Yogesh S. Thanvi#
Cloud Security & DevSecOps | Securing Internet-Scale Infrastructure#
I build and validate the systems that keep large-scale distributed infrastructure reliable, secure, and continuously compliant. Fourteen-plus years in cloud and edge security, DNS and global traffic management, Kubernetes platform security, and DevSecOps governance, paired with peer-reviewed research and active leadership in the global cybersecurity profession through ISACA.
My current focus: engineering trust into modern systems, so that compliance is a property systems prove continuously, not a periodic activity they survive.
About me · Publications · Speaking · Service & Leadership · Writing
Cloud security. DevSecOps governance. Continuous compliance. AI risk.
Cloud Security & DevSecOps engineer securing internet-scale distributed infrastructure. Fourteen-plus years in cloud and edge security, DNS and global traffic management, Kubernetes platform security, and DevSecOps governance, paired with peer-reviewed research and active leadership in the global cybersecurity profession through ISACA.
Detecting insecure pods on Azure Kubernetes Service is not the same as preventing them. Here is how Azure Policy and OPA Gatekeeper move enforcement to admission time, so a misconfigured workload never reaches the cluster.
In cloud-native and AI-driven systems, compliance can no longer be a periodic activity. It has to be continuously demonstrated. Here is the architecture for engineering that trust.
Most Cloud Security Posture Management tools detect misconfigurations and stop there. Detection without enforcement leaves the exposure in place. Here is what changes when policy-as-code blocks and remediates at admission time.
Every production incident is a lesson the system has already paid for. Most teams capture that lesson in a postmortem document and then reintroduce the same failure months later. Here is how to convert incidents into automated controls that make a failure mode impossible to repeat.
Generative AI on Azure introduces control points that traditional application security never had to handle. Here are practical governance patterns for Azure OpenAI and AI Foundry workloads, mapped to where the real risks live.