whoami
13+ years across application, infrastructure, and cloud security. Threat modeling, red teams, secure code review, vulnerability programs. AI/ML systems are where I spend most of the recent work.
Thirteen years on the security side of the house. Application code first, then infrastructure, cloud, the boring-but-load-bearing operational work, then, like everyone else who didn't see it coming, AI. The Data Science M.Tech from BITS Pilani is what lets me read the ML math behind the security headlines. The rest came from doing the job in places where the cost of a wrong call is real.
ls ./practice
Eight areas built over 13+ years. They cluster, method, offense, program, infrastructure, and the writing reflects how.
STRIDE, PASTA, attack trees, plus the five-zone method I use for agentic AI. Picking apart how a system can fail before someone else does.
Manual and tool-assisted. Web, API, mobile. Findings tied to OWASP risks with reproducible POCs, not scanner output dumped into a report.
Source-level audits with reproducible POCs and remediation patches. Best paired with a threat model, so the review focuses where the model says risk lives.
Building the program, not running the scanner. Triage, ownership, SLA design, the boring metrics that move the needle.
Red-team and purple-team exercises. Includes adversarial testing of LLMs and agentic systems. The EchoLeak-class chains classical pentests don't catch.
AWS, Azure, GCP. IAM, segmentation, data, runtime, CI/CD. Findings calibrated to the actual workload, not a CIS checklist.
Pipeline integration. SAST, SCA, IaC tool selection. Security gates that don't break the build. Practical adoption sequences for teams of 10 or 1000.
MAESTRO-aligned reviews. Training pipeline integrity, model supply chain, inference-time risk. Data poisoning, model inversion, evasion, covered end to end.
tail -n 3 ./writing
Long-form notes from the field. Threat modeling, red teaming, the places where classical security thinking stops working.
STRIDE was built for deterministic systems. Agentic AI breaks its core assumptions. Here is a five-zone method that actually finds EchoLeak-class attacks.
Read articlePrompts are payloads. Why classical red-teaming misses LLM-native attacks, and how to design adversarial tests that surface jailbreaks, tool misuse, and goal hijack.
Read articleIoT devices fail open by default. A pragmatic checklist for hardening firmware, networks, and lifecycle management against the threats most teams overlook.
Read article