Point-in-time assessments were designed for a more stable environment. The rapid evolution of AI in vendor products has exposed the limits of that model.
Most GRC teams conduct vendor assessments with genuine rigor. Questionnaires are distributed, responses are reviewed, risks are scored, and findings are documented. The process functions as intended. The problem is not the process itself. The problem is the assumption embedded within it: that a vendor's risk posture remains sufficiently stable between annual review cycles to be governed by a single point-in-time assessment.
That assumption no longer reflects the operating environment. AI has fundamentally changed the rate at which vendor products evolve. A vendor can update its underlying model, introduce a generative AI feature into an existing workflow, or modify its data handling practices through a standard product release. None of these changes require a contract amendment, and few trigger proactive notification under legacy agreements. By the time a GRC team conducts its next scheduled review, the product being assessed may bear little resemblance to the one that was evaluated twelve months prior.
The interval between assessment cycles creates what practitioners increasingly refer to as a surveillance gap: a period during which material changes to a vendor's risk profile may occur without the knowledge of the organizations that depend on that vendor. For vendors with stable, slowly evolving products, this gap carries limited consequence. For AI-enabled vendors whose capabilities are updated continuously, the gap can be substantial.
Consider a vendor that was assessed prior to the introduction of its AI-powered features. The original assessment addressed data handling, access controls, and incident response procedures. Since that review, the vendor has integrated a third-party foundation model, introduced an AI assistant that processes organizational data, and updated its inference log retention policy. None of these developments are reflected in the current risk record, and none were captured by an assessment designed for the product as it existed at a prior point in time.
Addressing the surveillance gap does not require abandoning the annual assessment framework. Regulatory expectations and audit requirements make structured, periodic reviews a permanent fixture of sound GRC practice. What is required is a supplementary layer of continuous assurance that generates signal between scheduled reviews.
In practice, this means establishing defined triggers for out-of-cycle re-assessment. Events that should prompt immediate review include vendor announcements of new AI capabilities, changes to a vendor's underlying model provider, and AI-related incidents reported by peer organizations using the same platform. These triggers ensure that the assessment record reflects material changes as they occur, rather than as they are discovered at the next annual cycle.
It also requires revisiting contractual frameworks. Organizations whose vendor agreements predate the introduction of AI capabilities should prioritize amendments that establish notification obligations for material changes to AI features and data handling practices. Without such provisions, vendors face no contractual obligation to inform their customers when the risk profile of their product changes in ways that matter.
Annual assessments remain a necessary foundation for vendor risk management. They are not, however, sufficient in an environment where the products being assessed can change materially within a single quarter.