Why Independent Assessment Matters: Managing Risks with Third-Party AI Vendors

With the emergence of AI laws worldwide, the significance of independent AI risk assessment solutions for monitoring AI vendors and ensuring compliance has amplified.

As AI increasingly becomes the backbone of modern business operations, organisations are more inclined towards relying on external AI vendors to integrate AI solutions into their workflows. This necessitates a re-evaluation of how companies assess and manage the risks associated with AI adoption within their organisations.

Emergence of regulatory bodies for AI Oversight

Various oversight bodies have been established to address the ethical, safety, and fairness concerns surrounding AI technologies. These bodies include the Partnership on AI, the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, and the European Commission's High-Level Expert Group on Artificial Intelligence. While these initiatives are instrumental in setting standards and guidelines, the reliance on third-party AI solutions demands a more proactive approach towards risk assessment and management.

Shift in strategies for third-party risk management

Due to the deterministic nature of traditional software and IT products, testing them once before market release usually suffices. However, AI systems necessitate ongoing training with data, raising concerns regarding data sources, copyright infringement risks, and the intricacies of the training process itself. This dynamic nature challenges the conventional testing approach and demands a more nuanced and continuous evaluation process.

In the above context, adopting a holistic approach to vendor assessments becomes imperative. Traditional third-party risk management (TPRM) strategies need to evolve to accommodate the dynamic integration of AI. A siloed approach to risk assessment is no longer effective, especially considering the complexities associated with AI technologies.

A comprehensive evaluation of third-party AI systems involves understanding the technical intricacies of the AI components deployed by vendors. This includes scrutinising dataset attributes and model attributes to identify potential risks such as bias, data quality issues, and transparency in decision-making processes.

Furthermore, AI governance frameworks play a crucial role in navigating compliance and legal requirements. It is essential for organisations not only to have robust internal AI governance practices, but also to evaluate their vendor's AI governance framework. This ensures alignment with global standards and regulations such as the EU AI Act, thereby promoting responsible AI usage throughout the ecosystem.

Operationalizing a comprehensive risk assessment mechanism involves integrating AI governance into existing TPRM workflows. Third-Party Risk Management solutions can streamline this process, helping companies make informed decisions about AI vendor solutions by providing comprehensive insights into potential risks and mitigating those risks effectively:

1. Vendor Evaluation:TPRM solutions can assist in evaluating AI vendors by collecting and analysing various data points about the vendor, including their reputation, financial stability, compliance with regulations, and past performance.
2. Solution Risk Identification: Identify potential risks associated with the AI vendor solution, such as data security vulnerabilities, compliance issues, ethical concerns, and operational risks.
3. Compliance Checks and Ethical Evaluation: Ensure that the AI vendor solution complies with relevant regulations and industry standards (such as EU AI Act, GDPR, HIPAA, or industry-specific regulations) and ethical implications of using the solution (such as bias in algorithms, privacy concerns, or potential societal impacts).
4. Security Assessment and Qualify Risks: Conduct security assessments to identify any vulnerabilities in the AI vendor's technology or infrastructure that could pose a risk to the company's data or operations. Additionally, qualify risks i.e. assess vendor and product risks mapped against the organisation’s size and risk appetite, to ensure that the organisation can manage the risks.
5. Scalability and Performance: Assess scalability and performance of the AI solution to ensure it meets the company's requirements and can handle future growth.
6. Monitoring across life cycle: Offer continuous monitoring capabilities to keep track of changes – such as the vendor's risk profile over time or the training data for AI solution - and promptly alert the enterprise regarding any emerging risks.
7. Reporting and Documentation: Generate comprehensive reports and documentation summarising findings of the risk assessment process, which can be used for internal review, compliance purposes, or communication with stakeholders.

For instance, AIQURIS - launched by TÜV SÜD, a global leader in testing, inspection, and certification services - empowers enterprises to qualify AI solutions that meet strict safety, security, and ethical standards while ensuring regulatory compliance.

Conclusion

Independent third-party risk assessment is essential for building trust and transparency in the AI vendor landscape, as organisations can mitigate potential legal and ethical pitfalls associated with AI adoption. This proactive approach not only safeguards against risks but also fosters an environment conducive to innovation and sustainable growth.

AIQURIS, a TÜV SÜD venture, helps organisations safeguard against third-party AI vendor risks and establish a comprehensive AI governance and testing framework. It serves as a benchmark for quality and trust in the dynamic AI procurement landscape and streamlines the adoption process for confident and efficient procurement.

The platform is dedicated to empowering enterprises to qualify AI solutions meeting stringent safety, security, and ethical standards, while ensuring regulatory compliance. Contact the team for intelligent assessment methodologies, comprehensive data testing services, and ongoing solution monitoring.