Cybersecurity as a discipline: threat modeling, architecture, and business risks
Moving from chaotic software procurement to systematic cyber resilience through threat modeling, architectural controls, and Security by Design principles.
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Practical guidance on management accounting, budgeting, treasury, period close, financial controls and evidence-based AI.
64 publications
Moving from chaotic software procurement to systematic cyber resilience through threat modeling, architectural controls, and Security by Design principles.
Transitioning to a Data Mesh architecture resolves integration chaos through data decentralization, data contracts, and moving away from fragile point-to-point connections.
A practical analysis of architectural patterns for connecting legacy systems to modern ecosystems: how to avoid point-to-point chaos and why RPA is only a temporary solution.
Why ignoring organizational principles, lacking data owners, and "big bang" migrations doom corporate integration, and how to build a managed architecture.
The success of AI initiatives depends on data lineage transparency and Data Governance maturity. Learn how to avoid poor data quality traps and prepare enterprise IT architecture for AI scaling.
How to transition from fragile point-to-point connections to a managed integration layer using API gateways and schema registries to ensure enterprise system stability.
Comparing Kafka and RabbitMQ for building reliable event-driven architectures. Learn how to avoid integration issues and ensure data integrity.
The transition to decentralized architectures and AI integration has revealed a critical bottleneck: the lack of clear data ownership undermines the reliability of enterprise systems.
Comparing architectural approaches: when direct connections become inefficient and how implementing an integration bus helps avoid complexity during scaling.
Transitioning from point-to-point integrations to standardized EIP patterns, API Gateways, and managed Master Data Management to preserve corporate data integrity.
How to combine the flexibility of AI with the rigid logic of BPMN 2.0 and DMN to automate first-line request processing without security risks or operational chaos.
Process Mining methodology reconstructs real task flows using digital footprints, revealing hidden bottlenecks and unauthorized workarounds invisible to management.