence decision-making, and how?
- How does the vendor deal with human oversight?
- Does the vendor support customer, audit, and regulatory requirements?
- Is the AI system’s decision-making process transparent, and can it be tracked?
However, it is important to note that AI vendor due diligence is not a one-time questionnaire. It is not a document you ask the vendor to fill and forget in one of the many files. Your legal team has to align this with your organization’s corporate governance framework, vendor management guidelines, privacy compliance program, and other processes. Additionally, due diligence should be an ongoing monitoring process, as unexpected issues may arise when the systems are in use.
What are the Steps for Approving an AI Vendor?
Every organization has its own AI vendor compliance framework and checklist for approval. However, the following steps are found in many of them.
Internal AI Requirements
The process starts with an employee or a team leader submitting a request for an AI tool. Create a detailed submission form to collect all requirements and have a clear idea of what is required, why AI can help, how it changes the workflow, etc.
Internal Evaluation
The submitted requests are processed to identify use cases that can genuinely benefit from AI integration and help the organization gain a competitive edge. Senior executives also start a search to find vendors who offer the required products and services.
Screening AI Vendors
Once you have a list of potential AI vendors, screen them based on your specifications. This could be budget, timeline, customizations, compatibility with existing systems, and so on. This is to filter and shortlist the best service providers.
Comprehensive Evaluation
Then, you should conduct a legally governed AI partner evaluation to review their offerings and verify the claims, services, documentation, etc. While due diligence can be a continuous monitoring process after you integrate the AI solutions, you still have to set a deadline for legal verification.
Partnership and Agreements
Once you finalize the AI vendor, it is time for SLA (service level agreements), NDAs (non-disclosure agreements), and other contracts that clearly set the terms of your partnership. These are agreed upon after multiple meetings and discussions.
Post-Deployment Monitoring
Even after the AI systems are deployed and integrated into your IT infrastructure, they have to be monitored to ensure transparency. This allows issues to be flagged quickly and resolved before they can blow up.
What is the AI Vendor Governance Checklist?
Before approving an AI vendor, you should consider the governance checklist with the following factors. Each of these further has multiple subcategories and questions.
AI Usage and Scope
Does the AI vendor offer products and services you require? When do you plan to implement the AI capabilities? How will these be communicated to customers? Which AI models does the vendor use? What is the scalability?
Data Management and Privacy
Where and how will the AI systems store user/business/customer data? Will this data be used to train the models? Does the vendor share it with third-party providers (Google, Microsoft, OpenAI, etc.)?
Regulatory Compliance
Which regulations and frameworks does the AI solution comply with? Are there records that can be used for auditing and regulatory purposes? Does the AI vendor have the required certifications as evidence?
Model Governance and Explainability
How does the AI vendor deal with human oversight? Does the service provider ensure transparent systems, especially in how the AI model processes the data from input to output? How can the model be tested for bias, accuracy, fairness, hallucinations, etc., before deployment?
Security and Asset Control
What security and governance measures does the AI vendor offer to protect your data and AI systems? What kinds of access does the model have (role-based, multi-factor, etc.)? How does it prevent data poisoning and deal with anomalies?
Third-Party Dependencies
Does the AI vendor rely on third-party providers and cloud platforms? Which critical core capabilities depend on other parties? What are the contingency plans to keep the systems running during disruptions?
Operational Resilience
What is the AI system’s uptime? What were the metrics used to calculate its performance and efficiency? What will happen if the AI tool fails or delivers inaccurate outcomes? Is there priority support to fix the issues quickly?
How DataToBiz Approaches This
At DataToBiz, we understand and value the importance of transparency and accountability in third-party relationships as technology and service providers. Our governance policies cover various legal requirements, thus assuring CEOs that we offer genuine, ethical, and reliable AI solutions aligned with their specifications.
We can offer our AI vendor compliance framework or comply with your organization’s checklist to address your concerns and answer them satisfactorily. Additionally, our data and AI governance services can support your due diligence of other vendors and ensure that the infrastructure is secure and compliant.
Conclusion
Your legal team should perform a thorough verification before approving an AI vendor as your technology partner. This checklist includes several factors, covering security, transparency, reliability, accountability, decision-making, and many more. AI companies should provide the required information and access policy documentation so that you can determine if your values align. A reliable AI service provider supports your legal team in completing the verification process seamlessly.
More in Artificial Intelligence Services Providers
Artificial intelligence (AI) services encompass various offerings, ranging from consulting to end-to-end product development and post-implementation maintenance. The solutions are tailored to align with your use cases, business values, and industry standards, thus generating higher ROI, increasing customer experience, and giving you an edge over competitors. CTOs can select the services they want from the AI company based on long-term objectives. DataToBiz partnered with a Los Angeles-based multi-location healthcare center to streamline operations, increase efficiency, automate workflows, and enhance AI governance.
FAQs
What governance documents should legal teams request from an AI vendor?
Your legal teams should request the following governance documents from an AI vendor:
- Training data governance
- AI management system (AIMS)
- AI risk management framework (RMF)
- Human oversight measures
- AI system description documentation
- Post-deployment monitoring
At DataToBiz, we ensure transparent services by providing the required documents and answering the clients’ questions in detail. We guarantee regulatory compliance for all our solutions.
What’s included in a complete AI vendor due diligence checklist?
The following are some important factors to include in your complete AI vendor due diligence checklist:
- Data handling
- Model training
- Operational reliability
- Security and compliance
- Bias mitigation
- Model transparency
- Integration fit
- Continuous monitoring
Contact DataToBiz to know about the factors that affect AI implementation in your business and how we address them.
How is vendor governance different from vendor security compliance?
Vendor governance is a larger framework dealing with the entire lifecycle and alignment of third-party solutions. Vendor security compliance is a part of it, dealing with regulatory compliance and data laws. It determines whether the vendor adheres to the required regulations. At DataToBiz, we provide our clients with the necessary documentation to assure them of complete governance and compliance in our solutions.
What red flags indicate weak governance in a technology vendor?
The following red flags indicate weak governance in a technology vendor:
- Undefined roles
- Frequent executive turnover
- Outdated security certifications
- Opaque financial reporting
- Vague documentation
- Evasive responses and lack of transparency
- Unclear policies for whistleblowing, anti-bribery, and anti-corruption
At DataToBiz, we strive to maintain transparency in all our services and regularly update our certifications. You can check out our policies here.
What governance policies has DataToBiz published for review?
DataToBiz has five main governance and compliance policies:
- Environmental, Social & Governance (ESG) Policy
- Corporate Social Responsibility (CSR) Policy
- Whistleblower Policy and Vigil Mechanism
- Anti-Bribery & Anti-Corruption Policy and Supplier Code of Conduct
- Human Rights and Modern Slavery Statement
Contact us for more details about our governance policies. Our team will provide you with the required information.
How can I access DataToBiz’s full set of governance policies?
You can easily access the full set of DataToBiz’s governance policies on our Governance and Compliance page. The documents are available for online reading and to download as PDFs. Clients with queries can schedule a meeting with our team and get more information about our governance policies. We believe in transparency, clear communication, and ethical business practices.
