TrustWorks360 Selected for The Buy Australian AI Partnership

AI in banking is moving from advice and analysis into action. That changes the risk profile.

When an AI system can access sensitive data, assess a customer request, recommend a credit outcome, trigger a payment workflow or interact with financial crime controls, governance cannot sit only in policy documents or post-incident reviews. It needs to operate at the point of action.

TrustWorks360 has been selected as a National Digital Track participant in the Buy Australian AI Partnership, a program designed to support local AI capability and help Australian organisations adopt AI safely and responsibly.

Through the program, TrustWorks360 will work with participating financial institutions to explore how AI can be adopted in high-impact banking environments. Our focus will be practical AI governance leverageing Trustworks360 AI Governance Framework. We particularly interested in exploring runtime guardrails that can translate organisational policies, risk limits and delegated authority into controls that act before an AI system takes a consequential step.

The goal is simple to state and difficult to execute well: turn responsible AI principles into practical, enforceable controls that allow useful AI adoption while reducing avoidable risk.

Why the Buy Australian AI Partnership matters

The Buy Australian AI Partnership supports the development and adoption of Australian AI capability. The program is backed by the National AI Centre as Principal Sponsor and Stone & Chalk as Delivery Partner.

For organisations in regulated sectors, local capability matters for more than procurement. It supports closer alignment with Australian regulatory expectations, data handling norms, customer needs and industry risk settings.

Banking is a clear example. Financial institutions already operate within strict obligations around privacy, security, credit, payments, fraud, financial crime, conduct and operational resilience. AI does not replace those obligations. It adds new ways they can be met, tested or breached.

That is why safe AI adoption in banking needs more than experimentation. It needs a practical way to answer questions such as:

• What can an AI agent access?

• What can it decide by itself?

• What must it escalate to a person?

• What actions are never permitted?

• How are exceptions handled?

• How is every decision or blocked action recorded?

• How do controls change when the risk level changes?

  
  

These are not abstract governance questions. They affect product design, customer experience, compliance, operational risk and trust.

The Buy Australian AI Partnership gives participating organisations a setting to explore those questions with Australian AI providers. For TrustWorks360, it is an opportunity to contribute to a key national conversation: how to make AI useful in financial services without allowing speed to outrun control.

AI in banking is becoming more active

Early uses of AI in banking often centred on analysis. Systems helped detect suspicious patterns, classify documents, support call centre teams or improve search across internal knowledge bases.

Those uses still matter. Yet the next wave of AI is more active.

AI systems and agents can now be designed to:

• Read and summarise bank records

• Interpret customer intent

• Recommend next steps

• Prepare responses

• Trigger workflows

• Request or use sensitive data

• Take actions through connected systems

  
  

That shift creates value. It can help staff respond faster, reduce manual handling and improve consistency in complex processes. It can support better customer outcomes when designed and controlled well.

It also raises harder questions. A chatbot that gives general guidance presents one type of risk. An AI agent that can initiate a payment, change a customer setting, assess hardship information or influence a credit workflow presents a different type of risk.

The risk is not only that an AI model may produce the wrong answer. It may take the wrong action, at the wrong time, with the wrong authority, using data it should not have accessed.

This is where runtime controls become central.

Safe AI adoption depends on what happens before an action occurs, not only on what an organisation reviews after the fact.

Runtime guardrails turn policy into action

Most organisations already have policies that describe acceptable conduct, risk limits, data access rules and delegated decision-making authority. The challenge is that policies are usually written for people and reviewed after work has been done.

AI agents need controls that operate in the flow of activity.

A runtime guardrail is a control that can inspect an AI request, decision or proposed action before it occurs. Based on the organisation’s policy and risk settings, the control can then allow, constrain, escalate or prevent the action.

That distinction matters.

A policy might say that only certain roles can approve a high-value payment. A runtime guardrail can check the payment value, account context, customer risk profile and the AI agent’s delegated authority before any instruction is sent.

A policy might say that sensitive customer data should only be used for an approved purpose. A runtime guardrail can check whether the AI agent’s request matches the customer task, whether the data category is permitted and whether a lower-risk alternative is available.

A policy might require human review for certain credit decisions. A runtime guardrail can identify when a recommendation crosses a threshold and route it for review instead of allowing automated completion.

In practice, runtime guardrails help answer four critical questions.

They define what the AI system is allowed to do

Permission needs to be specific. A system may be allowed to summarise a customer interaction, but not update account details. It may be allowed to prepare a draft payment instruction, but not release it.

Clear authority boundaries reduce confusion and make accountability easier to manage.

They adjust controls based on risk

The same AI action may be low risk in one setting and high risk in another. A customer service agent summarising a public product disclosure document is different from an AI system analysing hardship information or identity documents.

Runtime controls can reflect that difference by applying tighter checks when data, customer impact or transaction value increases.

They escalate when judgement is needed

Good governance does not mean blocking all difficult cases. It means knowing when a human needs to review, approve or own the outcome.

Escalation can support customer fairness, regulatory compliance and better operational decisions.

They create a record of what happened

Auditability is vital in banking. When an AI action is allowed, constrained, escalated or stopped, the organisation needs a clear record of the control decision.

That record should help answer why the action was treated that way, which rule applied and what happened next.

Banking use cases where stronger guardrails can help

As part of the program, TrustWorks360 would like explore potential banking use cases where stronger runtime controls could deliver value. The purpose is not to automate for its own sake. The focus is on identifying where AI can help while keeping control close to the point of risk.

Several areas stand out.

Payments

Payments are high-impact because they can lead to real-world financial movement. AI may assist with payment queries, detect unusual instructions, pre-fill transaction details or support internal workflow routing.

Runtime guardrails can help by checking:

• Transaction value

• Destination risk

• Customer consent

• Agent authority

• Suspicious patterns

• Required approval steps

  
  

For example, an AI system may be allowed to draft a payment response for review, but prevented from initiating or approving the payment itself. Where a transaction looks unusual, the system could escalate before any instruction progresses.

Customer servicing

AI can help service teams understand customer history, summarise prior contact, suggest next steps and provide faster responses. It can also introduce risk if it accesses too much information or gives advice outside policy boundaries.

Strong controls can limit data access to the customer’s current need, block sensitive inferences and require escalation for complaints, hardship, vulnerability signals or complex disputes.

That can support faster service while keeping human judgement involved where it matters.

Financial crime

Financial crime controls often depend on pattern detection, risk indicators and timely escalation. AI may support triage, case summarisation and alert handling.

The guardrail challenge is to ensure AI does not suppress, misclassify or mishandle material signals. Controls can set boundaries around what an AI system can close, what it can recommend and what needs specialist review.

For high-risk cases, the right answer may be speed with oversight, not automation alone.

Credit decisions

Credit is consequential. AI can support document review, affordability assessment, credit policy checks and explanation of decision factors. Yet credit outcomes require care, fairness, consistency and compliance with applicable obligations.

Runtime controls can help determine when AI may assist and when it must stop short of a final decision. They can also require review where an edge case, exception or protected customer circumstance appears.

The stronger the customer impact, the stronger the real-time control should be.

Sensitive data access

Banking often involves highly sensitive personal and financial information. AI systems should not treat data access as an all-or-nothing permission.

Runtime guardrails can assess purpose, context, data category and authority before access is granted. They can also mask or minimise data when full access is not needed.

This is one of the clearest areas where practical AI governance can improve both safety and customer trust.

Responsible AI needs enforceable controls

Responsible AI principles provide an essential foundation. They explain what an organisation values: fairness, transparency, privacy, accountability, safety and human oversight.

The hard work begins when those principles need to guide real systems.

For example, a principle might say that AI should be accountable. In a banking workflow, that needs to become specific:

• Who owns the AI-assisted action?

• What rules applied at the time?

• Was the AI allowed to act alone?

• Was a human required to review the output?

• What evidence is available for audit?

• What happens when the system is uncertain?

  
  

A principle might say that AI should protect privacy. In practice, that means defining data access rules, checking purpose, limiting unnecessary exposure and recording access decisions.

A principle might say that AI should be safe. In practice, that means preventing certain actions, slowing others down and escalating the cases that need human care.

This is why TrustWorks360’s focus in the Buy Australian AI Partnership is centred on runtime guardrails. They are the link between intent and enforcement.

What practical AI governance should look like

Practical governance should help teams build and operate AI safely. If governance is too vague, teams cannot apply it. If it is too slow, it pushes risk decisions outside the process. If it is too detached from systems, it becomes paperwork.

For high-impact AI in banking, good governance should be clear enough to implement and flexible enough to handle real cases.

Key features include:

Policy-to-control mapping

Organisational rules need to connect to system behaviour. A policy should map to a control, a threshold, an escalation path or a prevention rule.

Delegated authority for AI systems

AI agents need defined limits, just as people do. The organisation should know what each system can do, under what conditions and with whose authority.

Context-aware decisions

Risk changes with context. Controls should consider customer impact, data sensitivity, transaction value, uncertainty and business process.

Human review where it adds value

Human oversight should be purposeful. It should focus on judgement, exceptions, customer impact and accountability, not rubber-stamping every low-risk step.

Evidence and audit trails

Control decisions should be recorded in a way that supports assurance, review and improvement.

Continuous monitoring

AI systems and controls both need review as use cases, models, policies and risks change.

This is not only a compliance issue. Done well, guardrails can create confidence for teams to use AI in more valuable ways. Clear boundaries can make adoption easier, because staff and leaders know where AI can act and where it cannot.

Building Australian AI capability responsibly

Australia has an opportunity to build AI capability that reflects local expectations and sector needs. That matters in banking, where trust is central and customer harm can be significant.

Programs such as the Buy Australian AI Partnership help create practical pathways between technology providers and major institutions. They encourage real use cases, not abstract claims. They also help local AI companies test ideas against the demands of regulated, high-impact environments.

For TrustWorks360, participation is a chance to work with financial institutions on some of the most important questions in applied AI governance:

• How do we make AI agents safer before they act?

• How do we enforce policy without slowing every process?

• How do we protect sensitive data while still improving service?

• How do we give teams confidence to use AI responsibly?

• How do we turn high-level responsible AI commitments into working controls?

  
  

These questions will shape the next phase of AI adoption in banking.

What comes next

TrustWorks360 looks forward to contributing to the National Digital Track and working with participating organisations across the Buy Australian AI Partnership.

The work ahead will focus on practical banking use cases, including payments, customer servicing, financial crime, credit decisions and sensitive data access. Across each area, the emphasis will remain the same: identify where AI can create value, then define the controls needed to manage risk before action occurs.

Safe banking AI will not be achieved through principles alone. It will come from connecting principles to enforceable decisions in live workflows.

That is the promise of runtime guardrails. They make responsible AI practical. They help organisations adopt AI with clearer boundaries, stronger accountability and better protection for customers.

To learn more about the program, visit the Buy Australian AI Partnership.

TrustWorks360 is pleased to be part of this national effort and to help turn responsible AI into controls that support safer adoption, stronger trust and better customer outcomes.