1. Start with architecture
Clarify business context, trust boundaries, critical assets, and assumptions early so control decisions are anchored in the system rather than bolted on later.
How I work
Security that improves design and delivery, not just oversight.
My approach combines architecture, threat-led thinking, and delivery integration so security becomes part of how teams build, operate, and scale systems.
2. Threat model what matters
Use threat modeling to expose meaningful attack paths, align stakeholders, and drive prioritized actions that engineering teams can absorb.
3. Operationalize with DevSecOps
Move from recommendations to repeatable enforcement using pipeline controls, policy-as-code, automation, and measurable security feedback loops.
4. Build cloud guardrails
Strengthen identity, deployment patterns, infrastructure reviews, and monitoring so cloud adoption remains scalable without excessive security debt.
5. Prepare for modern application risks
Apply the same rigor to APIs, mobile, SaaS integrations, and AI-enabled workflows, especially where new capabilities create new trust and abuse boundaries.
6. Keep governance actionable
Translate security into language stakeholders can act on, with risk framing, evidence, accountability, and design guidance that supports delivery rather than blocking it.
Threat modeling as a leadership tool
Threat modeling is not just a workshop exercise. Used well, it becomes a bridge between architecture, engineering, and risk conversations. It helps teams decide what must be defended, where controls should live, and which tradeoffs are acceptable.
AI security with practical scope
AI security should be treated as part of system security, not a separate novelty. Model usage, prompt abuse, data exposure, pipeline integrity, and service isolation all sit within the same broader architecture discipline.