Guru Krish
ex-Google · ex-Amazon · Austin, TX · U.S. Citizen

Guru
Krish

Enterprise AI Transformation | AI Platforms | AI Security

$700M+
Business impact enabled
150+
Business units governed
18 yrs
Google & Amazon
$30-50M
AI portfolio managed
krish@gurukrish.org LinkedIn Work With Me →

"I help enterprises move from AI experimentation to measurable business outcomes — by building reusable platform architectures, operating models, security guardrails, and governance mechanisms that scale AI responsibly."

Trust by Design

An enterprise AI transformation framework — and a live platform that generates risk assessments, architecture decisions, and security threat models for any use case in 60 seconds.

The Trust by Design Framework
01
BUILD
AI-native systems, platform architecture, agentic AI patterns, LLM gateways, RAG pipelines, and implementation guidance — built, not just designed.
02
GOVERN
AI operating models, risk classification, intake frameworks, portfolio prioritization, financial governance, and executive alignment.
03
SECURE
Threat modeling, guardrail design, prompt injection defense, agentic AI security, data protection, and compliance by design.
04
MEASURE
AI FinOps, productivity baselines, ROI measurement, evaluation frameworks, and the metrics that prove AI is actually working — not just deployed.
NIST AI RMF AI FinOps Trust by Design Agentic AI LLM Gateways RAG Architecture
Hard-Won Lessons

Three things most enterprise AI programs get wrong

On Governance
01

"Governance that nobody uses is just overhead. The gateway has to be easier than the workaround."

At Google, Risk Inspector succeeded because compliance became the path of least resistance — not because we mandated it. At Amazon, adoption followed when we removed friction, not when we added policy.

On Transformation
02

"The platform scales. The headcount doesn't have to."

A centralized LLM gateway governing 600 engineers costs the same to operate whether you have 3 solutions engineers or 18. Build the mechanism first, then scale the team into it.

On AI Systems
03

"Fix the pain before you ask for adoption."

7-week access backlogs at Amazon. 3-month privacy reviews at Google. In both cases, adoption followed from removing friction — not from training sessions or mandates.

Case Studies

Real programs. Real outcomes.

Google — 3P Security & Privacy

Risk Inspector: Governing 150+ Business Units at Scale

Walked into a company-wide Salesforce crisis — $700M in platform value blocked overnight. Designed 12 security and privacy controls, built Risk Inspector as the mandatory governance platform, and unblocked the ecosystem.

150d→10h Partner onboarding time
$500M+ Ecosystem value unlocked
3,700+ Licenses eliminated
Amazon — Security Data Intelligence

Security Scorecard V2: End-to-End PM Ownership from Discovery to Ship

Full PM cycle — customer discovery interviews, usage analysis via log queries, demo ownership, gap synthesis, and V2 strategy delivered into OP1/OP2 planning. Findings adopted by engineering and shipped. Scorecard adoption increased 40% post-launch across 70%+ of engineering teams.

Discovery→Ship Full PM cycle owned end-to-end
40% Scorecard adoption increase post-launch
70%+ Engineering teams on standardized risk model
Google — AI/ML Strategy

Enterprise AI Operating Model: 14 Verticals, $50M

Built and ran the AI/ML strategy program across 14 Google corporate verticals — intake framework, financial impact model with Finance, SteerCo with 20+ Directors and SVPs, OKR alignment across 50+ programs.

50+ AI programs governed
$30-50M Portfolio managed
14 Corporate verticals
Frameworks & Playbooks

Reusable architectures for enterprise AI

Transformation Platform

Trust by Design

An Enterprise AI Transformation Platform for scaling AI from prototype to production through reusable architectures, operating models, AI security guardrails, governance patterns, evaluation frameworks, and implementation playbooks.

Read the Whitepaper
Transformation Playbook

Enterprise AI Transformation Playbook

From early traction to durable scale — the operating model, platform architecture, intake framework, and scaling flywheel that makes solution #50 as governed and reliable as solution #5. Coming soon as a published document.

Read the Playbook
Working AI Systems

Built, not just designed

Research & Applied — Amazon

LLM Data Access Pipeline — Amazon

Owned end-to-end — problem definition, build, stakeholder alignment, adoption program, and outcome measurement. Built an LLM classification pipeline on AWS Bedrock to analyze security access request tickets — Python API to extract ticket data, stored in S3, classified in Jupyter Notebook using prompt engineering to identify what data customers were requesting and why. Used findings to: build new features in the Shepherd security product, create a new data access policy published internally, design an optimized data access request workflow, and reduce engineering oncall burden. Ran weekly office hours, trained the data team oncall, personally took ownership of access approvals during transition. Resolution time: 7 weeks → 1 week.

AWS Bedrock Python S3 Jupyter Prompt Engineering Shepherd Security
Trust by Design — Live Demo

Enterprise AI Risk Assessment Tool

Describe any AI initiative your organization is considering. Get an instant risk tier, architecture recommendation, governance requirements, regulatory flags, AI FinOps cost estimate, and 90-day implementation roadmap — in 30 seconds. Built on Claude API with enterprise AI frameworks encoded: NIST AI RMF, OWASP LLM Top 10, SOX, GDPR, ISO 27001. This is what I built manually at Google for 50+ AI programs over two years. Now it runs in 30 seconds.

Claude API NIST AI RMF OWASP LLM Top 10 Agentic AI AI FinOps Enterprise Architecture
Request Live Demo →
Trust by Design — Live

AI Governance Intake Classifier

Built an AI-augmented governance intake system that takes a plain-text business use case, classifies risk tier (Low / Medium / High / Critical) using the Trust by Design framework, generates tailored discovery questions, and routes to the right approver — all in under 30 seconds. Architected on n8n's native AI Agent node (LangChain-based), with the risk policy pulled dynamically from GitHub at runtime for clean separation of concerns. Refactored from a brittle 13-node HTTP request pattern to a stable 4-node native agent architecture. Eliminates manual intake triage that previously took days.

n8n Claude API LangChain Prompt Engineering GitHub
How it works →
In Progress

Agentic AI Research Workflow

Multi-step research agent that takes a governance question, retrieves relevant information from a knowledge base, synthesizes findings, and returns a structured recommendation with sources and confidence level.

LangGraph Claude API RAG Vector DB

→ Live demo coming soon

Google — AI/ML Strategy

Enterprise AI Portfolio — Google SteerCo

Built and ran the operating model for Google Corporate Engineering's AI/ML program — intake framework, financial impact model, and prioritization framework across 50+ programs and 14 verticals. Led monthly SteerCo with 20+ Directors and SVPs. Portfolio represented $30–50M in AI investment.

Portfolio Management LLM Governance Executive Reporting
Work With Me

Engagements grounded in the Trust by Design framework

Most AI programs fail not because the model is wrong — but because the architecture, governance, security, and measurement weren't built around it. That's exactly what Trust by Design fixes.

AVAILABLE FOR CONSULTING ENGAGEMENTS ALONGSIDE FULL-TIME ROLE SEARCH

BUILD · 4–6 Weeks

Architecture Sprint

We design your enterprise AI platform architecture end-to-end — LLM gateway, RAG pipeline, agentic AI patterns, guardrail layer, and evaluation framework. You leave with a production-ready architecture decision package, not a slide deck.

Deliverable

Architecture decision record, component spec, guardrail design, and 90-day implementation plan — grounded in your use case, not generic patterns.

GOVERN · 2–3 Weeks

AI Readiness Assessment

We assess your AI portfolio against the Trust by Design risk framework — intake scoring, risk tiering, governance gaps, compliance mapping across NIST AI RMF, EU AI Act, HIPAA, and GDPR. You leave with a prioritized roadmap and governance operating model.

Deliverable

Risk tier report, governance gap analysis, compliance mapping, and a prioritized 90-day remediation roadmap.

SECURE · 2–4 Weeks

Threat Model & Guardrail Design

We threat-model your AI system against OWASP LLM Top 10 and design the guardrail architecture specific to your system — not generic controls. Prompt injection defense, agent permission boundaries, data protection controls, and zero-trust principles built in.

Deliverable

OWASP-mapped threat model, guardrail specification, and security control implementation guide tied to your specific architecture.

MEASURE · Ongoing

AI FinOps & Measurement Baseline

Most organizations deploy AI and then guess whether it's working. We define the measurement baseline before deployment — productivity metrics, cost per successful outcome, model routing optimization, and the 30/60/90-day milestones that prove ROI to your CFO and board. Built from the same FinOps framework used to govern $30–50M in AI investments at Google.

Schedule a 30-Min Conversation →
Let's Talk

Open to Director and VP roles in Enterprise AI Transformation

Austin-based. Open to remote & relocation. Focused on Enterprise AI Transformation, AI Platforms, AI Strategy, and AI Security roles.

krish@gurukrish.org LinkedIn GitHub