A new legal analysis warns that organisations may be significantly underestimating the risks arising from third-party artificial intelligence tools embedded across their operations. According to JD Supra, businesses are rapidly adopting external AI models for customer service, analytics, fraud detection, hiring, and productivity applications. However, most lack visibility into how these models are trained, governed, and monitored — creating a widening blind spot in enterprise risk management.
The report highlights that third-party AI introduces layered exposures because companies rely on algorithms they do not own, control, or fully understand. This creates legal, operational, and reputational vulnerabilities, especially as regulators worldwide move toward stricter AI accountability standards. Misaligned training data, opaque model logic, and undisclosed subcontractors in AI supply chains add further complexity. When embedded models generate biased outputs, inaccurate decisions, or security failures, liability often still falls on the organisation using the tool, not the vendor.
Experts cited in the analysis argue that existing vendor-risk frameworks are inadequate for AI systems because they focus on cybersecurity, financial stability, and contractual compliance, but do not address model drift, data provenance, hallucinations, algorithmic bias, or misuse risks. As AI becomes deeply integrated into third-party platforms — from HR systems to payment tools — the absence of technical audits and ongoing monitoring heightens systemic exposure.
To close the gap, legal professionals encourage organisations to establish AI-specific risk assessments, demand transparency on training data and model governance, negotiate stronger contractual protections, and implement continuous monitoring. With AI regulation accelerating in the US, EU, and Asia, the report emphasises that companies must treat third-party AI oversight as a core compliance requirement rather than a peripheral IT concern.
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