About

Two disciplines. One accountable team.

Responsible AI Solutions combines cloud, data and AI implementation capability with AI governance and risk expertise. Engagements are principal-led from problem framing through operating handover.

Practice Lead
AWS Cloud, Data & AI

Shangar Sivananthan

Platforms built for the team that inherits them.

Shangar is a cloud solutions architect specialising in AWS. He has designed and delivered production migrations, integrations and AI platforms for major banks, manufacturers and government bodies across Asia-Pacific.

At AWS and Deloitte he designed and operationalised generative AI platforms, enterprise data platform modernisations, AWS landing zones and Cloud Centre of Excellence programmes for banks and government agencies across the region. He owned delivery end to end, from target-state architecture, security and networking design through implementation, migration and Day 2 operations.

He has worked every side of an engagement, from writing the RFP through to implementation and handover. His delivery model treats infrastructure as code, architecture decision records, runbooks, recovery rehearsal and role-based training as part of the build rather than post-project documentation.

He holds every active AWS certification and is an AWS Golden Jacket recipient, AWS’s recognition for the small global cohort certified across its entire technology ecosystem.

All active AWS certificationsAWS Golden JacketDatabricks

shangar@responsible-ai-solutions.com LinkedIn

Practice Lead
AI Governance

Vivegavalen Vadi Valu

Governance translated into operating control.

Vivegavalen is an AI governance practitioner with more than thirteen years across technology law, privacy, data governance and AI risk management in Asia-Pacific. He specialises in moving AI governance beyond principles and policy into operational control across live systems, including generative and agentic AI.

His work spans the AI governance delivery chain: board mandate and policy architecture, risk and impact assessment, model and system evaluation, guardrail design, human oversight, and governance of systems in production.

Most recently he was Associate Director at Deloitte Australia, where he led AI governance engagements for clients including the Commonwealth Bank of Australia, Optus, and the justice departments of South Australia and Western Australia. Earlier roles at Accenture, Nestlé and PwC spanned legal, advisory and regulated environments across the region.

In July 2026 he made a submission to the public consultation on Malaysia’s proposed AI Governance Bill.

LLB (Hons)ISO/IEC 42001 Lead ImplementerIAPP AIGPIAPP CIPMUniversity of Edinburgh · Data Ethics, AI & Responsible Innovation

vivek@responsible-ai-solutions.com LinkedIn Policy submission

Client names describe prior practitioner experience at a previous employer. No endorsement, current affiliation or client relationship is implied.

Delivery principles

What an engagement should leave behind.

The test is not whether a framework, platform or workshop was delivered. It is whether the institution can make and evidence the required decisions without the external team.

A documented system

Policies, architecture, procedures, decision records and controls are sufficiently precise to operate.

An evidence record

The basis for material decisions, tests, exceptions and approvals can be reconstructed.

Capable owners

Named people have authority, context, tools and rehearsed procedures to perform the work.

An explicit exit

The external team steps back against acceptance criteria rather than remaining an unplanned dependency.

How engagements run

Principal-led, evidence-oriented, designed to transfer.

01
Map the operating fact. Understand what exists, where decisions are made and where risk or delivery constraints actually sit.
02
Bound the work. Shape scope and depth around sector, mandate, maturity and the decision that must be reached.
03
Deliver through real artefacts. Work with live workflows, architecture, controls, tests and accountable decisions.
04
Transfer and step back. Support the team after go-live and exit on an agreed timetable once the capability is proven.

Start with one real problem

Bring the operating problem, not a pre-written scope.

We will work with you to identify the decision, system boundary and evidence that should govern the engagement.

Discuss a use case