Artificial intelligence governance must become operational, cross-functional, and embedded from the earliest stages of development if firms are to manage the growing risks posed by AI and generative AI. Speakers argued that model risk professionals are un
For organizations rushing to deploy
artificial intelligence, the greatest risk may not be the technology itself,
but waiting too long to govern it.
That was the central argument put
forward by a founder of a consultancy specializing in model risk management
frameworks, who warned that firms can no longer afford to treat AI governance
as a compliance exercise performed after development is complete.
Instead, governance must become
operational, embedded, and active throughout the entire AI lifecycle.
Speaking during CeFPro’s Advanced
Model Risk conference, the executive argued that traditional model risk
management frameworks provide an important foundation, but are no longer
sufficient to address the complexity, speed, and opacity associated with modern
AI and generative AI applications.
“AI governance, particularly in the
insurance industry, must grow up fast,” she said.
The speaker described how
organizations increasingly rely on AI systems that influence underwriting
decisions, claims management, customer interactions, and internal operations.
While the technologies continue to
evolve rapidly, she argued that the core challenge remains unchanged: ensuring
that systems are reliable, transparent, explainable, and fair.
“At the heart of this challenge is
risk,” she said, warning that traditional concerns around model explainability
and unintended bias become amplified when AI systems operate at scale.
The executive highlighted a growing
list of risks associated with both traditional AI and generative AI
applications, including complexity, lack of transparency, bias, privacy
concerns, legal exposure, reputational damage, and potential harm to consumers.
She noted that generative AI
compounds these challenges by introducing vast external datasets, decentralized
experimentation, and limited visibility into how outputs are produced.
Regulators have begun responding. The
speaker pointed to guidance emerging from insurance regulators including the
National Association of Insurance Commissioners, Colorado regulators, and the
New York Department of Financial Services. Yet she cautioned against viewing
regulation as the primary solution.
“These regulatory developments are
welcome but uneven,” she explained. “They signal intent, not full alignment.”
Instead, she argued that
organizations must proactively establish governance structures capable of
adapting as regulatory expectations continue to evolve.
Central to that effort is
operationalizing governance rather than simply documenting it.
“The question isn’t just about
compliance,” she said. “It’s about creating systems of governance that are
functional, effective, and embedded into the DNA of how companies operate.”
The executive advocated for
enterprise-wide AI governance frameworks built around transparency,
accountability, privacy, fairness, and security. However, she stressed that
principles alone are insufficient without supporting structures.
