Automating Agentic AI Governance for Banks

Executive summary

• Automating agentic AI governance is achievable, but only with disciplined data, decisioning, and control automation.
• The core requirement is a structured, lifecycle-managed record for each use-case (the “Specification”) covering business context, technology, data, risks, controls and audit trail.
• Specialist second-line Risk Agents make most inherent and residual risk determinations, escalating exceptional cases to human second-line reviewers.
• Specialist Control Design Agents help the use-case owner to identify and configure appropriate controls to mitigate the inherent use-case risks.
• Deployment pipelines verify approvals and automatically configure technical controls (monitoring, logging, access, policy enforcement) in line with the approved design.

If organisations want to deploy thousands (or tens of thousands) of agents, today’s governance, risk assessment, and control (GRC) approaches must be materially automated, or they will become the primary bottleneck to scale.

With sufficient ambition, automation can go beyond ‘faster GRC’ to enable continuous, evidence-driven assurance, a state that is potentially extensible across the broader technology control environment.

Automating AI GRC

To automate the agentic AI GRC journey it must be digitised, which relies on five key elements:

1. Standardising and structuring use-case descriptions (business context, operating model placement, technology, data, risks, controls and audit trails).
2. Inheriting the risk/control posture of underlying platforms and capabilities that have already completed GRC assessment.
3. Deployment of second-line “Risk Agents” to perform risk and control analysis and conclude outcomes in most cases, with defined escalation paths.
4. Implementation of deployment gates that verify risk approval and required control design before release.
5. Automation of technical controls, monitoring and evidence capture, configured as part of the deployment pipeline.

Elements 1, 2 and 3 from the above list sit at the heart of automation, with elements 4 and 5 extending automation through into deployment.

The Agentic Development Framework (ADF)

We refer to this end-to-end approach as the Agentic Development Framework (ADF), which is a repeatable lifecycle for designing, assessing, deploying and operating agentic AI with embedded governance. Each stage can be automated independently or delivered as an end-to-end journey. The key stages of the ADF are:

1. Information gathering
2. Risk assessment
3. Solution design of the Agentic Operating Model (use-case)
4. Solution build
5. Testing
6. Deployment
7. Ongoing control and oversight

We note the only difference between the ADF, and any other development framework is its focus. The ADF is about changing the operating model as opposed to just building software. Agents, by their very nature, are designed to be embedded into processes and may not be readily visible to the process owners and operators. As such, a focus on the operating model is essential for maintaining understanding and avoiding a loss of control. Nevertheless, this is not a pre-requisite for automating AI GRC.

Enabling automated GRC within an ADF

At the heart of an automated ADF is the creation and lifecycle management of a structured information repository for each agent and its use-case (aka “the Specification”). In this context, the specification differs from typical agent documentation in a number of ways, as follows:

• It is comprehensive: it captures the full use-case, not just the technical agent (business, process, technology, data, risks, and controls and the audit trail).
• It is connected: it links to organisational taxonomies and hierarchies (e.g., organisational structure, process, legal entity, technical).
• It persists: the Specification will last for the agent’s lifetime and remains connected to the running service.
• It supports explainability: it includes facts and judgement calls (with rationale) made about the use-case.
• It is detailed: it maintains a complete audit trail of decisions and approvals, both automated and human.
• Is it fully legible: the Specification is both human and machine-readable and can be interrogated programmatically.
• It is integrated: it integrates with software delivery (i.e., CI/CD1) to enforce ‘approved-to-deploy’ gates and prove ‘compliance-to-proceed’.
• It enables outcomes: it can drive control configuration, evidence capture, attestation and assurance activities.
• It supports accountability: the Specification can also be digitally signed to support full accountability and non-repudiation.

The Specification is created early and incrementally enriched throughout
the ADF process. A key benefit of automation is that risk and control
expectations can be re-evaluated whenever the Specification changes. This
provides an always-current view of required controls and readiness-to-deploy, facilitating an agile development approach.

Automated GRC tooling

The Specification set out previously is at the heart of the GRC automation. It provides the backbone which all of the automated tools leverage to “understand” the AI, enabling decision making and action and writing decisions and actions back to the specification. Choosing the right tooling for the Specification that can integrate with the rest of the organisational technology stack is therefore of paramount importance. The components below can all enhance the levels of automation possible:

• Guidance Agents that help owners and engineers complete the Specification to a defined minimum standard of completeness, evidence and traceability.
• A policy and legal/ethics assessment agent to determine permissibility and alignment with organisational standards.
• Second-line Risk Agents that classify inherent risk by risk type, with explicit escalation thresholds.
• Control Design Agents that propose mitigating controls for both the agent and the surrounding business process.
• Residual risk evaluation Risk Agents that assess whether proposed controls reduce risk to within appetite (with escalation where required).
• A go/no-go decision service that maintains indicative and final deployment status as the Specification evolves.
• Automated registration of deployed agents in the configuration management database² (CMDB) or service registry for ownership, dependencies and criticality.
• A control configuration and monitoring layer that provisions and manages technical oversight controls (inc. telemetry, logging, policy enforcement, alerting).
• A standardised control outcome schema to record control execution and evidence, enabling monitoring and assurance to consume results consistently.

How to automate your ADF

To start automating the ADF requires organisations to embrace two foundational concepts. First, that deploying an agent into a business process is not only a technology delivery challenge. It is also a change to the target operating model, which we will refer to here as the ‘Agentic Operating Model,’ since controls span the agent as well as the surrounding operating environment, including manual and process controls.

Secondly, organisations will need to establish mechanisms for capturing and maintaining a structured Specification for each agent that meets minimum standards. A practical success measure is that human reviewers can reach decisions from the Specification alone, without recourse to the use-case owner. If they cannot, then neither could a Risk Agent.

Once these foundational elements are in place, the automation of the ADF can truly begin. Our suggested order would be as follows:

1. Specification quality drives the accuracy and efficiency of all downstream activity, whether human or automated. Hence, the first move should be to codify the Specification standard within the ADF – including required fields, evidence standards, taxonomies, policy references and control expectations – and then deploy Guidance Agents to help owners and engineers create and maintain Specifications to that standard³.
2. Next implement second-line Risk Agents by risk type. These agents should themselves be designed, assessed and approved through the ADF, using their own Specification. Once approved, they can assess other agentic use cases by reading the relevant Specification and applying a codified risk-domain methodology. Institutions should start with domains suited to structured assessment before moving to areas requiring more judgement-led analysis. As the Risk Agents develop and mature, new information requirements will emerge. These should be fed back into the Specification standard and reflected in the Guidance Agent accordingly.
3. Now implement CI/CD pipeline controls, providing automated verification of second-line approval-to-deploy, automated configuration of monitoring controls, CMDB registration, control-library instantiation and reporting dashboards.
4. Finally, consider a broader technical controls architecture that captures control execution and outcomes in a standardised form across the enterprise – one that encompasses both systems and manual processes. This will enable automated attestation, assurance and retrospective analysis of operating effectiveness, and can materially improve the consistency of operational monitoring.

Some manual elements remain in this automation paradigm

While much of the heavy lifting can be fully automated there are key elements that will always remain manual because they ensure that humans remain accountable and in control of their agentified processes. These include:

• Identification of the use-case (for now)
• Risk judgements over very high-risk use-cases exceeding threshold
• Accountability for the risk assessment agent’s decisions
• Manual controls forming part of the use-case’s control environment
• Accountability for the use-case in operation within the business
• Independent assurance (for now)

Conclusion

If organisations want to safely scale their agentic deployment, they need a GRC paradigm that is data-driven and automation-first.

The essential enabler is a structured Specification for each use-case, treated as a living record and integrated into delivery workflows. With this foundation in place, specialist Risk Agents can remove assessment bottlenecks through high-confidence decisions and clear escalation paths, while automated deployment gates and monitoring controls provide continuous assurance.

References

1. CI/CD refers to Continuous Integration/Continuous Delivery and is used here to describe the software delivery pipeline that moves an AI use case from build and test into release, while enforcing governance controls along the way.

2. A Configuration Management Database (CMDB) is the central inventory used to record technology assets, ownership dependencies, and control requirements.

3. As a point of further clarification, Risk Agents contain “knowledge” about the information they need to be able to deliver their risk judgements. The Guidance Agent curates this information request from each of the risk agents and presents them to the user in a well organised and succinct (i.e., de-duplicated and amalgamated) manner. In this way the “guidance” can be maintained by the human team who are expert in each risk domain. Until such time as the risk agents are in play, the Guidance Agent can be provided with a pre-defined set of information that is needed. In this context, the Guidance Agent itself is simply seeking to fill in the required Specification information.