Designing Data-Sovereign GCCs under EU AI Act and Digital Personal Data Protection Act, 2023

Published on

December 17, 2025

The rapid rise of Artificial Intelligence (AI) has triggered the need for stronger regulatory frameworks globally. The EU AI Act, the world’s first comprehensive AI law, classifies AI systems based on risk. It sets stringent compliance requirements for high-risk applications.

Meanwhile, India’s Digital Personal Data Protection Act (DPDP Act), 2023 aims to protect the digital personal data of citizens. It draws a balance between the rights of privacy and the requirements of businesses in processing data for lawful purposes. These laws reflect a governance approach focusing on transparency, accountability, and user protection.

For GCCs dealing in extensive volumes of data, such regulations are rewriting the rules of operations and minimum standards of compliance. Designing data-sovereign GCCs under this dual lens involves creating infrastructures that respect local data protectional laws while also ensuring businesses flourish in trust, compliance, and innovation.

Why the EU AI Act and DPDP Act 2023 Reshape GCCs

The EU AI Act and India’s DPDP Act, 2023, are two milestone regulations recasting the global governance of AI and personal data. While the EU AI Act, which came into effect in August 2024, categorizes AI systems based on risks and introduces strict accountability, the DPDP enumerates consent norms, cross-border transfer controls, and breach obligations.

For GCCs, these laws transform both operations and compliance models. GCCs must ensure AI systems meet EU standards and align with India’s DPDP Act for personal data handling. These frameworks compel GCCs to adopt systematic data discovery and governance-first strategies. Thus, GCCs need to position themselves as data-sovereign ecosystems built on trust and regulatory resilience.

High-risk vs GPAI Obligations

High-risk AI systems face the strictest obligations under the EU AI Act. Providers must implement a comprehensive risk management system and ensure data governance using representative, error-free training datasets. They must also maintain technical documentation to prove compliance.

GPAI models, designed for broad tasks and integration across applications, also carry important responsibilities. Providers must prepare documentation and training records, share integration guidance with downstream developers, and respect copyright rules when using large datasets.

Records, Logging, and Incident Reporting

The EU AI Act and India’s DPDP Act emphasize accountability through systematic documentation, automated logging, and timely incident reporting. These provisions ensure that organizations handling sensitive AI systems or personal data maintain transparent audit trails.

Here are some of the key provisions:

EU AI Act

Documentation and Records: Providers of high-risk AI must maintain technical documentation, quality management records, conformity declarations, and approved change logs for at least 10 years (Article 18).

Automated Logging: High-risk AI systems must keep automatically generated logs for at least six months, ensuring traceability of decisions and compliance with Union law (Article 19).

Incident Reporting: Serious incidents linked to AI systems must be reported to market surveillance authorities immediately or within 15 days, with follow-up investigations and corrective measures (Article 73).

DPDP Act, 2023

Breach Reporting: Data fiduciaries must notify the Data Protection Board without delay upon breach discovery, with detailed reporting within 72 hours, and directly inform affected data principals.

Penalties: Non-compliance can attract penalties up to ₹250 crore for inadequate safeguards and ₹200 crore for failure to notify breaches, with additional sanctions for violations involving children’s data.

Data Residency and Model Governance

The term ‘Data localization’ refers to the requirement that data, especially personal or sensitive information about a country’s citizens, must be stored, processed, or handled within that country’s borders. While the DPDP Act, 2023 does not impose universal data localization, it enforces sector-specific requirements, such as RBI’s mandate for storing payment data within India. It ensures data sovereignty, requiring that sensitive data remain under national oversight. It also enables penalties for breaches and strict notification rules.

The EU AI Act focuses on AI model governance rather than localization. High-risk and GPAI models must meet obligations for risk assessments, transparency, documentation, and EU registration. While DPDP emphasizes where data is stored, the EU AI Act prioritizes how AI models are governed, together shaping global compliance for GCCs.

Model Cards, Audit Trails, Vendor Clauses

The EU AI Act mandates high-risk AI providers to keep audit logs, technical documentation, and quality management records for up to 10 years. This record-keeping ensures traceability and oversight. It also requires reproducibility features like iteration history and activity tracking. It enables regulators to verify compliance at any stage. The DPDP Act, 2023 enforces timely breach reporting to the Data Protection Board and impacted individuals, with penalties for failures.

With regard to vendor obligations, the EU AI Act compels providers to ensure downstream partners follow risk, governance, and oversight controls. The DPDP Act similarly makes fiduciaries liable for third-party processors. It requires equal safeguards for security and privacy. Non-compliance by vendors can directly penalize the contracting entity. It drives stricter vendor agreements and audit-ready processes.

Reference Governance Stack for Captives

GCCs must adopt a layered governance framework that aligns with both the EU AI Act and India’s DPDP Act, 2023. While the EU AI Act emphasizes on lifecycle accountability for high-risk AI, the DPDP Act enforces strict responsibilities around personal data usage, consent, and fiduciary duties. Together, they require captives to embed compliance across people, processes and technology.

Lawful Basis and Purpose Limitation (DPDP): Data processing only under valid consent, contracts, or legitimate purposes.

Transparency and Notice (DPDP): Clear disclosures on data use, model training, and automated decision-making.

Significant Data Fiduciary Obligations (DPDP): Independent audits, impact assessments, and governance officers for large-scale AI/data operations.

Continuous Risk Management (EU AI Act): Ongoing evaluation of safety, rights, and compliance throughout the AI lifecycle.

Comprehensive Documentation (EU AI Act): Technical records on datasets, algorithms, event logs, and governance controls.

Human Oversight and Transparency (EU AI Act): Ensuring human accountability in AI decision-making.

Bias and Quality Controls (EU AI Act): Monitoring training datasets for accuracy, fairness, and security.

Policy, Registry, Monitoring Layers

Policy Layer

The EU AI Act prohibits harmful AI uses such as manipulative techniques, exploitative profiling, and biometric categorization. These rules set clear boundaries for what AI systems cannot do. It protects individuals from high-risk misuse.

Registry Layer

High-risk AI systems must be formally registered in an EU database before deployment. This registry ensures transparency, allowing regulators to track which AI systems are in use. They also verify compliance with safety and rights-based requirements.

Monitoring Layer

The Act requires continuous post-market monitoring for high-risk AI. Providers must collect data, track incidents, and document performance throughout the AI lifecycle. It ensures that systems remain compliant after being placed on the market.

90-day Implementation KPIs

90-Day Implementation KPIs for the EU AI Act

A structured 90-day roadmap helps organizations align with the EU AI Act by ensuring risk classification. It ensures governance readiness and compliance accountability. The focus is on inventory, classification, and operational alignment of AI systems.

Weeks 1-2: Inventory of all AI systems to identify those falling under the Act.

Weeks 3-4: Classification of AI systems by risk category.

Weeks 5-6: Appointment of an AI compliance officer.

Weeks 7-8: Development of a compliance strategy with system modifications.

Weeks 9-10: Staff training on compliance protocols and ethical AI use.

Weeks 11-12: Review and adjust organisational policies to align with Act requirements.

90-Day Implementation KPIs for the DPDP Act

For the DPDP Act, KPIs measure consent management, rights protection, vendor governance, and training effectiveness. These metrics demonstrate readiness to handle sensitive personal data under the Act’s strict obligations.

Consent Posture: ≥95% consent-log coverage; simple withdrawal processes.

Rights Performance: ≥90% on-time closures; fulfilment within ≤7 days.

Risk Cadence: Quarterly DPIAs for high-risk processes.

Vendor Risk Trend: Reduction in high-risk vendor issues and SLA adherence.

Culture Metrics: ≥95% training completion; ≥90% refresher course rates.

Designing data-sovereign GCCs under the EU AI and DPDP means embedding privacy, governance, and compliance. Partner with ANSR to build future-ready GCC frameworks that align with global mandates, strengthen governance, and unlock talent potential.