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Reserve Bank of India's August 2025 framework establishing seven guiding principles for responsible and ethical artificial intelligence enablement in financial services sector. Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) Committee Report Reserve Bank of India's August 2025 framework establishing seven guiding principles for responsible and ethical artificial intelligence enablement in financial services sector. Previous Next The AIACT.IN India AI Regulation Tracker This is a simple regulatory tracker consisting all information on how India is regulating artificial intelligence as a technology, inspired from a seminal paper authored by Abhivardhan and Deepanshu Singh for the Forum of Federations, Canada, entitled, "Government with Algorithms: Managing AI in India’s Federal System – Number 70 ". We have also included case laws along with regulatory / governance documents, and avoided adding any industry documents or policy papers which do not reflect any direct or implicit legal impact. August 2025 Read the Document Issuing Authority Reserve Bank of India (RBI) Type of Legal / Policy Document Guidance documents with normative influence Status Enacted Regulatory Stage Pre-regulatory Binding Value Guidance documents with normative influence AIACT. Regulation Visualiser Find more sources Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition Learn More Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Learn More AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Learn More Artificial Intelligence, Market Power and India in a Multipolar World Learn More Related draft AI Law Provisions of aiact.in Section 10 – Composition and Functions of the Council Section 10 – Composition and Functions of the Council Section 11 – Registration & Certification of AI Systems Section 11 – Registration & Certification of AI Systems Section 12 – National Registry of Artificial Intelligence Use Cases Section 12 – National Registry of Artificial Intelligence Use Cases Section 13 – National Artificial Intelligence Ethics Code Section 13 – National Artificial Intelligence Ethics Code

Privacy by Design Date of Addition 26 April 2024 Privacy by Design states that any action a company undertakes that involves processing personal data must be done with data protection and privacy in mind at every step. This was largely proposed in the Article 25 of the General Data Protection Regulation of the European Union. Related Long-form Insights on IndoPacific.App 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Learn More [Version 1] A New Artificial Intelligence Strategy and an Artificial Intelligence (Development & Regulation) Bill, 2023 Learn More [Version 2] Draft Artificial Intelligence (Development & Regulation) Act, 2023 Learn More [AIACT.IN V3] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 3 Learn More AIACT.IN Version 3 Quick Explainer Learn More Sections 4-9, AiACT.IN V4 Infographic Explainers Learn More [AIACT.IN V4] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 4 Learn More [AIACT.IN V5] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 5 Learn More Reckoning the Viability of Safe Harbour in Technology Law, IPLR-IG-015 Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Learn More Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Learn More AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Learn More 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Learn More Previous Term Next Term The Indic Pacific Glossary The Complete Glossary terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com

NITI Aayog's June 2018 foundational policy document establishing India's national AI strategy identifying five priority sectors including healthcare, agriculture, education, smart cities, and mobility. National Strategy for Artificial Intelligence (#AIforAll) NITI Aayog's June 2018 foundational policy document establishing India's national AI strategy identifying five priority sectors including healthcare, agriculture, education, smart cities, and mobility. Previous Next The AIACT.IN India AI Regulation Tracker This is a simple regulatory tracker consisting all information on how India is regulating artificial intelligence as a technology, inspired from a seminal paper authored by Abhivardhan and Deepanshu Singh for the Forum of Federations, Canada, entitled, "Government with Algorithms: Managing AI in India’s Federal System – Number 70 ". We have also included case laws along with regulatory / governance documents, and avoided adding any industry documents or policy papers which do not reflect any direct or implicit legal impact. June 2018 Read the Document Issuing Authority NITI Aayog Type of Legal / Policy Document National Strategies Status Enacted Regulatory Stage Pre-regulatory Binding Value Non-binding but institutionally endorsed instruments AIACT. Regulation Visualiser Find more sources Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition Learn More Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Learn More AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Learn More Artificial Intelligence, Market Power and India in a Multipolar World Learn More Related draft AI Law Provisions of aiact.in Section 1 – Short Title and Commencement Section 1 – Short Title and Commencement Section 2 – Definitions Section 2 – Definitions Section 14 – Model Standards on Knowledge Management Section 14 – Model Standards on Knowledge Management

Section 14 – Model Standards on Knowledge Management PUBLISHED Previous Next Section 14 - Model Standards on Knowledge Management (1) The IAIC shall develop, document and promote comprehensive model standards on knowledge management practices concerning the development, maintenance, and governance of high-risk AI systems. These standards shall focus on the effective management of knowledge assets; (2) The model standards shall encompass the following areas: (i) Intellectual property management practices to safeguard and leverage AI-related intellectual property rights such as patents, copyrights, trademarks and industrial designs. (ii) Processes for documenting and organizing technical knowledge assets like research reports, manuals, standards and industrial practices related to AI systems. (iii) Frameworks for capturing, retaining and transferring the tacit knowledge and expertise of human capital involved in AI development and deployment. (iv) Organisational systems and methodologies to enable effective knowledge capture, storage, retrieval and utilisation across the AI system lifecycle. (v) Mechanisms for leveraging customer-related knowledge assets such as data, feedback and insights to enhance AI system development and performance. (vi) Analytical techniques to derive knowledge from data analysis, including identifying patterns, trends and developing predictive models for AI systems. (vii)Collaborative practices to foster cross-functional knowledge sharing and generation through teams, communities of practice and other initiatives. (3) All entities engaged in the development, deployment, or utilisation of high-risk AI systems shall be bound by the model standards on knowledge management and decision-making as provided by this section. The compliance timeline for such high-risk AI systems shall be determined by the IAIC and may vary based on the technical, commercial and risk-based classification of those systems under Section 12. (4) For artificial intelligence technologies subject to commercial classification as determined by the factors outlined in sub-section (1) of Section 6, the requirement to comply with these model standards on knowledge management shall be assessed by the IAIC on a case-by-case basis, taking into consideration the specific commercial classification factors applicable to each AI technology. Illustration A startup has developed an AI-powered language translation app that allows users to translate text, documents, and speech between multiple Indian languages. Based on an assessment of the factors in Section 6(1), such as the app’s user base, market influence, and data integration, the IAIC may determine that this AI technology falls under the AI-Pro or AIaaS category. The IAIC will then evaluate if the startup needs to fully comply with the knowledge management standards or if certain requirements can be relaxed or made optional based on the app’s specific use case and commercial profile. (5) In determining the case-by-case application of these model standards to commercially classified AI technologies under sub-section (1) of Section 6, the IAIC shall take into account any relevant sector-specific standards, codes of practice, or regulatory guidelines pertaining to knowledge management practices in the sector to which the AI technology belongs or is intended to be deployed. Illustration An agritech startup has developed an AI system that analyzes satellite imagery and weather data to provide crop yield predictions and advisory services to farmers. As this AI technology falls within the agriculture sector, the IAIC’s assessment of its knowledge management requirements will consider any relevant guidelines or standards issued by bodies like the Indian Council of Agricultural Research (ICAR) or the Ministry of Agriculture & Farmers’ Welfare. These may include data governance norms for agricultural data, model validation protocols for AI-based advisory services, or best practices for maintaining data trails and audit logs in agritech applications. (6) Failure to adhere to the prescribed model standards for knowledge management and decision-making processes shall result in regulatory actions by the IAIC, which may include: (i) Issuance of show-cause notices to the non-compliant entity, requiring them to explain the reasons for non-compliance and outline corrective measures within a specified timeline. (ii) Imposition of monetary penalties, determined based on the severity of non-compliance, the risk level of the AI system involved, and the potential impact on individuals, businesses, or society. The monetary penalties shall be commensurate with the financial capacity of the non-compliant entity. (iii)Suspension or revocation of certifications or registrations related to the non-compliant AI system, preventing its further development, deployment, or operation until compliance is achieved. (iv) Mandating independent audits of the non-compliant entity’s knowledge management and decision-making processes at their own cost, with the audit reports to be submitted to the IAIC for review and further action. (v) Issuing directives to the non-compliant entity to implement specific remedial measures, such as enhancing data quality controls, improving model governance frameworks, or strengthening decision-making procedures, within a defined timeline. (vi) In cases of persistent or egregious non-compliance, the IAIC may recommend the temporary or permanent suspension of the non-compliant entity’s AI-related operations, subject to due process and the principles of natural justice. (vii) Any other regulatory action deemed necessary and proportionate by the IAIC to ensure compliance with the prescribed model standards and to safeguard the responsible development, deployment, and use of high-risk AI systems. (7) The IAIC shall encourage the sharing of AI-related knowledge, including datasets, models, and algorithms, through open-source software repositories and platforms, subject to applicable intellectual property rights and the provisions of the Digital Personal Data Protection Act, 2023 and other relevant data protection and governance frameworks as may be prescribed. Related Indian AI Regulation Sources National Strategy for Artificial Intelligence (#AIforAll) June 2018 Karnataka Global Capability Center (GCC) Policy 2024-2029 November 2024 Strengthening AI Governance Through Techno-Legal Framework (White Paper, Part 2 of Emerging Policy Priorities Series) January 2026 Flipkart Internet Private Ltd v. Joint Controller of Patents and Designs & Voicemonk Inc., CMA(PT) No. 9 of 2024, Madras High Court, Order dated January 5, 2026 January 2026 Gammon–OJSC Mosmetrostroy JV and Chennai Metro Rail Limited (CMRL) January 2026

AI-based Anthropomorphization The Indic Pacific Glossary The Complete Glossary AI-based Anthropomorphization Date of Addition 26 Apr 2024 AI-based anthropomorphization is the process of giving AI systems human-like qualities or characteristics. This can be done in a variety of ways, such as giving the AI system a human-like name, appearance, or personality. It can also be done by giving the AI system the ability to communicate in a human-like way, or by giving it the ability to understand and respond to human emotions. This idea was discussed in the 2020 Handbook on AI and International Law (2021), Deciphering Artificial Intelligence Hype and its Legal-Economic Risks, VLiGTA-TR-001 (2022), Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) and Promoting Economy of Innovation through Explainable AI, VLiGTA-TR-003 (2023). Related Long-form Insights on IndoPacific.App Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Artificial Intelligence Governance using Complex Adaptivity: Feedback Report, First Edition, 2024 Learn More Legal Strategies for Open Source Artificial Intelligence Practices, IPLR-IG-004 Learn More Ethical AI Implementation and Integration in Digital Public Infrastructure, IPLR-IG-005 Learn More Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Learn More Artificial Intelligence and Policy in India, Volume 5 [AIPI-V5] Learn More Legal-Economic Issues in Indian AI Compute and Infrastructure, IPLR-IG-011 Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More Artificial Intelligence, Market Power and India in a Multipolar World Learn More Previous Term Next Term terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com

Whole-of-Government Response Date of Addition 26 April 2024 A whole-of-government response under the (proposed) Digital India Act is a coordinated approach to the governance of digital technologies and issues. It involves the participation of all relevant government ministries and agencies, as well as other stakeholders such as industry and academia. The goal of a whole-of-government response is to ensure that digital technologies are used in a way that is beneficial to society, while also mitigating any potential risks. This may involve developing new policies and regulations, investing in research and development, and raising awareness of digital issues. This was discussed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023) . Related Long-form Insights on IndoPacific.App The LegalTechPolicy.com Playbook, First Edition Learn More 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Global Customary International Law Index: A Prologue [GLA-TR-00X] Learn More Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] Learn More An Indian Perspective on Special Purpose Acquisition Companies [GLA-TR-001] Learn More India-led Global Governance in the Indo-Pacific: Basis & Approaches [GLA-TR-003] Learn More Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] Learn More Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] Learn More Global Legalism, Volume 1 Learn More Global Relations and Legal Policy, Volume 1 [GRLP1] Learn More South Asian Review of International Law, Volume 1 Learn More Indian International Law Series, Volume 1 Learn More Global Relations and Legal Policy, Volume 2 Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Deciphering Regulative Methods for Generative AI [VLiGTA-TR-002] Learn More Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] Learn More Auditing AI Companies for Corporate Internal Investigations in India, VLiGTA-TR-005 Learn More [Version 1] A New Artificial Intelligence Strategy and an Artificial Intelligence (Development & Regulation) Bill, 2023 Learn More [Version 2] Draft Artificial Intelligence (Development & Regulation) Act, 2023 Learn More Artificial Intelligence Governance using Complex Adaptivity: Feedback Report, First Edition, 2024 Learn More Draft Digital Competition Bill, 2024 for India: Feedback Report [IPLR-IG-003] Learn More Legal Strategies for Open Source Artificial Intelligence Practices, IPLR-IG-004 Learn More Artificial Intelligence and Policy in India, Volume 4 [AIPI-V4] Learn More Ethical AI Implementation and Integration in Digital Public Infrastructure, IPLR-IG-005 Learn More [AIACT.IN V3] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 3 Learn More AIACT.IN Version 3 Quick Explainer Learn More The Indic Approach to Artificial Intelligence Policy [IPLR-IG-006] Learn More Reimaging and Restructuring MeiTY for India [IPLR-IG-007] Learn More Artificial Intelligence and Policy in India, Volume 5 [AIPI-V5] Learn More Indic Pacific - ISAIL Joint Annual Report, 2022-24 Learn More The Legal and Ethical Implications of Monosemanticity in LLMs [IPLR-IG-008] Learn More Navigating Risk and Responsibility in AI-Driven Predictive Maintenance for Spacecraft, IPLR-IG-009, First Edition, 2024 Learn More Impact-Based Legal Problems around Generative AI in Publishing, IPLR-IG-010 Learn More Legal-Economic Issues in Indian AI Compute and Infrastructure, IPLR-IG-011 Learn More Paving the Path to an International Model Law on Carbon Taxes [IPLR-IG-012] Learn More Sections 4-9, AiACT.IN V4 Infographic Explainers Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] Learn More [AIACT.IN V4] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 4 Learn More [AIACT.IN V5] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 5 Learn More Reckoning the Viability of Safe Harbour in Technology Law, IPLR-IG-015 Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Learn More The Global AI Inventorship Handbook, First Edition [RHB-AI-INVENT-001-2025] Learn More Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Learn More AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Learn More Artificial Intelligence and Policy in India, Volume 6 [AIPI-V6] Learn More Artificial Intelligence, Market Power and India in a Multipolar World Learn More 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Learn More Next Term Previous Term The Indic Pacific Glossary The Complete Glossary terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com

Multi-alignment Date of Addition 26 April 2024 Multi-alignment in foreign policy is a strategy in which a country maintains close ties with multiple major powers, rather than aligning itself with a single power bloc across regions, industry sectors, continents and power centers. This was discussed in India-led Global Governance in the Indo-Pacific: Basis & Approaches, GLA-TR-003 (2022). Related Long-form Insights on IndoPacific.App Global Customary International Law Index: A Prologue [GLA-TR-00X] Learn More An Indian Perspective on Special Purpose Acquisition Companies [GLA-TR-001] Learn More India-led Global Governance in the Indo-Pacific: Basis & Approaches [GLA-TR-003] Learn More Regulatory Sovereignty in India: Indigenizing Competition-Technology Approaches [ISAIL-TR-001] Learn More Global Legalism, Volume 1 Learn More Global Relations and Legal Policy, Volume 1 [GRLP1] Learn More South Asian Review of International Law, Volume 1 Learn More Indian International Law Series, Volume 1 Learn More Global Relations and Legal Policy, Volume 2 Learn More Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Learn More The Policy Purpose of a Multipolar Agenda for India, First Edition, 2023 Learn More The Indic Approach to Artificial Intelligence Policy [IPLR-IG-006] Learn More Paving the Path to an International Model Law on Carbon Taxes [IPLR-IG-012] Learn More Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Learn More AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas Learn More Previous Term Next Term The Indic Pacific Glossary The Complete Glossary terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com

lexploit Date of Addition 25 May 2026 The term implies itself to be a cybersecurity exploit in which a document is intentionally manipulated, typically at the font-rendering layer to trick an Artificial Intelligence (AI) system or a Large Language Model (LLM) into reading text that is completely different from what is visible to a human reader. Key Characteristics: Mechanism: Unlike "hidden text" or "white ink" tricks, a lexploit operates at the foundational rendering level of the document ( e.g., utilizing custom fonts like noroboto.tff ) . Distinct from Prompt Injection: While prompt injection manipulates the instructions an AI is given, a lexploit manipulates the actual source data the AI perceives during ingestion. Use Cases Offensive ("Weaponized Hallucination"): Deceiving an AI during automated document review. For example, formatting a contract so a human reads it as being "governed by Maryland law," while an AI conducting M&A due diligence misreads it as "governed by Delaware law." Defensive (Anti-Scraping): Protecting intellectual property by rendering documents invisible or garbled to automated AI ingestion pipelines and scraping agents, while keeping the content perfectly legible to human readers. Attributions & Credits Concept & Terminology: Coined and demonstrated by the team at LegalQuants , an organization focused on ethical hacking, enterprise security, and cyber defense in the legal industry. Demonstration & Development: Articulated by LegalQuants co-founders Raymond Sun (who demonstrated its defensive anti-scraping applications) and Jamie Tso (who demonstrated its offensive M&A applications). Technical Execution: The underlying proof-of-concept font ( noroboto.tff ) was developed by the LegalQuants Red Team: Drew Miller, Iris Ng, Andrius Petrenas, and Aleks Valkov . Related Long-form Insights on IndoPacific.App NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 Learn More Previous Term Next Term The Indic Pacific Glossary The Complete Glossary terms of use This glossary of terms is provided as a free resource for educational and informational purposes only. By using this glossary developed by Indic Pacific Legal Research LLP (referred to as 'The Firm'), you agree to the following terms of use: You may use the glossary for personal and non-commercial purposes only. If you use any content from the glossary of terms on this website in your own work, you must properly attribute the source. This means including a link to this website and citing the title of the glossary. Here is a sample format to cite this glossary (we have used the OSCOLA citation format as an example): Indic Pacific Legal Research LLP, 'TechinData.in Explainers' (Indic Pacific Legal Research , 2023) You are not authorised to reproduce, distribute, or modify the glossary without the express written permission of a representative of Indic Pacific Legal Research. The Firm makes no representations or warranties about the accuracy or completeness of the glossary. The glossary is provided on an "as is" basis and the Firm disclaims all liability for any errors or omissions in the glossary. You agree to indemnify and hold the Firm harmless from any claims or damages arising out of your use of the glossary. If you have any questions or concerns about these terms of use, please contact us at global@indicpacific.com

Liked our Work? Search it now on IndoPacific.App Get Searching Our Research Know more about our Knowledge Base, years of accumulated and developed in-house research at Indic Pacific Legal Research. Search our Research Treasure on IndoPacific.App. :) Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Get this Publication 2023 ISBN 978-81-959932-3-9 Author(s) Abhivardhan, Akash Manwani, Sanad Arora Editor(s) Not Applicable IndoPacific.App Identifier (ID) VLiGTA-TR-004 Tags Abhivardhan, Blockchain Integration, Blockchain Solutions, Case Studies in Web3 Adoption, Crypto Regulation in India, Cryptocurrency Adoption, Decentralized Finance (DeFi), Digital Payments, eCommerce and Web3, eCommerce Trends, Fintech Innovation, India's Digital Economy, Legal Framework for Web3, NFTs (Non-Fungible Tokens), Regulatory Challenges, Smart Contracts, Supply Chain on Web3, Technology Policy, Tokenization of Assets, VLiGTA, VLiGTA TR 004, Web3 and Data Ownership, Web3 and Financial Inclusion, Web3 Compliance, Web3 for Governance, Web3 for Small Businesses, Web3 Identity Management, Web3 Opportunities in India, Web3 Policy Regulations, Web3 Privacy and Security, Web3 Technology, Web3 Use Cases, Woocommerce Integration Related Terms in Techindata.in Explainers Definitions - A - E Automation Distributed Ledger Ethics-based concept classification Definitions - F - J Federated Learning Federated Unlearning Intended Purpose / Specified Purpose Definitions - K - P Manifest Availability Multi-alignment Multivariant, Fungible & Disruptive Use Cases & Test Cases of Generative AI Object-Oriented Design Privacy by Default Privacy by Design Definitions - Q - U SOTP Classification Technical concept classifcation Technology by Default Technology by Design Technology Distancing Technology Transfer Technophobia Definitions - V - Z Zero Knowledge Systems Zero Knowledge Taxes Related Articles in Techindata.in Insights 34 Insight(s) on AI Ethics 9 Insight(s) on AI Governance 8 Insight(s) on AI and Competition Law 8 Insight(s) on AI and Copyright Law 8 Insight(s) on AI and media sciences 8 Insight(s) on AI regulation 8 Insight(s) on AI literacy 5 Insight(s) on AI and Evidence Law 4 Insight(s) on Abhivardhan 2 Insight(s) on AI and Intellectual Property Law 1 Insight(s) on AI and Securities Law 1 Insight(s) on Algorithmic Trading . Previous Item Next Item

Section 32 – Offenses and Penalties PUBLISHED Previous Next Section 32 – Offenses and Penalties (1) Any person who contravenes or fails to comply with any provision of this Act, or the rules or regulations made thereunder, shall be liable to penalties as specified in this Section. (2) Systemically Significant Digital Enterprises (SSDEs) under the Digital Competition Act, 2024, that employ high-risk AI systems and fail to comply with the provisions of this Act shall be liable to the following penalties: (a) For the first offense, a fine of up to 5% of the SSDE’s total worldwide turnover in the preceding financial year or INR 50 crores, whichever is higher; (b) For subsequent offenses, a fine of up to 10% of the SSDE’s total worldwide turnover in the preceding financial year or INR 100 crores, whichever is higher. (3) Significant Data Fiduciaries (SDFs) under the Digital Personal Data Protection Act, 2023, that employ high-risk AI systems and fail to comply with the provisions of this Act shall be liable to the following penalties: (a) For the first offense, a fine of up to 4% of the SDF’s total worldwide turnover in the preceding financial year or INR 25 crores, whichever is higher; (b) For subsequent offenses, a fine of up to 8% of the SDF’s total worldwide turnover in the preceding financial year or INR 50 crores, whichever is higher. (4) Entities developing, deploying, or operating high-risk AI systems, other than those covered under sub-sections (2) and (3), that fail to comply with the provisions of this Act shall be liable to the following penalties: (a) For the first offense, a fine of up to INR 10 crores; (b) For subsequent offenses, a fine of up to INR 25 crores. (5) In addition to the financial penalties specified in sub-sections (2), (3), and (4), the IAIC may take the following actions against non-compliant entities: (a) Issuing warnings and directions for remedial measures; (b) Suspending or revoking the certification of the AI system; (c) Prohibiting the deployment or operation of the AI system until compliance is achieved; (d) Mandating independent audits of the entity’s processes at their own cost; (e) Recommending the temporary or permanent suspension of the entity’s AI-related operations in cases of persistent or egregious non-compliance. (6) Entities developing, deploying, or operating AI systems exempted from certification under Section 11(3) shall be encouraged to voluntarily comply with the provisions of this Act. Non-compliance by such entities shall not attract any penalties, provided that: (a) The AI system remains within the scope of the exemption criteria specified in Section 11(3); (b) The entity maintains the incident reporting and response protocols as required under Section 11(4); (c) The entity cooperates with the IAIC in the event of any investigation or inquiry related to the AI system. (7) The IAIC shall establish clear guidelines for the determination and imposition of penalties, ensuring transparency, proportionality, and due process. Factors such as the nature, severity, and duration of the non-compliance, the entity’s willingness to cooperate and take remedial measures, and the potential harm caused by the non-compliance shall be considered while deciding the quantum of penalties. (8) Any penalty imposed under this Section shall not prevent the initiation of criminal proceedings against the offender if the same act or omission constitutes an offense under any other law for the time being in force. (9) All sums realized by way of penalties under this Act shall be credited to the Consolidated Fund of India. Related Indian AI Regulation Sources

Section 11 – Registration & Certification of AI Systems PUBLISHED Previous Next Section 11 – Registration & Certification of AI Systems (1) The IAIC shall establish a voluntary certification scheme for AI systems based on their industry use cases and risk levels, on the basis of the means of classification set forth in Chapter II. The certification scheme shall be designed to promote responsible AI development and deployment. (2) The IAIC shall maintain a National Registry of Artificial Intelligence Use Cases as described in Section 12 to register and track the development and deployment of AI systems across various sectors. The registry shall be used to inform the development and refinement of the certification scheme and to promote transparency and accountability in artificial intelligence governance. (2) The certification scheme shall be based on a set of clear, objective, and risk-proportionate criteria that assess the inherent purpose, technical characteristics, and potential impacts of AI systems. (3) AI systems classified as narrow or medium risk under Section 7 and AI-Pre under sub-section (8) of Section 6 may be exempt from the certification requirement if they meet one or more of the following conditions: (i) The AI system is still in the early stages of development or testing and has not yet achieved technical or economic thresholds for effective standardisation; (ii) The AI system is being developed or deployed in a highly specialized or niche application area where certification may not be feasible or appropriate; or (iii) The AI system is being developed or deployed by start-ups, micro, small & medium enterprises, or research institutions. (4) AI systems that qualify for exemptions under sub-section (3) must establish and maintain incident reporting and response protocols specified in Section 19. Failure to maintain these protocols may result in the revocation of the exemption. (5) Applicability of Section 4 Classification Methods: (i) The conceptual methods of classification outlined in Section 4 are intended for consultative and advisory purposes only. Their application is not mandatory for the National AI Registry of Use Cases under this Section. The IAIC is empowered to: (a) Issue advisories, clarifications, and guidance documents on the interpretation and application of the classification methods outlined in Section 4. (b) Provide sector-specific recommendations for the voluntary use of these classification methods by stakeholders, including developers, regulators, and industry professionals. (c) While these classification methods are not mandatory, stakeholders are encouraged to adopt them on a self-regulatory basis. Voluntary application of these methods can help: (i) Enhance transparency in AI development. (ii) Promote responsible AI deployment across sectors. (iii) Facilitate alignment with ethical standards outlined in the National Artificial Intelligence Ethics Code (NAIEC) under Section 13. (ii) The IAIC may periodically review and update its advisories, clarifications and guidance documents to reflect advancements in AI technologies and emerging best practices, ensuring that stakeholders have access to the latest guidance for applying these conceptual methods. (6) Notwithstanding anything contained in sub-section (5), entities registering high-risk AI systems as defined in the sub-section (4) of Section 7 and those associated with strategic sectors as specified in Section 9 must apply the conceptual classification methods outlined in Section 4. (7) The certification scheme and the methods of classification specified in Chapter II shall undergo periodic review and updating every 12 months to ensure its relevance and effectiveness in response to technological advancements and market developments. The review process shall include meaningful consultation with sector-specific regulators and market stakeholders. Related Indian AI Regulation Sources Principles for Responsible AI (Part 1) February 2021 Operationalizing Principles for Responsible AI (Part 2) August 2021 The Ethical Guidelines for Application of AI in Biomedical Research and Healthcare March 2023 Fairness Assessment and Rating of Artificial Intelligence Systems (TEC 57050:2023) July 2023 Policy Regarding Use of Artificial Intelligence Tools in District Judiciary July 2025 Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) Committee Report August 2025 Strengthening AI Governance Through Techno-Legal Framework (White Paper, Part 2 of Emerging Policy Priorities Series) January 2026

Liked our Work? Search it now on IndoPacific.App Get Searching Our Research Know more about our Knowledge Base, years of accumulated and developed in-house research at Indic Pacific Legal Research. Search our Research Treasure on IndoPacific.App. :) Global Relations and Legal Policy, Volume 1 [GRLP1] Get this Publication 2020 ISBN 978-93-5407-220-8 Author(s) Akash Manwani, Akshat Mall, Amin Labbafi, Anubhav Banerjee, Arpan Chakravarty, Avishikta Chattopadhyay, Dhanya Visweswaran, Manohar Samal, Mugdha Satpute, Padmja Mishra, Pragya Sharma, Pranay Bhattacharya, Pratham Sharma, Ridhima Bhardwaj, Vasu Sharma Editor(s) Abhivardhan, Amulya Anil IndoPacific.App Identifier (ID) GRLP1 Tags Diplomacy, Geopolitics, Global Relations, Governance, Human Rights, International Law, International Organizations, International Trade, Legal Policy, Policy Related Terms in Techindata.in Explainers Definitions - A - E CEI Classification Class-of-Applications-by-Class-of-Application (CbC) approach Definitions - F - J GAE Indo-Pacific International Algorithmic Law Definitions - K - P Multi-alignment Multipolar World Multipolarity Permeable Indigeneity in Policy (PIP) Phenomena-based concept classification Definitions - Q - U Strategic Autonomy Strategic Hedging Technophobia Definitions - V - Z WANA WENA Whole-of-Government Response Related Articles in Techindata.in Insights 4 Insight(s) on Government Affairs 1 Insight(s) on India-US Relations 1 Insight(s) on governance 1 Insight(s) on Indic Pacific 1 Insight(s) on India 1 Insight(s) on strategic sectors . Previous Item Next Item