An engineering approach to risk.
Risk engineering applies technical and analytical methods to identify what could go wrong, evaluate the consequences, and design ways to reduce harm and business loss.
risk.engineer explores that practice across cyber, third-party, and organizational risk. Through writings, tools, and working labs, I’m developing a methodology for understanding systems, testing assumptions, and making better risk decisions.
Risk lives in the connections.
Explore what a business service depends on.
Software delivery
A release depends on build infrastructure, signing credentials, and an accountable approval process. Those connections give technical evidence business meaning.
Illustrative relationships—not live telemetry or a risk assessment. Drag to rearrange; changes are not saved.
Why risk.engineer?
How does a system fail? How far could the consequences reach? Which intervention would help, and how would we know? These questions connect risk management to engineering: model the problem, examine uncertainty, test an approach, and learn from the result.
My focus is the full risk lifecycle: identification, evaluation, response, monitoring, and communication. AI, software, data pipelines, probability models, and simulation are methods to explore within that practice. Lab 01 starts with continuous identification; future work will investigate the other parts.
The broader field provides foundations in hazard analysis and loss prevention, alongside risk analysis and safety engineering courseware. This publication builds on those foundations through practical questions from cyber risk and GRC.
GRC. GRC engineering. Risk engineering.
- GRC establishes the practice.
- Governance, risk management, and compliance help an organization set direction, understand uncertainty, and meet its obligations.
- GRC engineering makes work executable.
- Code, APIs, and automation can make evidence collection and control checks repeatable and easier to inspect.
- Risk engineering applies engineering methods to risk decisions.
- Understand dependencies and failure paths, evaluate uncertainty and consequences, design and test responses, and explain the tradeoffs. Its methods can draw on software, statistics, simulation, and systems engineering.
Start exploring
Read: Building an AI-first TPRM program →
Read: GRC engineering is more than automation →
Latest writing
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Start with your business, then assess the vendor #TPRM#Business context Business dependencies, access, data, and recovery options should shape where a third-party review goes deeper.
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The risk lifecycle needs a continuous evidence pipeline #Risk engineering#Data pipelines#AI How connected evidence, business context, and human review can keep risk identification useful as systems change.
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Risk changes continuously. Our understanding should too. #Risk engineering#Risk identification#Data pipelines Why I’m building a pipeline that revisits risk scenarios as systems, vendors, and business dependencies change.