Australian Financial Services are moving fast with AI and APRA has high expectations.
Most boards haven’t caught up yet. APRA knows this, and it has said so plainly in its letter to the industry. The regulator’s message is clear: existing governance, risk, and operational resilience practices are not keeping pace with how quickly AI is being deployed inside regulated entities. That gap is now a supervisory concern.
This article explains what APRA and Australia’s updated privacy laws require of Financial Services using AI, and what a Quality Assurance strategy needs to look like in response.
APRA Sees Three Governance Failures Happening Right Now
APRA’s concerns are specific and three patterns appear consistently in its observations of the sector.
Boards are making AI decisions without enough information. Many boards rely on vendor presentations to understand their AI risks. That’s a problem when the vendor is also the one selling the product. APRA expects boards to be capable of independent challenge, and most are not there yet.
Governance exists on paper, not in practice. Entities have acknowledged that existing prudential standards apply to AI. Few have actually operationalised that acknowledgement with post-deployment monitoring, change management, and model decommissioning are common gaps.
Supplier dependencies are underexamined. Many FinTechs have concentrated significant AI activity with a single provider without tested exit strategies. When those providers rely on foundation models, training data, or fourth-party services, the chain of accountability becomes opaque. APRA expects visibility all the way through that chain.
APRA frames AI governance inside existing obligations such as CPS 230 for operational risk, outsourcing standards, and the Financial Accountability Regime (FAR). There is no separate AI rulebook. The existing rulebook applies, and APRA believes most entities are not meeting it.
Privacy Law Has Changed. Your AI Systems Probably Haven’t.
The Privacy and Other Legislation Amendment Act 2024 introduced changes that directly affect Financial Services using AI to make decisions about customers.
The most significant change for AI is the new automated decision-making transparency requirement. If your systems use personal information to make decisions that significantly affect individuals, such as loan approvals or credit scoring, customers will have a right to meaningful information about how those decisions are made. The two-year grace period ends on 10 December 2026.
Two other changes carry direct operational implications. First, Australians now have a personal right to sue for serious invasions of privacy. AI systems that process sensitive personal data at scale carry real exposure here. Second, the Office of the Australian Information Commissioner (OAIC) has stronger enforcement powers, including tiered civil penalties and the ability to conduct compliance assessments. The OAIC has already issued specific guidance on commercially available AI products.
APP 11 now explicitly requires “technical and organisational measures” to protect personal information. This is not an IT-only obligation and covers the full range of privacy and security controls around AI systems.
The Regulator Expects Boards to Lead, Not Delegate
Both APRA and ASIC have reached the same conclusion from different directions: Financial Services are adopting AI faster than their governance frameworks can handle it, and boards are too far removed from the risk to provide effective oversight.
APRA’s expectation is that boards understand AI well enough to set strategic direction and challenge assumptions. ASIC’s concern is that licensees are creating consumer harm by deploying AI without updating their risk and compliance frameworks.
The practical implications are straightforward. Boards and executives need sufficient AI literacy to ask hard questions of management and vendors. Accountability for material AI use cases needs to be named, not distributed. Under FAR, accountable persons need to be identifiable for decisions made by or with AI systems. Staff need training that goes beyond “here is the tool” and covers misuse, limitations, and secure practices.
Human oversight and ownership is not optional for high-risk decisions. AI can inform those decisions but a named person needs to own them.
Your AI Systems Need to Fail Safely, Not Just Perform Well
CPS 230, effective from 1 July 2025, extends operational resilience obligations to AI-enabled systems. This creates concrete requirements that go beyond standard performance monitoring.
AI systems supporting critical operations need tested fallback processes. That distinction matters when regulators ask for evidence. AI failure modes that need to be planned for include hallucination, silent degradation, and susceptibility to adversarial inputs such as prompt injection or data poisoning.
Security requirements have also become more specific. AI adoption changes the attack surface with more entry points, faster attack cycles, and new risks from non-human AI agents with system access. APRA expects strong privileged access management, timely patching, hardened configurations, and penetration testing that covers AI-specific vulnerabilities, including AI-generated code.
Data governance sits underneath all of this. The quality and provenance of training data affects model behaviour. That is now a prudential concern.
What an APRA-Ready AI QA Strategy Looks Like
Active supervision of AI is underway with regulators no longer observing and advising. They are assessing and acting. An AI QA strategy needs to be designed for that environment.
The foundation is a centralised AI inventory: every AI system in use, including third-party tools, mapped to the regulatory obligations it touches under CPS 230, FAR, and the Privacy Act. Without this, gap assessments and audit processes cannot function.
From that inventory, four capabilities need to be in place.
Continuous monitoring for bias, drift, and performance degradation. AI models do not stay stable. A model that was accurate at deployment may not be accurate six months later. Automated monitoring catches this before regulators do.
Independent assurance for high-impact systems. Internal audit and external review processes need to cover AI systems with material customer or operational impact. This cannot be delegated to the team that built the system.
Testing frameworks built for AI. Standard software testing does not address algorithmic bias, adversarial inputs, or the behaviour of AI-generated code. Testing frameworks need to evolve to cover these risks explicitly.
Automated compliance tooling. Governance, Risk, and Compliance (GRC) platforms can automate the monitoring and reporting that manual processes cannot keep up with. Predictive compliance tools can identify non-compliant states before they become audit findings. SaaS Security Posture Management (SSPM) tools provide continuous measurement of security controls against regulatory baselines.
The Gap Between Adoption and Governance Closes in One Direction
Regulators are not going to slow down their expectations to match the pace of industry governance. The direction of travel is more scrutiny, not less.
To get ahead of this do three things. Build board-level AI literacy that enables genuine challenge, not just approval. Establish clear ownership for AI decisions at the individual accountability level. Instrument their AI systems for continuous oversight rather than periodic review.
APRA has been explicit about what it is looking for. The question is whether your governance framework reflects what your AI systems are actually doing, not what the documentation says they do.