Executive Summary
We are witnessing the early formation of what could become one of the most consequential general-purpose technologies of our lifetime: generative artificial intelligence.
Much like the early days of the internet or the proliferation of cloud computing, the promise of GenAI has sparked widespread excitement. But this excitement comes with an underappreciated reality: strategic, reputational, and regulatory risks are rising just as fast.
For C‑suite leaders in regulated sectors, especially financial services, this is not simply a question of if or when – but how.
This playbook outlines a path forward. It provides a framework for adopting GenAI responsibly: aligned to enterprise controls, tested under real-world constraints, and guided by structured decision-making. It draws from the NIST AI Risk Management Framework and current enterprise practice to help financial institutions close the gap between opportunity and operational readiness through effective AI in risk management in banks.
1. The Strategic Risk Landscape is Shifting
AI risk is not theoretical. It is now an enterprise-wide concern, cutting across model risk, data privacy, cyber security, procurement, and reputational exposure.
GenAI introduces characteristics that traditional enterprise controls are not designed to manage:
- Stochastic behaviour: Outputs vary unpredictably across sessions
- Opacity: Most GenAI models cannot explain how a result was generated
- Leakage pathways: Input data may be stored, learned from, or exposed
- Rapid adoption: Employees and teams are using GenAI tools faster than control functions can respond
This creates a fragmented risk surface that makes it difficult for institutions to understand where they are exposed, how to respond, or how to scale responsibly.
Strategically, GenAI risk must be treated not as a technical issue – but as a board-level concern with long-term governance implications.
2. Maturity, Not Speed, Wins the Race
Enterprise AI adoption is uneven – and that’s not necessarily a bad thing. What matters is not how early you adopt, but how mature your approach is.
We define four levels of AI adoption maturity:
| Stage | Description |
|---|---|
| Curious | Monitoring GenAI developments; no experimentation underway |
| Experimental | Limited sandboxed trials, often driven by innovation or strategy teams |
| Operational | Select use cases deployed with controls, involving risk and compliance |
| Strategic | GenAI embedded across workflows, governed centrally, and aligned with business objectives |
Most enterprises today sit between Stage 2 and 3. The risk is not lagging adoption, but moving too quickly from experimentation to deployment without validation.
Disciplined progression through these stages allows enterprises to innovate without overexposing themselves.
3. The Execution Gap: Where Intent Meets Reality
Even with GenAI strategies and toolkits in place, most enterprises experience what we call the execution gap – the space between ambition and operational readiness.
This gap emerges because:
- Traditional onboarding timelines (6 - 12 months) are too slow for GenAI
- Procurement and risk processes were designed for stable, deterministic software
- Demos and slide decks are poor predictors of performance in live environments
- There’s often no environment to run real evaluations before triggering onboarding
The result is a dangerous dynamic: vendor hype moves faster than institutional control. GenAI tools are introduced into business units without clarity on how they behave, what risks they carry, or whether they align with internal policy, posing significant challenges for AI in risk management in banks.
The solution is to build a trusted execution layer – a space between demo and deployment where technology can be tested, evaluated, and scored before any contracts are signed.
4. Anchoring Risk: A Practical Framework for Enterprises
To manage GenAI risk in a repeatable, structured way, we recommend grounding assessments in four domains, aligned to the NIST AI Risk Management Framework:
1. Governance Risk
- Do we have board-level ownership of AI usage?
- Are lines of accountability and escalation clearly defined?
2. Data Risk
- Is input data (e.g. prompts, customer info) governed by policy?
- Are synthetic datasets being used to simulate sensitive scenarios safely?
3. Model Risk
- Are outputs explainable, testable, and free from harmful bias or hallucinations?
- Do we have a mechanism to measure and compare model behaviour across vendors?
4. Operational Risk
- Are AI tools integrated into secure workflows?
- Do end-users understand how to use AI tools - and when not to?
By anchoring risk to these four domains, leaders can better align procurement, IT, and governance teams around a shared vocabulary and evaluation process, strengthening AI in risk management in banks.
5. Building the Infrastructure to Test, Not Just Talk
Validation must happen before enterprise deployment – not after.
What’s needed is an environment where teams can:
- Access pre-vetted GenAI vendors
- Test solutions in secure, air-gapped sandboxes
- Use production-quality synthetic data without breaching privacy rules
- Run structured proof-of-concepts (PoCs) with built-in scoring, documentation, and decision points
- Share results with stakeholders before triggering onboarding or procurement
Without this infrastructure, enterprises are flying blind – deploying tools they haven’t meaningfully tested under their own constraints.
6. The Institutions That Win Will Be the Ones That Test First
GenAI will transform how financial products are built, delivered, and governed. But it will also expose institutions to a new category of fast-moving, hard-to-control risk, underscoring the critical role of AI in risk management in banks.
The institutions that succeed will not be those that adopt first.
They’ll be the ones that test first.
By anchoring risk, aligning teams around maturity stages, and closing the execution gap, leaders can unlock GenAI’s potential – with control.
Use NayaOne to Operationalise This Strategy
NayaOne is the leading Vendor Delivery Infrastructure for enterprises in regulated industries.
We enable your teams to:
- Discover and assess GenAI vendors quickly
- Validate tools using compliant, synthetic-data sandboxes
- Align evaluations to the NIST AI Risk Management Framework
- Run short, structured PoCs before onboarding vendors
Understand your GenAI risk exposure before you scale.
Join a 30-minute session to assess readiness and explore a risk-aligned approach.
