I work as a platform architect on AI-native security products at we45. My
projects sit between security engineering, LLM systems, developer experience, and product architecture: the goal is
to make security review workflows feel native to how teams already design, build, and ship software.
SecurityReview.ai is an AI-native security architecture review platform. It converts architecture documents,
diagrams, tickets, code, and engineering notes into system-specific threat models, countermeasures, and
compliance-ready reports. My role has focused on the platform architecture: shaping how messy engineering context
becomes structured security ground truth, how specialized agents reason over it, and how the output stays useful
for both security teams and builders.
Technical Architecture
Designed multi-modal ingestion for PDFs, diagrams, markdown, JSON, voice notes, and code artifacts.
Built code-profiling workflows with agents for architecture, APIs, data stores, dependencies, IaC, and existing controls.
Architected an agentic threat-modeling harness using category-specific reasoning, live threat intelligence, and implementation-aware countermeasures.
Mapped findings into OSCAL-backed compliance structures so reports connect engineering evidence to framework subsections.
Product Experience
Focused the workflow around how teams actually review systems: upload context, inspect assumptions, refine scope, and generate review artifacts.
Balanced security depth with legibility so outputs work for architects, application security engineers, and product teams.
Designed review outputs to move from abstract risks to concrete implementation guidance and defensible compliance evidence.
VibeReview is a guardrail platform for AI-assisted development. It profiles repositories, generates
threat-informed rules, and delivers that guidance into the tools where AI coding happens: IDEs, pull requests,
MCP-enabled workflows, GitHub, and GitLab. The product is built around a simple idea: AI coding assistants should
understand the security shape of the system before they suggest changes.
Technical Architecture
Architected repository profiling that extracts compact system context from code, dependencies, APIs, data flows, and existing security controls.
Designed rule-generation workflows that translate system profiles into practical, threat-informed guardrails for AI coding tools.
Built integration patterns for MCP, GitHub, GitLab, and IDE delivery so guidance can appear close to the developer's change loop.
Focused on keeping guardrails specific to the repository instead of relying on generic secure-coding checklists.
Product Experience
Designed the platform around developer ergonomics: low-friction onboarding, understandable rules, and review guidance that fits PR workflows.
Connected security architecture context to day-to-day coding decisions, especially when developers are using AI agents or copilots.
Shaped VibeReview as a bridge between application security intent and the fast, iterative style of AI-assisted software development.