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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

Released in July 2025 by a drafting committee constituted by MeitY, chaired by Prof. Balaraman Ravindran (IIT Madras), this document provides recommendations that include establishing an AI Governance Group (AIGG) for policy coordination, a Technology & Policy Expert Committee (TPEC) for expert advice, and empowering the AI Safety Institute (AISI) for technical validation and safety research. The guidelines advocate a "whole of government" approach with sectoral regulators maintaining enforcement powers. India AI Regulation Landscape 101 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. India AI Governance Guidelines: Enabling Safe and Trusted AI Innovation Released in July 2025 by a drafting committee constituted by MeitY, chaired by Prof. Balaraman Ravindran (IIT Madras), this document provides recommendations that include establishing an AI Governance Group (AIGG) for policy coordination, a Technology & Policy Expert Committee (TPEC) for expert advice, and empowering the AI Safety Institute (AISI) for technical validation and safety research. The guidelines advocate a "whole of government" approach with sectoral regulators maintaining enforcement powers. Previous Next November 2025 Issuing Authority Ministry of Electronics and Information Technology (MeitY), Government of India Type of Legal / Policy Document Guidance documents with normative influence Status Enacted Regulatory Stage Pre-regulatory Binding Value Guidance documents with normative influence Read the Document AI Regulation Visualisation Related Long-form Insights on IndoPacific.App 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 NIST Adversarial Machine Learning Taxonomies: Decoded, IPLR-IG-016 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 15 – Guidance Principles for AI-related Agreements Section 15 – Guidance Principles for AI-related Agreements Section 16 – Guidance Principles for AI-related Corporate Governance Section 16 – Guidance Principles for AI-related Corporate Governance Section 17 – Post-Deployment Monitoring of High-Risk AI Systems Section 17 – Post-Deployment Monitoring of High-Risk AI Systems

AI Red Teaming Explainers The Complete Glossary AI Red Teaming Date of Addition 17 Oct 2025 A systematic adversarial testing methodology that probes AI systems for vulnerabilities, unintended behaviors, socio-technical harms, and potential misuse scenarios through simulated attack patterns and boundary condition exploration. Unlike traditional software security testing, AI red teaming addresses emergent behaviors, alignment failures, bias amplification, and novel attack vectors specific to machine learning systems including prompt injection, jailbreaking, and data poisoning. The practice has evolved from cybersecurity roots into a board-level governance requirement for organizations deploying generative AI in production environments. Related Long-form Insights on IndoPacific.App Artificial Intelligence Governance using Complex Adaptivity: Feedback Report, First Edition, 2024 Learn More Decoding the AI Competency Triad for Public Officials [IPLR-IG-014] 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 AI Bias & the Overlap of AI Diplomacy and Governance Ethics Dilemmas 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

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. :) The Policy Purpose of a Multipolar Agenda for India, First Edition, 2023 Get this Publication 2023 ISBN 978-81-959932-4-6 Author(s) Abhivardhan Editor(s) Not Applicable IndoPacific.App Identifier (ID) IPLR-IG-001 Tags Abhivardhan, Diplomacy, Emerging Powers, Foreign Policy, Geopolitics, Global Order, India, International Relations, Multipolarity, Strategic Studies Related Terms in Techindata.in Explainers Definitions - A - E AI Knowledge Chain Accountability All-Comprehensive Approach Automation CEI Classification Class-of-Applications-by-Class-of-Application (CbC) approach Compute Ethics-based concept classification Definitions - F - J GAE Indo-Pacific International Algorithmic Law Definitions - K - P Multi-alignment Multipolar World Multipolarity Performance Effect Permeable Indigeneity in Policy (PIP) Definitions - Q - U Skirmish Propaganda Capacity Destruction Strategic Autonomy Strategic Hedging Technology Transfer Technophobia Definitions - V - Z WANA WENA 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

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

Income Tax Appellate Tribunal Bengaluru February 2025 case involving AI-hallucinated fake citations withdrawn under Section 254(2) highlighting judicial AI usage risks. India AI Regulation Landscape 101 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. Buckeye Trust v. PCIT, ITA No. 1051/Bang/2024 (ITAT Bengaluru Bench 2024-2025) Income Tax Appellate Tribunal Bengaluru February 2025 case involving AI-hallucinated fake citations withdrawn under Section 254(2) highlighting judicial AI usage risks. Previous Next February 2025 Issuing Authority Income Tax Appellate Tribunal, Bengaluru Type of Legal / Policy Document Judicial Pronouncements - National Court Precedents Status Enacted Regulatory Stage Miscellaneous Binding Value Legally binding instruments enforceable before courts Read the Document AI Regulation Visualisation Related Long-form Insights on IndoPacific.App 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 Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] 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 15 – Guidance Principles for AI-related Agreements Section 15 – Guidance Principles for AI-related Agreements Section 16 – Guidance Principles for AI-related Corporate Governance Section 16 – Guidance Principles for AI-related Corporate Governance

SOTP Classification Date of Addition 26 April 2024 This is one of the two Classification Methods in which Artificial Intelligence could be recognised as a Subject, an Object or a Third Party in a legal issue or dispute. This idea was proposed in the 2020 Handbook on AI and International Law (2021). Related Long-form Insights on IndoPacific.App 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Regularizing Artificial Intelligence Ethics in the Indo-Pacific [GLA-TR-002] 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 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 Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] 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 Artificial Intelligence and Policy in India, Volume 5 [AIPI-V5] 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 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 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 Previous Term Next Term Explainers 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

Distributed Ledger Explainers The Complete Glossary Distributed Ledger Date of Addition 26 Apr 2024 A distributed ledger (also called a shared ledger or distributed ledger technology or DLT) is the consensus of replicated, shared, and synchronized digital data that is geographically spread (distributed) across many sites, countries, or institutions. Related Long-form Insights on IndoPacific.App Reinventing & Regulating Policy Use Cases of Web3 for India [VLiGTA-TR-004] Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] 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 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

Section 23 – Content Provenance and Identification PUBLISHED Previous Next Section 23 - Content Provenance and Identification (1) AI systems that generate or manipulate content must establish and maintain robust mechanisms for source attribution, origin documentation, and ethical data handling. These mechanisms shall integrate technical measures, human oversight, and compliance with applicable laws to ensure transparency and accountability in the following manner: (i) Clearly document the origins of all content sources, ensuring that: (a) Sources are identified with precision, including the website, database, or platform from which data is obtained; (b) Only publicly available data or data acquired with explicit, documented consent from the data subject is utilised, where such data collection adheres to ethical practices, defined as: (i) Ensuring transparency by publicly disclosing the purpose, scope, and intended use of data collection, enabling accountability across all applications of the AI system; (ii) Complying with all applicable laws, including the Digital Personal Data Protection Act, 2023, and respecting the terms of service, intellectual property rights, and access restrictions of data sources, to safeguard the integrity of content generation and manipulation processes; (iii) Avoiding the collection of sensitive personal data unless strictly necessary, legally permitted, and subject to heightened safeguards, including mandatory risk assessments for applications involving high-stakes decision-making or vulnerable populations; (iv) Implementing measures to prevent unauthorized access, use, or distribution of the collected data, including the use of anonymisation or pseudonymisation techniques to minimize the risk of re-identification, where: (a) Anonymisation refers to the irreversible process of transforming data into a form where the data subject cannot be identified, meeting standards of irreversibility as per best practices; (b) Pseudonymisation refers to replacing identifying characteristics with artificial identifiers, ensuring that re-identification is only possible with additional, securely stored information; (v) Permitting the use of in-copyright works for text and data mining (TDM) purposes, provided that: (a) The TDM is conducted for non-commercial research, statistical, or operational optimization purposes, supporting innovation while respecting the rights of content creators; (b) The entity has lawful access to the data, either through public availability, consent, or authorised licensing; (c) The TDM process does not involve the reproduction or distribution of the original copyrighted works beyond what is necessary for the mining process, and appropriate attribution is provided where feasible; (vi) For AI systems deployed in strategic sectors under applicable regulations, additional compliance with sector-specific data security and national interest requirements shall apply, as prescribed by the relevant authority. (c) Any use of web scraping adheres to the target website’s terms of service and robots.txt protocols, with prior written permission obtained where required. (ii) Maintain comprehensive and auditable technical documentation of data collection methods used in training datasets, which shall include: (a) A detailed description of acquisition techniques, such as APIs, manual collection, or automated scraping, ensuring all methods comply with legal and ethical standards; (b) Evidence of compliance with the Digital Personal Data Protection Act, 2023, for any personal data collected, including records of user consent where applicable; (c) A commitment to data minimization, ensuring that only data necessary for the specified purpose is collected and processed. (iii) Establish and maintain verifiable records of data provenance, categorizing data as follows: (a) Personal data, processed strictly in accordance with the Digital Personal Data Protection Act, 2023, with documented consent and purpose limitation; (b) Non-personal data, collected through authorized and transparent methods, ensuring no violation of intellectual property rights or website terms of service; (c) Synthetic data, generated by the AI system itself, with clear documentation of the generation process to distinguish it from real-world data and prevent misrepresentation. (2) Accountability for tracking AI-generated content shall be determined by the specific use cases of the AI system, such that for end-users and business end-users of AI systems, accountability and liability for AI-generated content must be examined based on factors such as: (i) Whether they intentionally misused or tampered with the AI system despite being aware of its key limitations; (ii) Whether they failed to exercise reasonable care and due diligence in the utilisation of the AI system; (iii) Whether they knowingly propagated or disseminated AI-generated content that could cause harm; (3) Intermediaries that host, publish, or make available AI-generated content shall: (i) Implement non-discriminatory content policies that: (a) Prohibit demonetisation or de-prioritisation of content solely based on its AI-generated nature when properly watermarked and disclosed; (b) Maintain parity in content recommendation algorithms between human-created and AI-generated works meeting provenance requirements; (c) Provide appeal mechanisms for creators affected by automated moderation of AI-generated content; (4) Watermarking techniques must incorporate machine-readable metadata containing: (i) Scraping methodology classification; (ii) Geographic origin of training data sources; (iii) Licensing status of underlying datasets; (5) Developers, owners, and operators of AI systems as described in sub-sections (3) to (7) of Section 6 shall obtain and maintain adequate liability insurance coverage proportionate to their commercial classification and risk profile. The coverage must include: (i) Professional indemnity insurance to cover incidents involving inaccurate, inappropriate or defamatory AI-generated content; (ii) Cyber risk insurance to cover incidents related to data breaches, network security failures or other cyber incidents involving AI-generated content; (iii) General commercial liability insurance to cover incidents causing third-party injury, damage or other legally liable scenarios involving AI-generated content; (v) Specific coverage for claims arising from data scraping activities conducted in the development, training, or operation of the AI system. (6) Exceptions for AI-Preview (AI-Pre) Systems: AI systems as described in sub-section (8) of Section 6 shall be exempt from sub-section (5) requirements only if: (i) User base remains below 50,000 real-time active testers (ii) No personal/sensitive data processing occurs (iii) Annual development budget remains under ₹5 crore (iv) System displays prominent "Preview Version" watermarks (v) Revenue generation is limited to subscription fees for testing purposes, nominal one-time access fees, or cost recovery mechanisms that do not constitute full commercial deployment, provided that: (a) Such revenue does not exceed 15% of the developing entity's total annual revenue (b) All monetary transactions are clearly disclosed as supporting a preview or test version (c) No claims of complete or commercial-grade functionality are made in marketing materials (vi) The system is not used to generate, simulate, or manipulate user consent for any purpose (vii) All interactions regarding terms of service, permissions, or agreements are conducted without AI intermediation (viii) Regular checks or audits verify the system's inputs and outputs do not engage in preference or opinion manipulation (ix) The developer maintains comprehensive logs of all system prompts and responses that could influence user decision-making (x) Users are explicitly informed if the system utilises persuasive or preference-shaping techniques in its responses (xi) Educational implementations, provided that content generation capabilities are supervised; (xii) Research applications, provided that in the case of research institutions, centres and firms: (a) Limited usage by verified research entities; (b) Publication of findings adheres to responsible disclosure guidelines; (c) Basic insurance coverage for potential third-party effects is maintained. (xiii) Terms and conditions are easily accessible in clear and plain language, and a readily contactable person is designated in accordance with sub-sections (9) and (28) of Section 2 of the Consumer Protection Act, 2019, to handle user queries, complaints, or grievances. (xiv) Appropriate insurance is maintained for any public-facing implementations. (7) AI systems as described in sub-section (8) of Section 6 exceeding any criteria in (6) must: (i) Obtain insurance within 30 days of threshold breach (ii) Reclassify under appropriate Section 6 commercial category (8) The minimum insurance coverage required for AI content generation systems shall be: (vi) ₹ 50 crores for AI-S (Artificial Intelligence as a System) and AI-IaaS (Artificial Intelligence-enabled Infrastructure as a Service) under sub-sections (6) and (7) of Section 6 respectively (vii) ₹ 25 crores for AI-Pro (Artificial Intelligence as a Product) and AIaaS (Artificial Intelligence as a Service) under sub-sections (3) and (4) of Section 6 respectively (viii) ₹ 10 crores for AI-Com (Artificial Intelligence as a Component) under sub-section (5) of Section 6 (ix) ₹ 2 crores for AI-Pre (Artificial Intelligence for Preview) under sub-section (8) of Section 6 with public-facing implementations (9) The IAIC shall establish and maintain a public registry of open-access technical methods to identify and examine AI-generated content, accessible to end-users, business users, and government officials. This registry shall provide clear instructions for using these methods and information on their validity; (10) This Section shall apply to all AI systems that generate or manipulate content, regardless of the content’s purpose or intended use, including AI systems that generate text, images, audio, video, or any other forms of content. Related Indian AI Regulation Sources Amitabh Bachchan v. Rajat Nagi & Ors., CS(COMM) 819/2022, Delhi High Court, Order dated November 25, 2022 November 2022 Advisory on Ethical Use of Social Media and Deepfakes in Elections May 2024 Advisory on Labeling AI-Generated and Synthetic Content in Elections January 2025 Karan Johar v. India Pride Advisory Private Ltd. & Ors. ("Shaadi Ke Director Karan Aur Johar"), COM IPR Suit (L.) No. 17863/2024, Bombay High Court, Order dated March 7, 2025 March 2025 Advisory on Enhanced Standards for AI-Generated and Synthetic Content in Elections (Bihar Assembly Elections) October 2025 Aishwarya Rai Bachchan v. Aishwaryaworld.com & Ors., CS(COMM) 956/2025, Delhi High Court, Order dated September 9, 2025 September 2025 Akkineni Nagarjuna v. www.bfxxx.org & Ors., CS(COMM) 1023/2025, Delhi High Court, Order dated September 25, 2025 September 2025 Sudhir Chaudhary v. Meta Platforms Inc & Ors., CS(COMM) 1089/2025, Delhi High Court, Order dated October 10, 2025 October 2025 Suniel V Shetty v. John Doe & Ashok Kumar, COM IP Suit (L) No. 32130/2025, Bombay High Court, Order dated October 10, 2025 October 2025 Hrithik Roshan v. Ashok Kumar/John Doe & Ors., CS(COMM) 1107/2025, Delhi High Court, Order dated October 15, 2025 October 2025

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. :) 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Get this Publication 2021 ISBN 978-81-957087-1-0 Author(s) Abhivardhan, Aditi Sharma, Akash Manwani, Arundhati Kale, Dev Tejnani, Manohar Samal, Mayank Narang, Mridutpal Bhattacharyya, Saakshi Agarwal, Sameer Samal, Sanad Arora Editor(s) Abhivardhan, Akash Manwani, Kshitij Naik, Suman Kalani IndoPacific.App Identifier (ID) RHB 2020 ISAIL Tags AI, Data Science, Ethics, Governance, Handbook, Innovation, International Law, Legal Studies, Policy Related Terms in Techindata.in Explainers Definitions - A - E AI as a Concept AI as an Object AI as a Subject AI as a Third Party AI Explainability Clause Accountability Definitions - F - J Framework Fatigue General intelligence applications with multiple short-run or unclear use cases as per industrial and regulatory standards (GI2) General intelligence applications with multiple stable use cases as per relevant industrial and regulatory standards (GI1) Generative AI applications with a collection of standalone use cases related to one another (GAI2) Intended Purpose / Specified Purpose Definitions - K - P Manifest Availability Multipolar World Multipolarity Omnipotence Omnipresence Phenomena-based concept classification Privacy by Default Privacy by Design Definitions - Q - U SOTP Classification Semi-Supervised Learning Strategic Autonomy Technical concept classifcation Technology by Default Technology Distancing Technology Transfer Definitions - V - Z WANA WENA Whole-of-Government Response Related Articles in Techindata.in Insights 29 Insight(s) on AI Ethics 8 Insight(s) on AI and Copyright Law 7 Insight(s) on AI and Competition Law 7 Insight(s) on AI and media sciences 7 Insight(s) on AI regulation 5 Insight(s) on AI Governance 3 Insight(s) on AI and Evidence Law 3 Insight(s) on AI literacy 2 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

Generative AI applications with a collection of standalone use cases related to one another (GAI2) Explainers 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 Generative AI applications with a collection of standalone use cases related to one another (GAI2) Date of Addition 26 April 2024 This is an ontological sub-category of Generative AI applications. Such Generative AI Applications have more than one standalone use cases, which are related to one another. The best example of such a Generative AI Application is that of GPT-4's recent update, which can create text and images based on human prompts, and modify them as per requirements. This idea was proposed in Deciphering Regulative Methods for Generative AI, VLiGTA-TR-002 (2023). Related Long-form Insights on IndoPacific.App 2021 Handbook on AI and International Law [RHB 2021 ISAIL] Learn More Regulatory Sandboxes for Artificial Intelligence: Techno-Legal Approaches for India [ISAIL-TR-002] Learn More Deciphering Artificial Intelligence Hype and its Legal-Economic Risks [VLiGTA-TR-001] Learn More Promoting Economy of Innovation through Explainable AI [VLiGTA-TR-003] 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 Navigating Risk and Responsibility in AI-Driven Predictive Maintenance for Spacecraft, IPLR-IG-009, First Edition, 2024 Learn More Sections 4-9, AiACT.IN V4 Infographic Explainers Learn More Averting Framework Fatigue in AI Governance [IPLR-IG-013] Learn More [AIACT.IN V4] Draft Artificial Intelligence (Development & Regulation) Act, 2023, Version 4 Learn More Indo-Pacific Research Ethics Framework on Artificial Intelligence Use [IPac AI] Learn More Normative Emergence in Cyber Geographies: International Algorithmic Law in a Multipolar Technological Order, First Edition Learn More 2020 Handbook on AI and International Law [RHB 2020 ISAIL] Learn More Previous Term Next Term

Section 10-A – Composition and Functions of the Institute PUBLISHED Previous Next Section 10-A – Composition and Functions of the Institute (1) With effect from the date notified by the Central Government, there shall be established the Indian Artificial Intelligence Safety Institute (AISI), a statutory body for the purposes of this Act. (2) The Indian Artificial Intelligence Safety Institute (AISI) shall be established as an autonomous body corporate with perpetual succession, a common seal, and the power to acquire, hold and transfer property, both movable and immovable, and to contract and be contracted, and sue or be sued by its name. (3) The Governing Body of the Indian Artificial Intelligence Safety Institute shall consist of the following members: (i) A Director General of AI Safety, with at least 15 years of experience in artificial intelligence research, who shall serve as the Chief Executive Officer of AISI. (ii) One representative from the Ministry of Electronics and Information Technology (MeitY), not below the rank of Joint Secretary. (iii) One representative from the Ministry of Science and Technology (DST), not below the rank of Joint Secretary. (iv) One representative from the Ministry of Defence, not below the rank of Joint Secretary. (v) One representative from the Ministry of Communications, not below the rank of Joint Secretary. (vi) One representative from NITI Aayog, not below the rank of Joint Secretary. (vii)One representative from the Committee for AI Centers of Excellence (CoEs) as an ex-officio member (viii) One Representative from the Committee for Technical Institutions in Critical AI Research as an ex-officio member (ix) One Representative from the Committee on AI Ethics and Safety as an ex-officio member (4) In addition to the Governing Body, AISI shall include the following ex-officio members: (i) The Principal Scientific Advisor to the Government of India, or their nominee. (ii) One member from the Prime Minister’s Economic Advisory Council . (iii) One representative, being a government official or expert appointed by the Central Government, responsible for coordinating with global AI safety institutes to ensure knowledge exchange and collaboration on emerging risks and best practices. (5) The AISI shall establish specialized committees as deemed necessary for fulfilling its mandate. These committees shall include but are not limited to: (i) Committee for AI Centers of Excellence (CoEs) : This committee shall represent all AI-related Centers of Excellence across India. (ii) Committee for Technical Institutions in Critical AI Research : This committee shall coordinate with technical institutions engaged in critical research on AI systems. (iii) Committee on AI Ethics and Safety : This committee shall guide AISI on ethical principles governing AI systems. (6) The AISI shall undertake the following functions under this Act: (i) Develop protocols for risk assessment, monitoring, and mitigation concerning high-risk AI applications, particularly in strategic sectors such as healthcare, defence, finance, and public administration. (ii) Formulate and establish safety standards for high-risk AI applications for the IAIC. These standards shall be aligned with national security priorities and international norms governing AI safety. (iii) Conduct annual audits of high-risk AI systems deployed across various sectors. The findings from these audits shall be reported to IAIC for further action or policy formulation. (iv) Undertake research initiatives focused on identifying emerging risks associated with new developments in artificial intelligence. Such research shall be conducted in partnership with IAIC, academic institutions, technical bodies and centres of excellence (CoEs), and international organizations dedicated to AI safety. (v) Submit an annual report to the Central Government and IAIC, detailing safety incidents, audit findings, and research advancements. (7) AISI may engage in international partnerships and dialogues, contributing to India’s leadership in responsible AI governance. Related Indian AI Regulation Sources