The question is no longer whether AI systems require governance but how that governance should be structured. Across jurisdictions, regulatory frameworks are converging on requirements for impact assessment, ongoing monitoring, and independent audit. India, through the DPDPA and sector-specific guidance, is developing its own approach to AI compliance architecture. For organisations deploying AI systems, understanding these requirements and building appropriate governance structures is essential for sustainable operation.
The Compliance Landscape
AI compliance in India draws from multiple regulatory sources. The DPDPA imposes data protection obligations including impact assessments for high-risk processing. Sector-specific regulators including RBI, SEBI, and IRDAI impose technology governance requirements. Industry standards, while not legally binding, increasingly inform regulatory expectations and court assessments of reasonable practice. Voluntary frameworks like NITI Aayog's Responsible AI principles signal government priorities that may crystallise into binding requirements.
This multi-source compliance landscape requires integrated approaches. Organisations cannot treat data protection, sector regulation, and industry standards as separate compliance silos. The same AI system may engage multiple frameworks simultaneously. Governance structures must enable coordinated compliance across regulatory domains while avoiding duplication and conflict.
Algorithmic Impact Assessment
Impact assessment represents the foundation of AI governance. Before deploying AI systems that may affect individuals, organisations should systematically evaluate potential harms and mitigation measures. The DPDPA requires data protection impact assessments for processing that poses significant risk to data principals. While AI-specific impact assessment requirements are not yet mandatory, the trajectory points toward such requirements, and prudent organisations are adopting them proactively.
Effective AI impact assessments examine multiple dimensions: accuracy and error rates across different populations; potential for discriminatory outcomes; privacy implications of data collection and processing; security vulnerabilities and attack surfaces; transparency and explainability limitations; human oversight mechanisms; and pathways for affected individuals to seek recourse. The assessment should be proportionate to the system's risk profile, with high-risk systems receiving more intensive scrutiny.
Ongoing Monitoring
AI systems require continuous monitoring throughout their operational lifecycle. Unlike traditional software that behaves consistently, AI systems may drift as the world changes around them. Models trained on historical data may become less accurate as new patterns emerge. Feedback loops may amplify biases over time. Adversarial inputs may exploit vulnerabilities that testing did not reveal. Monitoring systems must detect these developments and trigger appropriate responses.
Monitoring should address both technical and outcome metrics. Technical metrics include model performance, error rates, latency, and throughput. Outcome metrics examine whether the AI system achieves its intended purposes without causing unintended harms. Monitoring should include mechanisms for escalation when metrics exceed defined thresholds, ensuring that deteriorating performance receives timely human attention.
Third-Party Audit
The DPDPA requires significant data fiduciaries to undergo periodic audits by independent data auditors. While the rules specifying audit requirements are still being developed, the direction is clear: external verification will become mandatory for large-scale data processing. AI systems that process personal data at scale will fall within audit scope.
Beyond mandatory audits, voluntary third-party assessment offers strategic benefits. Independent AI audits provide assurance to stakeholders, identify improvement opportunities, and generate evidence supporting regulatory compliance. The emerging AI audit profession is developing methodologies and standards that will shape audit practice. Organisations that engage with these developments position themselves advantageously for mandatory audit requirements when they arrive.
Documentation and Records
Compliance requires evidence, and evidence requires documentation. AI governance documentation should address system design choices, training data provenance, validation testing results, impact assessments conducted, monitoring protocols, incident response procedures, and audit findings. This documentation serves multiple purposes: it informs internal decision-making, supports regulatory inquiries, enables audit verification, and provides defence in potential litigation.
Documentation practices should be embedded in AI development and operation processes, not treated as afterthoughts. Requirements for documentation should be specified in development methodologies, deployment checklists, and operational procedures. Documentation should be maintained in accessible, searchable formats that enable rapid response to inquiries. The investment in documentation infrastructure pays dividends in reduced compliance burden and improved risk management.
Governance Structures
Effective AI compliance requires governance structures that assign clear responsibilities. Board-level oversight ensures that AI risks receive appropriate executive attention. AI governance committees or officers provide focused expertise. Cross-functional representation connects technical teams, legal, compliance, and business stakeholders. Clear escalation pathways ensure that significant issues receive timely decision-making.
The appropriate governance structure depends on organisational scale and AI intensity. A startup deploying a single AI application may address governance through existing management structures. A large enterprise with multiple AI applications across business units may require dedicated AI governance functions. What matters is not the specific structure but that someone is accountable for AI compliance, that accountability is backed by appropriate authority and resources, and that governance processes connect to operational reality.
Building Compliance Capability
AI compliance is an emerging discipline requiring new capabilities. Legal teams need technical understanding sufficient to advise on AI-specific issues. Technical teams need legal awareness sufficient to build compliance into systems. Compliance functions need AI expertise sufficient to assess regulatory obligations. Building these capabilities requires investment in training, hiring, and potentially external advisory relationships.
The lawyer advising on AI compliance should understand both the current state of requirements and their likely evolution. Current requirements, while significant, are less demanding than what is coming. Organisations that build robust compliance capabilities now position themselves to adapt as requirements intensify. Those that minimise current compliance investment may find themselves scrambling as mandatory requirements arrive. In AI governance, proactive investment yields both compliance benefits and competitive advantage.