How AI and Intelligent Automation can Support CPS 234 Readiness: Practical Use Cases and Evidence

Last updated: January 2026

Introduction

APRA’s CPS 234 standard focuses on information security capability and the strength of the control environment, rather than mandating specific technologies.

In practice, CPS 234 requires regulated entities to demonstrate that security processes are operating consistently, that risks are understood and managed, and that evidence exists to support those claims. Where many organisations encounter difficulty is not in defining policies, but in executing security processes reliably over time and assembling defensible evidence without excessive manual effort.

AI and intelligent automation can support CPS 234 readiness when applied with discipline. Used appropriately, they help standardise execution, reduce operational friction, and improve the quality and accessibility of evidence. Used without clear boundaries, they can introduce opacity, uncontrolled data movement, and uncertainty around how decisions were made. The difference lies in how these technologies are applied, what tasks they are trusted with, and how outcomes are recorded and reviewed.

This article explains where AI and intelligent automation can genuinely support CPS 234 outcomes, what evidence APRA typically expects to see in practice, and the characteristics of tools that are suitable for APRA-regulated environments.

Who this guidance applies to

This guidance is written for APRA-regulated Australian entities, including banks, insurers, and superannuation funds, as well as for technology, risk, and security teams responsible for delivering information security processes that must be repeatable and auditable. It is also relevant for service providers supporting regulated entities where platform choices affect access control, data handling, and evidence quality.

When this is relevant

This article is most useful when organisations are strengthening CPS 234 readiness, improving the consistency of security operations, preparing for assurance reviews, or selecting tools suitable for APRA-regulated environments where auditability and operational predictability are required.

In short

AI and intelligent automation support CPS 234 most effectively when they improve the reliability of security processes and the quality of evidence produced. The safest pattern is deterministic automation for execution, combined with AI used to assist analysis, summarisation, and prioritisation under defined human oversight. In APRA-regulated environments, tools should be selected based on their ability to integrate with enterprise identity, generate exportable audit logs, operate predictably in production, and keep data handling within clearly defined boundaries.

If you are selecting platforms, see our guide to intelligent automation tools suitable for APRA-regulated Australian environments: HERE

If you are not APRA-regulated, see our guide to intelligent automation tools for Australian businesses (2026): HERE

For Defence industry contexts, see intelligent automation tools for Australian DISP member organisations (2026): HERE

CPS 234 in operational terms

CPS 234 is often discussed at a policy or governance level, but readiness is determined by operational reality. APRA expects regulated entities to maintain information security capability commensurate with their size and risk profile, and to demonstrate that security controls are effective over time. This expectation translates into recurring activities such as maintaining accurate information asset registers, managing and reviewing access, monitoring security events, testing controls, and responding to incidents in a timely and documented manner.

These activities are ongoing rather than episodic. When they rely heavily on manual processes, they tend to degrade. Reviews become inconsistent, evidence is scattered across systems, and incident response becomes difficult to reconstruct. Intelligent automation supports CPS 234 by turning these activities into repeatable workflows with defined ownership, clear escalation paths, and retained evidence.

For the formal requirements, see APRA Prudential Standard CPS 234 (Information Security)

Where AI and intelligent automation add value under CPS 234

Information asset identification, classification, and review

Information asset registers and data maps evolve continuously as systems change, integrations are added, and vendors update their services. Many organisations struggle to keep these artefacts current outside of audit cycles. Intelligent automation supports CPS 234 by establishing a regular cadence of review, with workflows that prompt asset owners to validate classifications, confirm data handling, and attest to changes.

AI can assist by summarising what has changed since the previous review, highlighting inconsistencies, or grouping related assets for more efficient assessment. The primary control outcome is not the AI analysis itself, but the retained evidence that owners reviewed and approved asset information, including timestamps, decisions, and exceptions.

Access control and access reviews

Access reviews are a common pressure point in CPS 234 assessments due to their scale and manual effort. Automation improves consistency by defining review schedules, approval steps, escalation rules, and completion tracking across systems. This shifts access reviews from ad hoc exercises to a predictable operational process.

AI adds value when it supports reviewers rather than replacing them. For example, it can summarise outliers such as dormant accounts, elevated privileges, or access patterns that deviate from expected roles. Evidence should clearly show what access was reviewed, who approved or rejected it, what exceptions were raised, and how changes were implemented.

Control testing and assurance workflows

CPS 234 requires organisations to test the effectiveness of information security controls. In practice, this often fails due to inconsistent scheduling, unclear ownership, or weak documentation. Intelligent automation can coordinate control testing by assigning responsibilities, capturing results, triggering remediation actions, and recording approvals for closure.

AI can assist by summarising results for reporting and helping control owners focus on material issues. The evidence that matters includes test plans, test outcomes, remediation actions, and sign-off records that demonstrate controls are being actively managed rather than assumed to be effective.

Security logging, alert triage, and operational response

Security teams frequently contend with high alert volumes and limited context. Automation supports CPS 234 expectations by standardising triage steps, enriching alerts with contextual data, routing issues appropriately, and recording actions taken. This improves both response quality and evidence consistency.

AI can assist analysts by summarising alert context or correlating related events to support investigation. In regulated environments, the emphasis should remain on traceability. AI should inform human judgement, while workflows and logs record what actions were taken and when.

Incident response coordination and the 72-hour window

During security incidents, coordination and documentation often prove more challenging than technical remediation. CPS 234 heightens this challenge by introducing expectations around timely notification of material incidents.

Intelligent automation supports incident response by orchestrating runbooks that coordinate tasks, communications, and evidence capture in a structured manner.

AI can assist by drafting internal situation reports, summarising timelines, and supporting post-incident reviews. The system of record, however, should remain the incident workflow and associated logs, which preserve a defensible chronology of decisions, actions, and notifications.

Third-party evidence collection and service provider oversight

CPS 234 extends to information assets managed by service providers. Evidence collection is frequently fragmented, with assurance artefacts dispersed across emails and file shares. Automation supports CPS 234 by treating third-party assurance as an ongoing process, prompting collection, routing documents for review, tracking approvals, and maintaining a current register.

AI can assist reviewers by summarising vendor reports and highlighting sections relevant to the organisation’s risk profile. The retained evidence should include review outcomes, risk decisions, expiry dates, and artefact history.

Where AI helps under CPS 234, and where it should be constrained

AI introduces CPS 234 risk when it obscures decision-making, expands data exposure, or produces outputs that are treated as authoritative without review. Common risk patterns include sensitive information being passed through prompts or external integrations, probabilistic outputs being mistaken for evidence, and insufficient logging of AI-assisted activities.

These risks are mitigated by clear boundaries. Execution of security processes should remain deterministic and auditable. AI should be limited to assistive roles such as summarisation, drafting, and prioritisation, with human review embedded where outcomes are material. Organisations should be explicit about what data AI systems can access, how outputs are used, and what is retained.

Privacy and data handling obligations should also be considered alongside CPS 234, including alignment with the Australian Privacy Principles published by the Office of the Australian Information Commissioner

What good CPS 234 evidence looks like in practice

Effective CPS 234 evidence allows an organisation to reconstruct what happened without relying on informal knowledge. In practice, this includes access review records, control testing artefacts, incident timelines, and third-party assurance registers that are timestamped, attributable, and retained according to policy.

Automation should reduce the effort required to assemble this evidence, not increase it. A useful test is whether an independent reviewer could understand how a control operated and what decisions were made using the records alone.

Tool characteristics suitable for APRA-regulated environments

CPS 234 does not mandate specific tools, but APRA-regulated environments consistently require certain characteristics. Tools should integrate with enterprise identity and access management, support role-based access control and separation of duties, and produce reliable, exportable audit logs. Execution should be predictable, with recoverability and resilience appropriate for the criticality of the process.

Where AI features are used, organisations should be able to control data handling, understand where processing occurs, and ensure that AI outputs are treated as inputs to human decision-making rather than unreviewed outcomes.

These considerations also intersect with broader operational resilience expectations under APRA Prudential Standard CPS 230 (Operational Risk Management)

Conclusion

CPS 234 readiness is primarily about operational discipline and evidence. AI and intelligent automation can support that discipline by making security processes repeatable, reducing manual overhead, and improving the quality and accessibility of records. The most robust approach is to keep execution deterministic and auditable, while using AI to assist humans with analysis and prioritisation under defined controls.

When implemented in this way, AI and automation become part of how the control environment operates and is evidenced, rather than a source of additional risk.

About Arkane Group

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