Alterations in Personal Data Protection Bill

Introduction

Artificial Intelligence (AI) driven autonomous weapons are reshaping military strategy, acting as force multipliers that can independently assess threats, adapt to dynamic combat environments, and execute missions with minimal human intervention, pushing the boundaries of modern warfare tactics. AI has become a critical component of modern technology-driven warfare and has simultaneously impacted many spheres in a technology-driven world. Nations often prioritise defence for significant investments, supporting its growth and modernisation. AI has become a prime area of investment and development for technological superiority in defence forces. India’s focus on defence modernisation is evident through initiatives like the Defence AI Council and the Task Force on Strategic Implementation of AI for National Security.

The main requirement that Autonomous Weapons Systems (AWS) require is the “autonomy” to perform their functions when direction or input from a human actor is absent. AI is not a prerequisite for the functioning of AWSs, but, when incorporated, AI could further enable such systems. While militaries seek to apply increasingly sophisticated AI and automation to weapons technologies, several questions arise. Ethical concerns have been raised for AWS as the more prominent issue by many states, international organisations, civil society groups and even many distinguished figures.

Ethical Concerns Surrounding Autonomous Weapons

The delegation of life-and-death decisions to machines is the ethical dilemma that surrounds AWS. A major concern is the lack of human oversight, raising questions about accountability. What if AWS malfunctions or violates international laws, potentially committing war crimes? This ambiguity fuels debate over the dangers of entrusting lethal force to non-human actors. Additionally, AWS poses humanitarian risks, particularly to civilians, as flawed algorithms could make disastrous decisions. The dehumanisation of warfare and the violation of human dignity are critical concerns when AWS is in question, as targets become reduced to mere data points. The impact on operators’ moral judgment and empathy is also troubling, alongside the risk of algorithmic bias leading to unjust or disproportionate targeting. These ethical challenges are deeply concerning.

Balancing Ethical Considerations and Innovations

It is immaterial how advanced a computer becomes in simulating human emotions like compassion, empathy, altruism, or other emotions as the machine will only be imitating them, not experiencing them as a human would. A potential solution to this ethical predicament is using a 'human-in-the-loop' or 'human-on-the-loop' semi-autonomous system. This would act as a compromise between autonomy and accountability.

A “human-on-the-loop” system is designed to provide human operators with the ability to intervene and terminate engagements before unacceptable levels of damage occur. For example, defensive weapon systems could autonomously select and engage targets based on their programming, during which a human operator retains full supervision and can override the system within a limited period if necessary.

In contrast, a ‘human-in-the-loop” system is intended to engage individual targets or specific target groups pre-selected by a human operator. Examples would include homing munitions that, once launched to a particular target location, search for and attack preprogrammed categories of targets within the area.

International Debate and Regulatory Frameworks

The regulation of autonomous weapons that employ AI, in particular, is a pressing global issue due to the ethical, legal, and security concerns it contains. There are many ongoing efforts at the international level which are in discussion to regulate such weapons. One such example is the initiative under the United Nations Convention on CertainConventional Weapons (CCW), where member states, India being an active participant, debate the limits of AI in warfare. However, existing international laws, such as the Geneva Conventions, offer legal protection by prohibiting indiscriminate attacks and mandating the distinction between combatants and civilians. The key challenge lies in achieving global consensus, as different nations have varied interests and levels of technological advancement. Some countries advocate for a preemptive ban on fully autonomous weapons, while others prioritise military innovation. The complexity of defining human control and accountability further complicates efforts to establish binding regulations, making global cooperation both essential and challenging.

The Future of AI in Defence and the Need for Stronger Regulations

The evolution of autonomous weapons poses complex ethical and security challenges. As AI-driven systems become more advanced, a growing risk of its misuse in warfare is also advancing, where lethal decisions could be made without human oversight. Proactive regulation is crucial to prevent unethical use of AI, such as indiscriminate attacks or violations of international law. Setting clear boundaries on autonomous weapons now can help avoid future humanitarian crises. India’s defence policy already recognises the importance of regulating the use of AI and AWS, as evidenced by the formation of bodies like the Defence AI Project Agency (DAIPA) for enabling AI-based processes in defence Organisations. Global cooperation is essential for creating robust regulations that balance technological innovation with ethical considerations. Such collaboration would ensure that autonomous weapons are used responsibly, protecting civilians and combatants, while encouraging innovation within a framework prioritising human dignity and international security.

Conclusion

AWS and AI in warfare present significant ethical, legal, and security challenges. While these technologies promise enhanced military capabilities, they raise concerns about accountability, human oversight, and humanitarian risks. Balancing innovation with ethical responsibility is crucial, and semi-autonomous systems offer a potential compromise. India’s efforts to regulate AI in defence highlight the importance of proactive governance. Global cooperation is essential in establishing robust regulations that ensure AWS is used responsibly, prioritising human dignity and adherence to international law, while fostering technological advancement.

References

●     https://indianexpress.com/article/explained/reaim-summit-ai-war-weapons-9556525/

●     https://blogs.icrc.org/law-and-policy/2024/04/25/the-road-less-travelled-ethics-in-the-international-regulatory-debate-on-autonomous-weapon-systems/

●     https://disarmament.unoda.org/the-convention-on-certain-conventional-weapons/background-on-laws-in-the-ccw/

●     https://www.cambridge.org/core/services/aop-cambridge-core/content/view/5FD01B5A96116766C3B1273490B24897/S0892679423000357a.pdf/banning-autonomous-weapons-a-legal-and-ethical-mandate.pdf

●     https://www.pib.gov.in/PressReleasePage.aspx?PRID=1846937

AI and other technologies are advancing rapidly. This has ensured the rapid spread of information, and even misinformation. LLMs have their advantages, but they also come with drawbacks, such as confident but inaccurate responses due to limitations in their training data. The evidence-driven retrieval systems aim to address this issue by using and incorporating factual information during response generation to prevent hallucination and retrieve accurate responses.

What is Retrieval-Augmented Response Generation?

Evidence-driven Retrieval Augmented Generation (or RAG) is an AI framework that improves the accuracy and reliability of large language models (LLMs) by grounding them in external knowledge bases. RAG systems combine the generative power of LLMs with a dynamic information retrieval mechanism. The standard AI models rely solely on pre-trained knowledge and pattern recognition to generate text. RAG pulls in credible, up-to-date information from various sources during the response generation process. RAG integrates real-time evidence retrieval with AI-based responses, combining large-scale data with reliable sources to combat misinformation. It follows the pattern of:

• Query Identification: When misinformation is detected or a query is raised.
• Evidence Retrieval: The AI searches databases for relevant, credible evidence to support or refute the claim.
• Response Generation: Using the evidence, the system generates a fact-based response that addresses the claim.

How is Evidence-Driven RAG the key to Fighting Misinformation?

• RAG systems can integrate the latest data, providing information on recent scientific discoveries.
• The retrieval mechanism allows RAG systems to pull specific, relevant information for each query, tailoring the response to a particular user’s needs.
• RAG systems can provide sources for their information, enhancing accountability and allowing users to verify claims.
• Especially for those requiring specific or specialised knowledge, RAG systems can excel where traditional models might struggle.
• By accessing a diverse range of up-to-date sources, RAG systems may offer more balanced viewpoints, unlike traditional LLMs.

Policy Implications and the Role of Regulation

With its potential to enhance content accuracy, RAG also intersects with important regulatory considerations. India has one of the largest internet user bases globally, and the challenges of managing misinformation are particularly pronounced.

• Indian regulators, such as MeitY, play a key role in guiding technology regulation. Similar to the EU's Digital Services Act, the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, mandate platforms to publish compliance reports detailing actions against misinformation. Integrating RAG systems can help ensure accurate, legally accountable content moderation. 
• Collaboration among companies, policymakers, and academia is crucial for RAG adaptation, addressing local languages and cultural nuances while safeguarding free expression. 
• Ethical considerations are vital to prevent social unrest, requiring transparency in RAG operations, including evidence retrieval and content classification. This balance can create a safer online environment while curbing misinformation.

Challenges and Limitations of RAG

While RAG holds significant promise, it has its challenges and limitations.

• Ensuring that RAG systems retrieve evidence only from trusted and credible sources is a key challenge. 
• For RAG to be effective, users must trust the system. Sceptics of content moderation may show resistance to accepting the system’s responses. 
• Generating a response too quickly may compromise the quality of the evidence while taking too long can allow misinformation to spread unchecked.

Conclusion

Evidence-driven retrieval systems, such as Retrieval-Augmented Generation, represent a pivotal advancement in the ongoing battle against misinformation. By integrating real-time data and credible sources into AI-generated responses, RAG enhances the reliability and transparency of online content moderation. It addresses the limitations of traditional AI models and aligns with regulatory frameworks aimed at maintaining digital accountability, as seen in India and globally. However, the successful deployment of RAG requires overcoming challenges related to source credibility, user trust, and response efficiency. Collaboration between technology providers, policymakers, and academic experts can foster the navigation of these to create a safer and more accurate online environment. As digital landscapes evolve, RAG systems offer a promising path forward, ensuring that technological progress is matched by a commitment to truth and informed discourse.

References

• https://experts.illinois.edu/en/publications/evidence-driven-retrieval-augmented-response-generation-for-onlin 
• https://research.ibm.com/blog/retrieval-augmented-generation-RAG 
• https://medium.com/@mpuig/rag-systems-vs-traditional-language-models-a-new-era-of-ai-powered-information-retrieval-887ec31c15a0 
• https://www.researchgate.net/publication/383701402_Web_Retrieval_Agents_for_Evidence-Based_Misinformation_Detection 

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Introduction

With the increasing frequency and severity of cyber-attacks on critical sectors, the government of India has formulated the National Cyber Security Reference Framework (NCRF) 2023, aimed to address cybersecurity concerns in India. In today’s digital age, the security of critical sectors is paramount due to the ever-evolving landscape of cyber threats. Cybersecurity measures are crucial for protecting essential sectors such as banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises. This is an essential step towards safeguarding these critical sectors and preparing for the challenges they face in the face of cyber threats. Protecting critical sectors from cyber threats is an urgent priority that requires the development of robust cybersecurity practices and the implementation of effective measures to mitigate risks.

Overview of the National Cyber Security Policy 2013

The National Cyber Security Policy of 2013 was the first attempt to address cybersecurity concerns in India. However, it had several drawbacks that limited its effectiveness in mitigating cyber risks in the contemporary digital age. The policy’s outdated guidelines, insufficient prevention and response measures, and lack of legal implications hindered its ability to protect critical sectors adequately. Moreover, the policy should have kept up with the rapidly evolving cyber threat landscape and emerging technologies, leaving organisations vulnerable to new cyber-attacks. The 2013 policy failed to address the evolving nature of cyber threats, leaving organisations needing updated guidelines to combat new and sophisticated attacks.

As a result, an updated and more comprehensive policy, the National Cyber Security Reference Framework 2023, was necessary to address emerging challenges and provide strategic guidance for protecting critical sectors against cyber threats.

Highlights of NCRF 2023

• Strategic Guidance: NCRF 2023 has been developed to provide organisations with strategic guidance to address their cybersecurity concerns in a structured manner.
• Common but Differentiated Responsibility (CBDR): The policy is based on a CBDR approach, recognising that different organisations have varying levels of cybersecurity needs and responsibilities.
• Update of National Cyber Security Policy 2013: NCRF supersedes the National Cyber Security Policy 2013, which was due for an update to align with the evolving cyber threat landscape and emerging challenges.
• Different from CERT-In Directives: NCRF is distinct from the directives issued by the Indian Computer Emergency Response Team (CERT-In) published in April 2023. It provides a comprehensive framework rather than specific directives for reporting cyber incidents.
• Combination of robust strategies: National Cyber Security Reference Framework 2023 will provide strategic guidance, a revised structure, and a proactive approach to cybersecurity, enabling organisations to tackle the growing cyberattacks in India better and safeguard critical sectors.

Rising incidents of malware attacks on critical sectors

In recent years, there has been a significant increase in malware attacks targeting critical sectors. These sectors, including banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises, play a crucial role in the functioning of economies and the well-being of societies. The escalating incidents of malware attacks on these sectors have raised concerns about the security and resilience of critical infrastructure.

• Banking: The banking sector handles sensitive financial data and is a prime target for cybercriminals due to the potential for financial fraud and theft.
• Energy: The energy sector, including power grids and oil companies, is critical for the functioning of economies, and disruptions can have severe consequences for national security and public safety.
• Healthcare: The healthcare sector holds valuable patient data, and cyber-attacks can compromise patient privacy and disrupt healthcare services. Malware attacks on healthcare organisations can result in the theft of patient records, ransomware incidents that cripple healthcare operations, and compromise medical devices.
• Telecommunications: Telecommunications infrastructure is vital for reliable communication, and attacks targeting this sector can lead to communication disruptions and compromise the privacy of transmitted data. The interconnectedness of telecommunications networks globally presents opportunities for cybercriminals to launch large-scale attacks, such as Distributed Denial-of-Service (DDoS) attacks.
• Transportation: Malware attacks on transportation systems can lead to service disruptions, compromise control systems, and pose safety risks.
• Strategic Enterprises: Strategic enterprises, including defence, aerospace, intelligence agencies, and other sectors vital to national security, face sophisticated malware attacks with potentially severe consequences. Cyber adversaries target these enterprises to gain unauthorised access to classified information, compromise critical infrastructure, or sabotage national security operations.
• Government Enterprises: Government organisations hold a vast amount of sensitive data and provide essential services to citizens, making them targets for data breaches and attacks that can disrupt critical services.

Conclusion  

The sectors of banking, energy, healthcare, telecommunications, transportation, strategic enterprises, and government enterprises face unique vulnerabilities and challenges in the face of cyber-attacks. By recognising the significance of safeguarding these sectors, we can emphasise the need for proactive cybersecurity measures and collaborative efforts between public and private entities. Strengthening regulatory frameworks, sharing threat intelligence, and adopting best practices are essential to ensure our critical infrastructure’s resilience and security. Through these concerted efforts, we can create a safer digital environment for these sectors, protecting vital services and preserving the integrity of our economy and society. The rising incidents of malware attacks on critical sectors emphasise the urgent need for updated cybersecurity policy, enhanced cybersecurity measures, a collaboration between public and private entities, and the development of proactive defence strategies. National Cyber Security Reference Framework 2023 will help in addressing the evolving cyber threat landscape, protect critical sectors, fill the gaps in sector-specific best practices, promote collaboration, establish a regulatory framework, and address the challenges posed by emerging technologies. By providing strategic guidance, this framework will enhance organisations’ cybersecurity posture and ensure the protection of critical infrastructure in an increasingly digitised world.