Leadership & Advisors
Built by people who know governance must work in practice.
Trustethica brings together deep experience across AI governance, technology law, enterprise transformation, international standards, ethics and risk.

Vivegavalen Vadi Valu
Founder · Singapore / Asia-Pacific
AI governance practitioner with 12+ years across technology law, privacy, data governance and AI risk management.
AI Governance · Technology Law · Risk Operations · GenAI & Agentic AI

Arvind Shanmuga
Nathan
Strategic Advisor · Australia
Board chair and strategic advisor connecting emerging technology, commercial strategy and governance.

Dr. Sundaraparipurnan Narayanan
AI Ethics & Risk Advisor · India
Ethics, governance and risk leader with two decades building accountable decision-making and practical AI governance.

Harm Ellens
Trustworthy AI Advisor · Australia
International AI standards leader with 20+ years across the socio-technical, ethical and economic dimensions of AI.
What We Believe
Governance should move at the speed of AI.
01
Purpose before policy
Every deployment begins with a defined business purpose, context and accountable owner.
02
Context over abstraction
AI risk is shaped by where, why and for whom a system operates—not by the model alone.
03
Continuous over periodic
Drift and policy deviation must surface at runtime, not at the next review cycle.
04
Evidence by design
Every governance decision should leave a structured, timestamped and defensible record.
Our Mission
Make enterprise AI governable in the moments that matter.
We give boards, regulators, risk leaders and engineering teams a shared line of sight—from authorised intent to live behaviour to enterprise impact.
Why We Exist
The model is not the whole risk. Context is.
Enterprises already monitor infrastructure, model performance and regulatory posture. Yet one critical question remains unanswered in production: is this AI use case still operating within the purpose it was authorised to serve?
Trustethica was founded to close that gap. We make authorised purpose observable at runtime, translate AI behaviour into operational, conduct, regulatory and reputational risk, and create evidence leaders can defend.
AI governance must operate at the layer where business purpose meets runtime behaviour, anchored to the enterprise risk taxonomy boards already use.