The financial services industry is rapidly emerging as a leader in AI adoption to personalise services, automate decisions, and streamline operations. These innovations introduce new risks, particularly as AI interacts with sensitive account and transactional data across complex hybrid environments. Due to the rapidly evolving nature of AI, the challenge is not just identifying risk, but about remediating it at speed.
Financial services environments are shaped by layered and highly specific policies governing data access, privacy, permissible outputs, and regional or role-based constraints. These policies are informed by regulation, internal risk frameworks, and business context, and they often vary across use cases, even when the same AI model or application is involved. When AI operates dynamically, responding to prompts and inputs as they occur, those policies must be enforced in context rather than assumed in advance.
Why custom controls matter for AI governance
Unlike traditional business applications, AI is inherently non-deterministic, meaning the same prompt or input can produce different outputs. So, when AI is used in production, risk is shaped by who is interacting with the system, what data is involved, and how outputs are ultimately used or acted upon. In financial services, the same AI capability may support very different scenarios, each with its own governance expectations and tolerance for risk. In an industry that is highly regulated, that creates new levels of complexity.
This complexity is compounded by the pace of agentic AI adoption. According to a 2026 survey by NVIDIA, 42 percent of financial services organisations are currently using or assessing agents.
This is why customisable controls for AI systems—often referred to as guardrails—become important. Rather than applying a single, uniform set of restrictions, organisations need the ability to tailor how policy is enforced during live AI interactions. Customisation allows controls to reflect contextual factors such as role, jurisdiction, data sensitivity, or intended use, helping to ensure governance remains aligned with institutional policy without treating all AI interactions as equally risky.
This approach also helps avoid common trade-offs. Guardrails that are too rigid can limit the usefulness of AI, while overly permissive ones increase the likelihood of policy violations or unintended exposure. Customisation provides a more balanced way to manage this tension
An example from financial services practice
Consider an example from a large financial services organisation exploring how to expand AI usage across the enterprise. Early pilots demonstrated value, but leadership identified a core concern: the same AI systems were being accessed by users with very different roles, permissions, and regulatory obligations.
Rather than treating AI as a single, uniform capability, the organisation focused on enforcing policy at the point of interaction. Guardrails were tailored to reflect internal governance rules, ensuring access to sensitive data and permissible outputs varied appropriately by role and jurisdiction. This approach allowed AI usage to expand without weakening existing access models or introducing new compliance blind spots.
The organisation also examined how these guardrails behaved under real-world conditions. By red teaming their AI systems, they were able to surface edge cases where prompts could lead to unintended disclosures or policy misalignment, particularly in multi-step interactions. Those findings informed refinements to how guardrails were applied, strengthening oversight while preserving usability.
Applying guardrails as AI use evolves
As AI adoption expands, usage patterns rarely remain fixed. New workflows emerge, responsibilities shift, and AI systems are introduced into areas with different risk profiles. Controls designed around early assumptions can quickly become misaligned with how AI is actually being used.
Customisable guardrails support incremental adjustment as these realities change. Policies can be refined to reflect new use cases, new areas of business, or evolving interpretations of risk without requiring organisations to redesign their AI systems or disrupt existing workflows. This adaptability is particularly important in financial services, where governance frameworks are often revisited as regulations mature and supervisory expectations become clearer.
Learning from real-world behavior
Even well-designed policies can behave differently once exposed to real-world usage. Ambiguous requests, edge cases, and multi-step interactions often reveal gaps between how guardrails are intended to work and how AI systems actually respond.
Structured evaluation and adversarial testing help surface these gaps by examining behaviour under pressure. Over time, insights from this process inform how controls are refined, allowing organisations to adjust enforcement based on observed behaviour rather than assumed risk alone. In regulated environments, this feedback loop supports a more defensible approach to governance by demonstrating that controls are actively maintained and informed by evidence.
Supporting responsible AI at scale
For financial services organisations, AI governance must be an operational concern that scales alongside AI usage. Having guardrails that can be customised and refined over time helps institutions maintain oversight without constraining innovation or usability.
By focusing on contextual enforcement, continuous learning from real-world behaviour, and reducing time to respond to vulnerabilities, organisations can adopt AI in a way that is both flexible and defensible. In doing so, new use cases can be supported as they emerge, while maintaining trust, accountability, and control.
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