A Practical AI Governance Checklist Framework for Indian Companies.
Artificial Intelligence is no longer experimental within Indian enterprises. It is embedded in HR systems, financial analytics, customer engagement platforms, fraud detection engines, cybersecurity tools, and generative applications.
Yet in many organisations, AI adoption has outpaced governance.
This checklist is designed for Boards, Audit Committees, Risk Committees, and CXOs to assess whether their organisation’s AI deployments are legally defensible, operationally controlled, and fiduciary-compliant.
For a deeper AI Governance at Board Level understanding, refer to our LinkedIn Newsletter article: “AI Governance Is Now a Board-Level Imperative.”
- Enterprise Visibility: Do You Know Where AI Exists?
☐ Has the organisation created a formal AI inventory or register?
☐ Does the inventory include internally developed systems as well as vendor-embedded AI tools?
☐ Are SaaS products with default AI features included in this mapping?
☐ Is the inventory reviewed periodically at Board / Risk Committee level?
Without consolidated visibility, meaningful oversight is not possible.
- Risk Classification: Are AI Systems Tiered by Impact?
☐ Are AI deployments classified based on impact severity (low, medium, high risk)?
☐ Are systems affecting financial decisions, employment, or consumer rights subject to enhanced scrutiny?
☐ Is there a documented approval threshold for high-impact AI systems?
☐ Are independent validation or testing mechanisms in place?
High-impact AI requires heightened governance discipline.
- Data Protection Compliance
☐ Do AI systems process personal data?
☐ Has consent architecture been validated under the Digital Personal Data Protection Act, 2023?
☐ Is purpose limitation documented and aligned with model deployment?
☐ Are data minimisation principles operationalised in training datasets?
☐ Is there a documented data retention and deletion policy applicable to AI systems?
Automated profiling and algorithmic decision-making fall squarely within data protection scrutiny.
- Intermediary & Platform Liability Exposure
☐ Does the organisation operate a digital platform or host user-generated content?
☐ Are AI systems used for content moderation or content recommendation?
☐ Are safeguards in place to manage deepfakes, synthetic media, impersonation, or unlawful amplification risks?
☐ Is intermediary compliance assessed under the Information Technology Act, 2000?
Algorithmic amplification can create regulatory exposure even without human intent.
- Consumer Law & Fairness Risk
☐ Do AI systems influence pricing, recommendations, or consumer eligibility?
☐ Is there a review mechanism to detect misleading or unfair outcomes?
☐ Are automated decisions explainable to consumers where legally required?
☐ Has exposure been evaluated under the Consumer Protection Act, 2019?
Algorithmic opacity does not shield enterprises from unfair trade practice claims.
- Fiduciary Oversight: Board-Level Accountability
☐ Is AI governance discussed at Board or Committee meetings?
☐ Has management briefed directors on AI risk exposure?
☐ Is AI oversight aligned with directors’ duties under Section 166 of the Companies Act, 2013?
☐ Is there documentation evidencing informed oversight?
Directors’ duty of care extends to algorithmic decision systems that materially influence enterprise conduct.
- Explainability & Human Oversight
☐ Can the organisation explain how high-impact AI systems arrive at outcomes?
☐ Are human-in-the-loop controls defined for critical decisions?
☐ Is there a documented override or escalation mechanism?
☐ Are anomalous outputs reviewed periodically?
Explainability is not merely technical — it is defensibility.
- Vendor & Procurement Governance
☐ Do AI vendor contracts address data sourcing legitimacy?
☐ Are bias mitigation representations included?
☐ Are audit rights contractually secured?
☐ Is indemnity allocation clearly defined for algorithmic harm?
☐ Is vendor AI risk reviewed by legal and compliance teams?
Many AI risks enter through procurement, not internal development.
- Incident Management & Reporting
☐ Is there an internal framework to report AI-related anomalies?
☐ Are discriminatory outputs, hallucinations, or manipulation events escalated?
☐ Is AI model drift monitored?
☐ Are significant AI incidents reported to the Board?
AI incidents should be treated with governance seriousness comparable to cybersecurity breaches.
- Ongoing Governance Rhythm
☐ Is there periodic AI risk reporting?
☐ Are independent audits or bias assessments conducted?
☐ Is regulatory horizon scanning performed?
☐ Is the AI governance framework periodically reviewed and updated?
AI risk evolves dynamically. Governance must be adaptive.
Deepfakes, Digital Harm, and the Rising Responsibility of Intermediaries
Final Governance Test
If a regulator, court, or shareholder were to question your AI deployment today, could your organisation demonstrate:
- Visibility
- Risk classification
- Legal compliance
- Documented oversight
- Board awareness
- Incident control
If the answer to any of these is uncertain, AI governance likely requires strengthening.
How CorpoTech Legal Assists: CorpoTech Legal advises Boards and enterprises on:
- AI risk mapping and governance structuring
- Statutory compliance integration
- Director fiduciary exposure assessment
- Vendor AI contract review
- AI governance policy drafting
For advisory engagements, please contact us corpotechlegal@gmail.com or through our website.
AI governance is now a Board-level duty. Explore fiduciary risk, DPDP compliance, and Section 166 implications for Indian directors. Read more articles on Linkedin NewsLetter AI Governance Boardroom from Ajay Sharma, an ISO 42001 Lead Auditor and Certified GRC Professional, Techno Legal Advisor and AI Governance Expert, focusing on technology risk, data protection compliance, and board-level accountability frameworks. He advises enterprises and directors on aligning digital transformation initiatives with statutory obligations, fiduciary duties, and evolving regulatory expectations. His work in AI governance centres on translating complex technological exposure into legally defensible oversight structures — enabling Boards to innovate responsibly while strengthening institutional resilience.