Cyber Risk Advisory
Navigating the Risks of Leveraging AI Third Parties
As Artificial Intelligence (AI) continues to evolve, organizations are quickly realizing the risks associated with leveraging such technologies and how to mitigate some of those risks. While AI is enabling organizations to be more productive and increase operational efficiency, emerging risks related to data privacy & security, bias, and intellectual property rights are becoming more and more prevalent. AI Third Parties can be defined as suppliers that provide cloud-based AI platforms, machine learning models, AI-driven analytics tools, and automated decision-making systems. Below, we will explore how to identify such risks and the mitigation strategies required to combat them.
Data Privacy & Security Risks
As expected, data privacy and security risks related to vendors is always top of mind for organizations, but when it comes to AI specific risks, many models require access to large amounts of data such as personal and proprietary information. More specifically, such risks include the AI vendor possibly being non-compliant with data protection regulations (e.g., GDPR and CCPA) as well as the misuse of your organization’s data or even over-collection of the data.
In order to mitigate privacy and security related risks, it is recommended to implement a data minimization protocol to limit how much data your AI vendor is truly ingesting, robust contractual agreements around data handling, storage, and destruction techniques, and data anonymization in order to safeguard your data.
Bias & Fairness Risks
As previously mentioned, the use of AI technologies is relatively new to all of us and one specific risk within this equation is managing the AI model’s potential for bias. If the data is used to develop and strengthen the AI model is biased, the outputs could perpetuate such biases, leading to inaccurate perceptions.
When developing strategies to address this specific concern, your organization should consider leveraging diverse data sets that are representative of the diverse populations and contexts, as well as regularly testing AI outputs for signs of bias. Additionally, consider leveraging AI tools/vendors that provide transparency into how AI decisions are made and outcomes are produced.
Intellectual Property (IP) & Ownership Risks
Data sharing partnerships with AI vendors can certainly blur the lines around IP ownership if proper agreements and protocols are not put in place. Building on the risks referenced in the ‘Data Privacy & Security Risks’ section above, some additional risks involve the ambiguity around ownership of the AI models and its outputs, the potential for misuse of your organization’s proprietary algorithms by third parties, and ultimately the loss of competitive advantages due to unclear IP rights.
Organizations can begin to address these threats by developing and defining a listing of IP ownership requirements before engaging with AI vendors, as well as implementing restrictive contractual clauses to prevent vendors from reusing your organization’s IP without authorization.
Beyond AI-specific risks, organizations must recognize that third-party risk management (TPRM) extends far beyond technology providers. Every external partnership—whether with cloud service providers, SaaS vendors, supply chain partners, or outsourced service firms—introduces potential risks related to regulatory compliance, financial stability, operational resilience, and reputational impact. A comprehensive TPRM strategy should include robust due diligence, continuous monitoring, and well-defined risk mitigation frameworks to address evolving threats. By fostering a proactive risk management culture, organizations can build stronger, more resilient third-party relationships, ensuring long-term success while safeguarding critical assets and stakeholder trust.
Coalfire helps organizations strengthen their TPRM function through our portfolio of services such as program maturity assessments, program development, and vendor risk assessments.