AI‑driven software that interrogates databases, activates services and shuttles data across corporate networks is spreading faster than the traditional, periodic controls many firms rely on. The result, senior director of governance, risk and compliance at Vanta Khush Kashyap warns, is a lack of visibility that could undermine risk management.
Visibility and inventory are the first steps
According to Kashyap, "The core issue is visibility." A large number of organisations cannot map every AI tool running inside their environment, leaving a hidden layer of applications that escape supervision. Without a clear picture, risk assessments become speculative.
To remedy this, companies should begin by compiling a detailed register of all AI systems, treating each as a potential risk hotspot. Each entry must be evaluated against criteria such as the sensitivity of the data it handles, the degree of autonomy it possesses and the impact on internal and external stakeholders. Based on this analysis, a risk tier – critical, high, medium or low – is assigned.
Matching controls to risk tiers
Control measures need to correspond with the assigned tier. High‑risk agents should operate only after human‑in‑the‑loop approval and under tightly defined permissions, whereas low‑risk tools may function with lighter safeguards. This tiered approach ensures that resources are focused where they matter most.
Regulatory momentum
Regulators are beginning to formalise these expectations. The EU AI Act already sorts AI systems into risk categories and requires proportionate governance, a framework that is shaping policy debates in Canada and the United States.
Even in the absence of binding legislation, forward‑looking firms are extending third‑party risk programmes to AI vendors, insisting on contractual clauses that limit model training, mandate incident reporting and grant audit rights.
From periodic checks to continuous monitoring
Continuous monitoring is replacing the old model of occasional reviews. As AI agents acquire new data sources or expand their capabilities, their risk profile must be reassessed in real time. Designating a single owner for AI governance creates clear accountability and bridges the divide between security, legal and data teams.
Documenting compliance evidence and sharing it with customers, regulators and other stakeholders is also essential. As Kashyap puts it, "When governance is built into AI from day one, it becomes a trust layer that enables rapid innovation rather than a bottleneck."
For businesses operating in Canada and across North America, the message is unequivocal: embed robust AI governance at the pace of adoption or risk falling behind a market that increasingly values responsible AI.