AI Governance & TrustEvery phase
In banks and insurers, AI can only operate at scale if decision rights, controls and evidence are built into the process and the system. We design who may decide what, which actions require approval, how agent behaviour is evaluated and monitored, and what evidence must exist before and after every material decision.
Control is part of the operating model.
Traditional controls assume that people perform the work and systems mainly record it. When agents can interpret information, make recommendations and execute actions, the control model has to change with the process.
Decision rights, approvals, access, evidence, escalation and evaluation need to be defined before the system acts in production. When those controls are designed with the process, governance becomes part of execution rather than a separate review layer around it.
Decision rights
For every material action, we define who is allowed to decide: the agent, a person, or both. Authority limits, confidence thresholds, approvals and escalation paths are part of the process design.
Controls in execution
Identity, permissions, segregation of duties, evidence capture, logging and audit trails are enforced as agents interact with systems. Every action can be traced back to its inputs, rules, approvals and execution.
Evaluation and monitoring
Before go-live, the system is tested against defined cases, quality thresholds and failure scenarios. In production, performance, drift, exceptions and incidents are monitored, and changes to models, prompts or tools are re-evaluated before release.
AI systems that can be explained, controlled and audited.
A governance model embedded in the process and technical implementation, so business, risk, compliance and engineering work from the same decision rights, controls and evidence.
Decision-rights model
A clear definition of what agents may decide, what requires human approval, which thresholds apply and what evidence is required at each critical step.
Embedded controls & auditability
Access, approvals, segregation of duties, logging and evidence capture built into the system and mapped to the organisation’s existing control framework.
Evaluation & monitoring framework
Test cases, quality criteria, production monitoring and change controls that show how the system performs, where it fails and when intervention is required.
Start with one AI use case.
Tell us which AI system you want to take into production and which questions risk, compliance or audit are asking. In a 45-minute working session, we look at the decisions the system makes, the controls it needs and how to evidence both.