Managing AI Risk Through a Use-Case Lens

ASIC’s findings show AI uptake outpacing oversight. This article explores how boards can connect enterprise governance with day-to-day risk decisions for safer, smarter AI adoption.

AI is being adopted at a rapid pace, but the guardrails aren’t always keeping up.

Earlier this year, ASIC released Report 798: Beware the Gap, which reviewed 624 AI use cases across financial services. The regulator found innovation is outpacing governance. Many organisations plan to increase their use of AI while adequate controls lag, leading to real risks of consumer harm and organisational exposure. With APRA also planning to assess the appropriateness of AI risk management and oversight practices as part of its strategic priorities, the use of AI in financial services is now under the microscope.

It’s a wake-up call not just to strengthen governance frameworks but to look more closely at how AI is used across a business. Because the risk of AI is more than the technology, it extends to its context.

That’s where the gap lies. Not just between innovation and governance, but between enterprise-level oversight and operational-level risk.

This article explores why boards and executive teams need a use-case lens, and how applying it can help manage risks and speed up the safe adoption of AI in regulated businesses.

Enterprise oversight vs operational risk

It’s tempting to see AI risk as a single issue. But it isn’t. The risks a board steers at a strategic level aren’t the same as the ones your team faces every day.

ISACA, a community of IS/IT professionals, explains this distinction well. Boards set the AI risk appetite – the big-picture boundary for how much risk an organisation is willing to take. Management then applies the AI risk tolerance – the day-to-day limits and thresholds for actual AI use.

Why does this matter? Because appetite without clear tolerance measures leaves people guessing. Tolerance without an agreed appetite leads to mixed decisions. For example, a board might say, “We’re open to experimenting with AI”, but without tolerances, a team could launch a chatbot that collects sensitive data without safeguards.

The interplay between the board, management and operations shows this distinction in action. Boards update their risk appetite statements as new AI uses emerge. Management takes that and updates accountability and assurance, risk and compliance (AR&C) functions so everyone knows who’s responsible. Here’s a non-prescriptive example of what that could look like:

Risk Type (Owner, CRO)	Appetite Level	Example Statement	Risk Metric(s)	Control Examples
Compliance Risk (General Counsel)	Low	Minimal tolerance for regulatory breaches, including AI non-compliance.	% of AI systems with completed regulatory impact assessment (target: 100%). Number of compliance breaches (target: 0).	AI regulatory impact assessments (RIAs) prior to production; Legal & Compliance sign-off workflow; Automated monitoring of regulatory changes.
Cybersecurity Risk (CISO, CIO)	Low	Minimal appetite for AI-related cyber and data privacy risks.	% of AI workloads with active vulnerability scanning & encryption at rest/in transit (target: 100%). Number of security incidents per quarter (target: 0).	Security posture scans in CI/CD; Vertex AI private endpoints; GCP IAM least-privilege access; AI system penetration tests.
Financial Risk (CFO)	Medium	Moderate risk accepted for technology investments, including AI adoption.	Variance of AI project spend vs. approved budget (acceptable ±10%). Portfolio ROI on AI investments (target: ≥ baseline ROI).	AI project portfolio tracking in GCP Looker; Stage-gate investment approvals; Quarterly cost/benefit analysis.
Product Development (Head of Product)	High	Willingness to take significant risks to leverage LLMs and AI for innovative products, with robust controls.	% of AI product releases meeting defined go/no-go criteria (accuracy, safety, fairness) (target: ≥90%). Time-to-market for AI prototypes (target: ≤ defined cycle).	AI product release checklist (accuracy, fairness, safety, interpretability); Sandbox environments; Rapid iteration pipelines with rollback capability.
Operational Risk (COO)	Medium	Moderate risk accepted in deploying advanced AI, provided risks are assessed and managed.	% of AI systems with completed risk assessments and operational fallback plans (target: 100%). Mean time to recover (MTTR) from AI service failure (target: within SLA).	AI service runbooks; Automated monitoring/alerting (Cloud Monitoring, Cloud Logging); Blue/green deployment strategy for model updates.
Reputational Risk (CMO, CEO)	Low	Low tolerance for adverse publicity or stakeholder concern arising from AI or LLM misuse.	Number of AI-related adverse media or regulatory notices (target: 0). Stakeholder trust score (via surveys) (target: ≥ baseline).	AI communication protocols; Proactive stakeholder engagement; Social media and press monitoring; Crisis response playbook.

At the management–operations level, clarity comes from simple and repeated messages. Teams need more than a policy to govern their use of AI at work. They need to know how it applies to the tools they use each day.

The case for a use-case lens

AI is being rapidly rolled out across organisations, but appropriate case-based oversight is still catching up. Too often, organisations apply the same governance rules to every AI use case – regardless of purpose, impact or risk level. For example, a chatbot offering general information doesn’t carry the same risks as an AI model making lending decisions or detecting fraud.

Rather than viewing AI as a single, uniform technology, assessing each application individually helps leaders understand what they’re governing and where to focus their attention.

Why you should adopt a use-case lens

Looking at AI one application at a time can improve the quality of risk decisions. It shifts oversight from abstract principles to practical action. Boards and executives can ask sharper questions about purpose, impact and risk before tools are deployed.

What a use-case lens looks like in practice

A use-case lens means breaking AI into distinct parts and applying the same basic checks to each tool. At a high level, here are some steps an organisation might take:

1. Map every AI tool currently in use.

2. State its purpose, data sources, outputs and decision impact.

3. Identify who uses it and who it affects e.g. customers, staff, or regulators.

4. Assess its potential for consumer harm, bias, financial exposure or operational disruption.

5. Assign an executive owner of use-case risk.

How it aligns AI investment with actual business risk

Different applications carry different risk profiles. Understanding those profiles avoids over-controlling low-risk tools and under-scrutinising high-risk ones. For instance:

• A chatbot answering simple FAQs may only need basic privacy checks and routine monitoring.

• A machine-learning model making credit decisions demands far more scrutiny, including fairness testing and clear escalation procedures.

• A predictive tool used internally to forecast maintenance schedules may sit in between – low customer impact but high operational dependency.

With this level of clarity, you can prioritise resources where they’re needed most and support innovation with clearer guardrails.

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Busting the AI hype

Before you assess AI risks, it helps to cut through the hype.

What AI is (and isn’t)

AI is another business transformation tool, like big data, cloud-first programs and digital initiatives.

How it actually works

Large language models generate statistical word patterns. They sound fluent but are brittle under testing.

Current AI produces answers one way only, with no memory of how those answers were calculated.

These tools struggle with long context and can contradict themselves easily.

These tools are not conscious, sentient or on a path to artificial general intelligence.

Where bias and risk arise

Bias in models is real, but the bias in our questions is often greater.

Without skilled people, AI can be dangerous. Critical tasks need trained staff, not unprepared or underqualified workers.

Cutting graduate hiring or trying to replace jobs with AI is short-sighted and undermines governance.

Using AI well

In skilled hands, these tools can improve decisions and efficiency.

With clear guardrails and human judgement, AI can support your business – but it won’t run it for you.

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Why does an operational risk approach matter?

As mentioned earlier, strong governance frameworks alone aren’t enough to manage AI well. Without an operational risk lens, boards and executives can miss key warning signs:

• Blind spots: When AI is treated as a single category, organisations can accumulate risk across multiple applications without seeing the combined effect. Small risks may appear acceptable on their own but can create a much larger exposure when added together.

• Misallocated oversight: Low-risk tools can get stuck in layers of approval, while high-risk tools slip through with minimal scrutiny. This imbalance can waste resources and weaken trust in the oversight process.

• Innovation bottlenecks: Without clear parameters, business units either hesitate to deploy AI or move ahead without the right guardrails. Both outcomes can slow progress and increase uncertainty.

• Board discomfort: When oversight is inconsistent, boards may lose confidence in management’s ability to manage AI-related risks responsibly. This can lead to more conservative decisions, delays and missed opportunities.

Taking these factors together shows why it’s worth connecting high-level governance with day-to-day risk decisions. It helps organisations make more confident choices about where AI fits in its operations.

Making AI oversight work day-to-day

Let’s look practically at how to prevent the risks listed in the previous section with day-to-day AI oversight that turns principles into action.

Establishing acceptable uses

Start by defining what AI can and cannot be used for in your organisation. This doesn’t need to be a heavy policy document. A short, clear acceptable use guide can sit alongside existing policies or be its own poster-style policy. Focus on:

• Data privacy and security

• Acceptable and prohibited uses

• Training and education

Keep it simple enough that staff actually read and use it.

Dos and don’ts

Practical guidelines help different groups understand their part in managing AI risks:

• Boards set the boundaries for acceptable AI uses and update the risk appetite statement as needed.

• Management embeds those boundaries into policies, training and accountability functions such as audit, risk and compliance.

• Operations receive clear, consistent messages and repeat them often, so staff know how to apply AI responsibly in everyday tasks.

Defining AI risk appetite

Clarify the organisation’s appetite for reputational, regulatory and operational risk from AI use. Boundaries give managers confidence to approve low-risk tools and escalate higher-risk ones without delay.

Developing business impact assessment tools

Create a standardised checklist for each AI application. Capture customer impact, financial exposure, bias potential and operational dependencies in one view. This keeps assessments consistent and comparable across business units and teams.

Setting review thresholds

Not every AI tool needs the same level of scrutiny. Define clear criteria for when applications require intensive review (such as customer decisioning or regulatory impact) and when streamlined approval applies, like for internal automation or low-stakes analytics.

Tracking cumulative exposure

Keep a live register of AI deployments and their associated risks. This reveals concentration risks, emerging trends and areas where your organisation’s risk appetite may already be consumed.

Turning high-level principles into repeatable practices like these helps connect board decisions to frontline reality and helps make responsible AI use easier to manage.

How we can help

Navigating AI governance and operational risk can feel complex, but you don’t have to do it alone. We work with boards and leadership teams to translate high-level principles into practical steps that fit their organisation.

Here are a few ways we can help you:

• Risk appetite expert assistance in framework structure uplift and update, in accordance with industry best practice and shifting regulatory expectation benchmarks.

• Business unit and enterprise-wide / Board level appetite workshop facilitation.

• Artificial intelligence expert status review, outlook, strategic guidance and appetite articulation from a global leadership perspective.

Contact our team today to see how we can support you.

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