AI Governance
AI governance is no longer a value statement, rather it is a proof statement. Across India, the EU, the UK and the US, the common governance spine is converging, even when laws differ.
The Seven Sutras of AI Governance
The organisations that will lead are the ones that can live by these seven sutras in daily operations, each backed by logs, reviews, testing, and audit ready records across the full AI lifecycle.
Compliance with applicable laws and regulations
Clear ownership and responsibility chains
Prevention of harm to individuals and society
Protection against threats and vulnerabilities
Explainability and openness in AI operations
Non discrimination and equitable treatment
Meaningful human control over AI systems
Eight Pillars of the Playbook
Across India, the EU, the UK and the US, the common governance spine is converging, even when laws differ. Each pillar requires demonstrable Policy, Controls, and Evidence.
India Regulatory Base
- ▸IT Act + DPDP Act + Sectoral (MeitY Health/Finance)
- ▸Law vs Guideline vs Best Practice
- ▸DPDP Act 2023 + DPDP Rules 2025
Global Regimes
- ▸EU AI Act (Full Enforcement & AI Office)
- ▸US NIST AI RMF 2.0 & ISO/IEC 42001 Cert
- ▸UK, OECD, G7 Hiroshima Process
Risk Classification
- ▸Context driven Risk Assessment
- ▸Tiers: Prohibited, High Risk (Lethal/Bias), Limited, Minimal
- ▸India's Risk based Expectations
Governance Roles
- ▸Board Oversight, AI Owner
- ▸AI Safety Officer (AISO)
- ▸Model Risk Committee, DPO
Lifecycle Controls
- ▸Data Governance & Privacy by Design
- ▸Model Dev & Testing (Versioning)
- ▸Deployment & Monitoring (Human in loop)
- ▸Change Management
Transparency & Documentation
- ▸Model Cards / System Cards
- ▸Algorithmic Impact Assessments (AIA)
- ▸Logs & Records (Watermarking)
Safety, Security & Resilience
- ▸Red Teaming | Adversarial Robustness
- ▸Prompt Injection Defense | Incident Response Plans
- ▸AI Safety / IP & Licensing
- ▸Bias/Fairness
Accountability & Audit
- ▸Metrics | Internal Audit
- ▸Third Party Certifications (ISO 42001)
- ▸Evidence Bundle, Continuous Monitoring (Drift)
Risks to Watch Today
These operational gaps represent the most common compliance failures observed across enterprises deploying AI systems without robust governance frameworks.
Without demonstrable data provenance and audit trails, AI systems cannot meet the evidence requirements of modern regulatory frameworks.
Fragmented Ownership
Unclear accountability across business units, IT, legal and compliance teams creates governance gaps.
Missing Data Lineage
Without clear provenance tracking, organizations cannot demonstrate compliant data sourcing for AI training.
Weak Vendor Assurance
Third party AI models deployed without adequate due diligence on their compliance posture.
Missing Decision Logs
Systems shipped without audit ready records that can withstand regulatory scrutiny.
India AI Impact Summit 2026
The India AI Impact Summit 2026 in New Delhi at Bharat Mandapam on 19 and 20 February 2026 matters, because it is positioned around impact, not hype, and around what can be demonstrated in policy, controls, and evidence.
Summit Focus Areas
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