Representative Engagements

Client Scale

Regional water utility deploying 80,000 smart meters across 80,000 households in metropolitan network serving critical infrastructure operations

Challenge

Legacy metering infrastructure required modernization to support real-time data analytics and operational efficiency. Existing systems lacked integration capabilities and scalability for large-scale smart meter deployment. The organization needed comprehensive architecture to transition from manual meter reading to automated near-real-time data operations while maintaining service continuity across 80,000 households.

Approach

Led architecture and strategy for 80,000 smart meter solution deployment across 80,000 households. Designed cloud-native data architecture with secure IoT integration patterns for large-scale meter data ingestion. Implemented batch ingestion pipelines to AWS and Snowflake data warehouse for analytics and reporting. Established data governance framework aligned with utility regulatory requirements. Deployed secure landing zone with multi-project isolation and IAM controls. Additionally authored the smart metering strategy for the New Zealand government's national water reform programme, defining technical framework and implementation roadmap for nationwide water infrastructure modernization.

Outcome

Delivered measurable improvements across operational metrics:

• Successfully deployed architecture supporting smart meters across 80,000 households
• Enabled real-time leak detection for water household usage
• Designed unified data model for smart water metering data
• Defined national smart metering strategy for NZ government water reform

Client Scale

Major university in Sydney with hybrid Azure and AWS infrastructure supporting research and education across 10 core applications

Challenge

Inconsistent security controls across Azure and AWS environments created vulnerability gaps. Flat network architecture prevented workload isolation and lateral movement protection. Organization required unified security posture supporting research collaboration while protecting sensitive data and intellectual property across diverse application portfolio.

Approach

Built comprehensive threat model identifying attack vectors, data flows, and trust boundaries across 10 core applications. Implemented micro-segmentation across Azure and AWS using Zero Trust principles with application-level isolation. Deployed unified identity and access management with conditional access policies. Established security monitoring with automated threat detection and response. Created secure collaboration zones for research data sharing with granular access controls.

Outcome

Enhanced security posture with quantified risk reduction:

• Implemented micro-segmentation across 10 core applications
• Reduced attack surface through network isolation
• Built entire threat model covering attack vectors and data flows
• Enabled secure research collaboration with partner institutions
• Passed external security audit with zero critical findings

Client Scale

Critical infrastructure operator with industrial control systems and IoT sensor networks

Challenge

Legacy operational technology (OT) systems lacked modern security controls and network segmentation. Increasing connectivity requirements for IoT analytics created cyber-physical security risks. Organization needed secure OT/IT integration supporting operational visibility while maintaining safety-critical system isolation and compliance with ISA/IEC 62443 standards.

Approach

Designed defense-in-depth architecture with Purdue Model network segmentation. Implemented secure data diodes for unidirectional OT-to-IT data flow. Deployed industrial DMZ with protocol inspection and anomaly detection. Established continuous monitoring aligned with NIST SP 800-82 R3 guidance.

Outcome

Secured industrial operations with measurable risk reduction:

• Implemented network segmentation across 5 Purdue levels
• Achieved ISA/IEC 62443 compliance for critical systems
• Reduced OT security incidents by 90% through monitoring
• Enabled secure IoT analytics without compromising safety systems
• Maintained 100% operational uptime during implementation