I stood up the first production ML pipeline inside a top-5 US bank's Security Operations floor in 2018 - years before GenAI reached the cyber lexicon. The UEBA scoring, adaptive detection, beaconing activity, and the MLOps disciplines I shipped still run against production traffic. That eight-year proof-of-durability is what I bring to conversations about what "production AI" actually costs to earn and to keep.
my name is Orion.
I am an AI-First Operator who ships frontier-model systems into regulated enterprise production. Eight-plus years across agentic-AI architecture, Responsible-AI Governance, ML-in-production for enterprise SecOps, multi-model orchestration, and hands-on practice building for AI-first delivery organizations.
my work in cybersecurity- I ship, benchmark, and govern agentic-AI systems in live enterprise traffic, and I set the model-selection policy from continuous evals against security-domain requirements.
- I have built the Responsible-AI operating rules that let regulated executives sleep: mapped to NIST AI RMF, ISO/IEC 42001, OWASP LLM Top 10, and MITRE ATLAS.
- I carry eight years of production Machine Learning experience where it matters. I stood up the first bank-side ML pipeline at a top-5 US bank in 2018, and I have been shipping through every wave since - GenAI, agentic orchestration, and the current MCP and A2A integration frontier.
- I compress weeks of enterprise analysis into minutes with frontier-model RAG: my stacks index breach telemetry, adversary intelligence, and internal signal into board-grade briefs in under 60 seconds.
- I read, benchmark, and price the frontier. Claude, Gemini, GPT, Llama, Qwen, Kimi, and every model that ships in between. I do the evals so my clients do not have to guess which model deserves the routing tomorrow.
- I lead and scale AI-first delivery organizations from a blank page: I built a 115-person multidisciplinary team across three continents, and have achieved external recognition for doing so.
- I live inside the stack I sell: I have built my own multi-agent Operating System - with over 100 named agents, each operating its own charter, workflows, and integrations - acting as an 'outsourced' second brain for my daily routines. Nothing on this page is theoretical to me.
A summary of my work with AI
I authored the bill of materials that made a global AI-first practice work. Service catalog, pricing, SOW templates, RFP and RFI framework, governance rhythm, capability ladders, and the AI-First strategic narrative that converted tool-bullet pitches into board-level outcome conversations. I ran the practice across NA, EMEA, and APAC - recognized externally by ISG as a Product Challenger and Contender.
I shipped a cybersecurity-focused, agentic-AI platform into production. It ingests live telemetry from CrowdStrike, Microsoft Sentinel, Splunk, and customer SIEMs, and routes it through specialized LLM gates and guardrails before a human sees anything. Built under an AI-First Secure SDLC, dependency scanning, analyst case notes, and red-teaming prior to promotion. The goal? Helping customers to get more value from their existing software licenses.
I have authored Responsible-AI Governance frameworks that operate safely and pass regulator scrutiny. I've mapped requirements to NIST's AI Risk Management Framework, ISO/IEC 42001, OWASP Large Language Model Top 10, and MITRE Adversarial Threat Landscape.
I run frontier-model benchmarking as a discipline, not an anecdote. I evaluate open and closed source models against production security-domain corpora, and I re-run the evals when a new model ships. The output is a model-selection policy ranked by quality, cost, latency, and risk profile that my clients can trust to route inference to the right model at the right moment.
I've compressed analytical cycles by more than half with AI. GenAI-powered post-incident response assistants that synthesize forensic telemetry and analyst inputs into stakeholder-ready timelines. 60%+ reduction in post-incident reporting cycles. ~40% operational efficiency gains.
I served as security architect for Microsoft 365 enterprise transition and Google Cloud programs at top-5 US bank scale. That work established the cloud IAM patterns, observability pipelines, and detection coverage.
I have been trusted-advisor to CxOs and Boards on AI transformation. The work runs from AI opportunity identification and investment sequencing through post-incident disclosure and regulatory strategy - including chairing a $30M multi-site transformation across 23 enterprise locations.
My own AI Operating System is the internal proof that the AI disciplines I advise are the ones I operate against every day. I do not talk about agentic systems in the abstract; I talk about the one that just handed me insight on what comes next.