Align
Clarify business priorities, risk appetite, regulatory obligations, board expectations and existing assurance gaps.
Regulatory compliance and assurance
Move beyond control compliance to self-documenting assurance.
Brad works with boards, CDOs, CIOs, risk leaders and business owners to translate agreed obligations into data, AI and operational controls. The objective is a control environment that records evidence as work happens, reducing repeated manual collection and returning leadership attention to core business priorities.
Board alignmentStrategy, risk appetite and accountability
Control architectureObligations translated into data and AI controls
Continuous evidenceProof captured through normal operations
Business focusLess manual compliance overhead for CDOs and CIOs
The shift
Compliance should be an output of the operating model.
Traditional compliance processes often sit beside the systems and teams they are meant to govern. Evidence is reconstructed through spreadsheets, screenshots, tickets, documents and attestations when boards, auditors or regulators ask for it.
Brad's approach connects obligations to accountable owners, policies, processes, data, systems, AI products, controls, results, issues and approvals. That connection is maintained through metadata, workflow and control telemetry.
The result is more than a compliance improvement. The same foundations strengthen data quality, operating resilience, AI governance, decision traceability and enterprise accountability.
How Brad works with leaders
Start with business outcomes. Build the control system around them.
Legal, risk and compliance teams determine the organisation's obligations and interpretation. Brad translates those agreed requirements into a practical data, AI and technology operating model.
Map
Connect obligations and policies to accountable owners, processes, data, systems, suppliers, AI use cases and control objectives.
Instrument
Capture lineage, data quality, access, security, change, workflow, AI lifecycle and service-control results through operational platforms.
Evidence
Retain exceptions, decisions, approvals, remediation and control results so assurance is produced from current records.
Self-documenting compliance
Evidence exists because the control operated.
For each material obligation, maintain a current connection between the requirement and the evidence that proves the control operated.
Manual regime
Reconstruct the answer.
- Separate control registers
- Evidence gathered across systems and documents
- Repeated attestation campaigns
- Lineage rebuilt for audits
- Specialist teams diverted into evidence collection
Self-documenting regime
Query the operating record.
- Obligations mapped to controls and owners
- Data, systems and AI linked to accountable domains
- Automated control results retained
- Lineage and change history preserved
- Evidence assembled from live records
Foundational capabilities
One operating capability, built from connected disciplines.
Data governance & catalogue
Definitions, ownership, policies, critical data and control context.
Master data management
Trusted records for core business entities, customers, suppliers, products and counterparties.
Reference data management
Controlled codes, classifications and shared reference values.
Data quality management
Preventative and detective controls, thresholds, remediation and scorecards.
Metadata, lineage & traceability
Source-to-decision visibility across reports, analytics, AI and operational processes.
Privacy & information lifecycle
Purpose, consent, access, retention, disposal and breach-management evidence.
Responsible AI governance
Inventory, risk classification, testing, oversight, monitoring and supplier assurance.
Evidence management
Reusable proof linking obligations, controls, decisions, issues and approvals.
Board assurance
Measure implementation, control effectiveness and business value separately.
Start with verified baselines. Set targets through risk appetite and business cases. Report trend and exceptions, then schedule independent assurance.
Strategic delivery
Roadmap milestones delivered with agreed outcomes.
Accountability
Critical domains with named owners, stewards, definitions and control responsibilities.
Critical data quality
Critical data meeting defined completeness, accuracy, validity, timeliness and reconciliation thresholds.
Traceability
Material reports, decisions and AI outcomes with verified lineage and control evidence.
Risk remediation
Material data, privacy, lineage and control issues resolved within approved timeframes.
Responsible AI
Production AI registered, risk assessed, approved, tested, overseen and monitored.
Operational outcomes
Customer, service, process and resilience measures tied to business cases.
Value realisation
Financial and non-financial benefits delivered, including avoided manual compliance effort.
Example: financial services
Apply the same model to APRA-regulated environments.
Australian financial services and superannuation provide a useful example because obligations span operational resilience, information security, data risk, accountability, member outcomes, supplier management and AI governance.
Brad works with superannuation leaders to translate frameworks such as CPS 230, CPS 234, CPG 235, SPS 220, SPS 515, the Financial Accountability Regime and APRA's AI expectations into practical data and AI controls.
The industry is an example of the method, not the boundary of Brad's work. The same principles apply wherever leaders need trusted data, governed AI, clearer accountability and stronger evidence without creating more manual compliance work.
Explore the financial services exampleBoard, CDO and CIO sessions
Design compliance so evidence is produced by the business, not reconstructed around it.
Executive briefings and workshops focused on trusted data, governed AI, enterprise architecture, control automation and measurable business value.
Contact Brad on LinkedIn