We Audit Not Just Systems — But Intelligence Itself.
As AI becomes embedded in credit decisions, insurance underwriting, trading algorithms, fraud detection, and customer service — the governance gap has never been greater. Your AI is only as trustworthy as the governance framework behind it.
Zorixx AI-GRA is India’s emerging independent assurance practice for AI-powered organisations.
WHY AI GOVERNANCE CANNOT WAIT
The Risk Is Real
AI systems making biased credit decisions, unfair insurance pricing, or erroneous fraud flags are not hypothetical. Without governance, your AI is a liability — not an asset.
Regulators Are Moving Fast
RBI responsible AI guidelines, SEBI algorithm review requirements, and global AI regulations (EU AI Act) are creating compliance obligations that will reach Indian organisations within 2–3 years.
Boards Are Exposed
Boards approving AI deployment without independent assurance are taking on undisclosed risk that has no precedent in traditional audit frameworks.
Trust Is Competitive Advantage
Organisations demonstrating responsible AI adoption attract better customers, better talent, and better regulatory relationships. AI trust is a board-level asset.
AI-GRA SERVICES
AI Governance Framework Design
- AI governance policy — roles, responsibilities, escalation frameworks
- AI inventory and risk-tier classification system
- Model approval and change management framework
- Human-in-the-loop requirements — when AI decisions require human review
- AI ethics principles operationalisation
- AI governance committee structure and charter
- Board-level AI risk reporting framework
AI Risk Assessment
- Model risk identification — inherent risks of the AI use case
- Bias risk — demographic bias, proxy bias, feedback loop bias
- Data quality and lineage risk — training data reliability and representativeness
- Adversarial risk — can the AI be manipulated or deceived?
- Concentration risk — over-reliance on a single AI vendor or system
- Regulatory risk — does the use case trigger specific obligations?
AI Lifecycle Controls Review
- Data sourcing controls — consent, data quality, training data bias
- Model development controls — version control, documentation, peer review
- Model validation — independent validation process, challenger model review
- Deployment controls — staging environment, rollback procedures, access controls
- Monitoring controls — performance drift detection, retraining triggers, alert thresholds
- Retirement controls — model decommissioning, data retention, audit trail
Regulatory Alignment & AI Ethics Review
- RBI responsible AI principles — lending, fraud, customer service AI
- SEBI algorithmic trading audit requirements — algo trading system review
- IRDAI AI-driven underwriting and claims processing governance
- EU AI Act readiness for Indian entities with EU exposure
- ISO/IEC 42001 AI Management Systems readiness assessment
- Ethics review — fairness, transparency, accountability, privacy in AI
Board-Level AI Assurance
- AI risk register with board-ready heat map
- AI governance maturity assessment and benchmarking
- Board briefing materials on AI risk — non-technical language
- Independent AI audit report for Audit Committee
- AI governance health score with peer organisation comparison