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Jeevan-0508/README.md

Jeevan Siddhabhaktula — AI governance, risk, and decision intelligence

Portfolio · LinkedIn · Email

Building systems that make risk visible

I’m a former Amazon Risk Manager and Program Manager focused on AI governance, AI risk, GRC, responsible AI, fraud intelligence, and decision forensics.

My work sits between operational risk and engineering: practical tools, explicit evidence, honest boundaries, and interfaces that help people make better decisions.

AI GOVERNANCE · RISK INTELLIGENCE · FRAUD SYSTEMS · GERMANY / EU

The portfolio

Six systems. One direction: make consequential decisions easier to inspect, challenge, and improve.

POLICY//AUDIT project visual

POLICY//AUDIT

Governance evidence traced from regulation to controls, implementation, contradictions, and gaps.

LOCAL · DETERMINISTIC
Live demo · Repository 		RISK//SWARM command center

RISK//SWARM

Seven-agent investigation where disagreement, provenance, and red-team review stay visible.

SNAPSHOT DEMO · HUMAN DECISION
Live demo · Repository
Fraud Watch simulation

Fraud Watch

A synthetic freight world that turns evolving behaviour into signals, investigations, and analyst-reviewed outcomes.

SIMULATED · DETERMINISTIC
Live demo · Repository 		RISK//OS command center

RISK//OS

A browser-first operating system for program risk, delivery decisions, controls, and benefits.

LOCAL · OFFLINE-FIRST
Live demo · Repository
RISK//REPLAY decision forensics

RISK//REPLAY

Decision forensics and counterfactual replay for inspecting what changes when evidence or inputs change.

LOCAL API · STATIC DEMO
Live demo · Repository 		AI Governance Control Room

AI Governance Control Room

An operator console for cross-framework coverage, evidence, controls, and governance exposure.

BROWSER-ONLY · CROSS-FRAMEWORK
Live demo · Repository

What I bring

Risk and governance
AI governance · operational risk · GRC · fraud intelligence · internal controls · evidence review · decision challenge 		Technical practice
TypeScript · React · Python · deterministic systems · simulations · data provenance · browser-first tools · testable decision logic
How I work

I separate evidence from claims, coverage from probability, replay from causation, and model output from a human decision. Synthetic data is labelled. Provider failures are visible. The interface should make those boundaries easy to understand.

Background
  • Former Amazon Risk Manager and Program Manager
  • Current focus: AI governance, AI risk engineering, responsible AI, GRC, and risk intelligence
  • Open to relevant roles and collaborations in Germany and the EU

Outside the core

Creative engineering lives in a separate lane: Nightfall Zero, HAKAI World, Scale of Everything, and RuleShift.

Evidence-led systems for consequential decisions.

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  1. All-in-one-desk All-in-one-desk Public

    An offline-first Python productivity and automation suite featuring text tools, duplicate removal, KPI calculations, document conversion, Excel/CSV processing, OCR, flowcharts, text comparison, ema…

    HTML 1