#FactCheck - AI-Generated Video Falsely Shared as Iran’s Attack on Israeli Nuclear Site

Data has become a critical asset for the advancement of a nation’s economic, social, and technological development. India’s emergence as a global digital economy hub makes it necessary to create a robust framework that addresses the challenges and opportunities of digital transformation. The Indian government introduced the Draft National Data Governance Framework Policy in 2022, aiming to create a comprehensive data handling and governance framework. This policy draft addresses key challenges in data management, privacy, and digital economy growth.  As per the recent media reports, the Draft National Data Governance Policy so prepared is under the finalisation stage, the government specified in its implementation document for the Budget 2023-24 announcement. The policy also aims to address the country's AI adoption and the issue of lack of datasets by providing widespread access to anonymized data.

Background and Need for the Policy

India has a robust digital economy with its adoption of the Digital India Initiative, Aadhaar digital identification, UPI for seamless payments and many more. In India, 751.5 million people connect to the internet, and is home to 462.0 million social media users in January 2024, equivalent to 32.2% of its total population (Data Reportal 2024). This has brought challenges including data privacy concerns, cybersecurity threats, digital exclusion, and a need for better regulation frameworks. To overcome them, the Draft National Data Governance Policy has been designed to provide institutional frameworks for data rules, standards, guidelines, and protocols for the sharing of non-personal data sets in a manner that ensures privacy, security, and trust so that they remain secure, transparent, and accountable.

Objectives omphasizesf the Framework 

The objective of the Framework Policy is to accelerate Digital Governance in India. The framework will standardize data management and security standards across the Government. It will promote transparency, accountability, and ownership in Non-Personal data and dataset access and build a platform to receive and process data requests. It will also set quality standards and promote the expansion of the datasets program and overall non-personal ecosystem. Further, it aims to build India’s digital government goals and capacity, knowledge, and competency in Government departments and entities. All this would be done while ensuring greater citizen awareness, participation, and engagement. 

Key Provisions of the Draft Policy 

The Draft Framework Policy aims to establish a cohesive digital governance ecosystem in India that balances the need for data utilization with protecting citizens' privacy rights. It sets up an institutional framework of the "India Data Management Office (IDMO) set up under the Digital India Corporation (DIC) which will be responsible for developing rules, standards, and guidelines under this Policy. 

The key provisions of the framework policy include: 

• Promoting interoperability among government digital platforms, ensuring data privacy through data anonymization and security, and enhancing citizen access to government services through digital means. 
• The policy e the creation of unified digital IDs, a standardisation in digital processes, and data-sharing guidelines across ministries to improve efficiency. 
• It also focuses on building digital infrastructure, such as cloud services and data centres in order to support e-governance initiatives. 
• Furthermore, it encourages public-private partnerships and sets guidelines for accountability and transparency in digital governance.

Implications and Concerns of the Framework 

• The policy potentially impacts data sharing in India as it mentions data anonymization. The scale of data that would need to be anonymised in India is at a very large scale and it could become a potential challenge to engage in. 
• Data localization and cross-border transfers have raised concerns among global tech companies and trade partners. They argue that such requirements could increase operational costs and hinder cross-border data flows. Striking a balance between protecting national interests and facilitating business operations remains a critical challenge.
• Another challenge associated with the policy is over-data centralization under the IDMO and the potential risks of government overreach in data access.

Key Takeaways and Recommendations

The GDPR in the European Union and the Digital Personal Data Protection Act passed in 2023 in India and many others are the data privacy laws in force in different countries. The policy needs to be aligned with the DPDP Act, 2023 and be updated as per the recent developments. It further needs to maintain transparency over the sharing of data and a user’s control. The policy needs engagement with industry experts, privacy advocates, and civil society to ensure a balance of innovation with privacy and security.

Conclusion

The Draft National Data Governance Framework Policy of 2022 represents a significant stage in shaping India's digital future. It ensures the evolution of data governance evolves alongside technological advancements. The framework policy seeks to foster a robust digital ecosystem that benefits citizens, businesses, and the government alike by focusing on the essentials of data privacy, transparency, and security. However, achieving this vision requires addressing concerns like data centralisation, cross-border data flows, and maintaining alignment with global privacy standards. Continued engagement with stakeholders and necessary updates to the draft policy will be crucial to its success in balancing innovation with user rights and data integrity. The final version of the policy is expected to be released soon.

References

• https://meity.gov.in/writereaddata/files/National-Data-Governance-Framework-Policy.pdf
• https://datareportal.com/?utm_source=DataReportal&utm_medium=Country_Article_Hyperlink&utm_campaign=Digital_2024&utm_term=India&utm_content=Home_Page_Link
• https://www.imf.org/en/Publications/fandd/issues/2023/03/data-by-people-for-people-tiwari-packer-matthan
• https://inc42.com/buzz/draft-national-data-governance-policy-under-finalisation-centre/  
• https://legal.economictimes.indiatimes.com/news/industry/government-unveiled-national-data-governance-policy-in-budget-2023/97680515

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"Cybercriminals are unleashing a surprisingly high volume of new threats in this short period of time to take advantage of inadvertent security gaps as organizations are in a rush to ensure business continuity.”

Cyber security firm Fortinet on Monday announced that over the past several weeks, it has been monitoring a significant spike in COVID-19 related threats.

An unprecedented number of unprotected users and devices are now online with one or two people in every home connecting remotely to work through the internet. Simultaneously there are children at home engaged in remote learning and the entire family is engaged in multi-player games, chatting with friends as well as streaming music and video. The cybersec firm’s FortiGuard Labs is observing this perfect storm of opportunity being exploited by cybercriminals as the Threat Report on the Pandemic highlights:

A surge in Phishing Attacks: The research shows an average of about 600 new phishing campaigns every day. The content is designed to either prey on the fears and concerns of individuals or pretend to provide essential information on the current pandemic. The phishing attacks range from scams related to helping individuals deposit their stimulus for Covid-19 tests, to providing access to Chloroquine and other medicines or medical device, to providing helpdesk support for new teleworkers.

Phishing Scams Are Just the Start: While the attacks start with a phishing attack, their end goal is to steal personal information or even target businesses through teleworkers. Majority of the phishing attacks contain malicious payloads – including ransomware, viruses, remote access trojans (RATs) designed to provide criminals with remote access to endpoint systems, and even RDP (remote desktop protocol) exploits.

A Sudden Spike in Viruses: The first quarter of 2020 has documented a 17% increase in viruses for January, a 52% increase for February and an alarming 131% increase for March compared to the same period in 2019. The significant rise in viruses is mainly attributed to malicious phishing attachments. Multiple sites that are illegally streaming movies that were still in theatres secretly infect malware to anyone who logs on. Free game, free movie, and the attacker is on your network.

Risks for IoT Devices magnify: As users are all connected to the home network, attackers have multiple avenues of attack that can be exploited targeting devices including computers, tablets, gaming and entertainment systems and even online IoT devices such as digital cameras, smart appliances – with the ultimate goal of finding a way back into a corporate network and its valuable digital resources.

Ransomware like attack to disrupt business: If the device of a remote worker can be compromised, it can become a conduit back into the organization’s core network, enabling the spread of malware to other remote workers. The resulting business disruption can be just as effective as ransomware targeting internal network systems for taking a business offline. Since helpdesks are now remote, devices infected with ransomware or a virus can incapacitate workers for days while devices are mailed in for reimaging.

“Though organizations have completed the initial phase of transitioning their entire workforce to remote telework and employees are becoming increasingly comfortable with their new reality, CISOs continue to face new challenges presented by maintaining a secure teleworker business model. From redefining their security baseline, or supporting technology enablement for remote workers, to developing detailed policies for employees to have access to data, organizations must be nimble and adapt quickly to overcome these new problems that are arising”, said Derek Manky, Chief, Security Insights & Global Threat Alliances at Fortinet – Office of CISO.

Introduction

Generative AI models are significant consumers of computational resources and energy required for training and running models. While AI is being hailed as a game-changer, however underneath the shiny exterior, cracks are present which significantly raises concerns for its environmental impact. The development, maintenance, and disposal of AI technology all come with a large carbon footprint. The energy consumption of AI models, particularly large-scale models or image generation systems, these models rely on data centers powered by electricity, often from non-renewable sources, which exacerbates environmental concerns and contributes to substantial carbon emissions.

As AI adoption grows, improving energy efficiency becomes essential. Optimising algorithms, reducing model complexity, and using more efficient hardware can lower the energy footprint of AI systems. Additionally, transitioning to renewable energy sources for data centers can help mitigate their environmental impact. There is a growing need for sustainable AI development, where environmental considerations are integral to model design and deployment.

A breakdown of how generative AI contributes to environmental risks and the pressing need for energy efficiency:

• Gen AI during the training phase has high power consumption, when vast amounts of computational power which is often utilising extensive GPU clusters for weeks or at times even months, consumes a substantial amount of electricity. Post this phase, the inference phase where the deployment of these models takes place for real-time inference, can be energy-extensive especially when we take into account the millions of users of Gen AI.
• The main source of energy used for training and deploying AI models often comes from non-renewable sources which then contribute to the carbon footprint. The data centers where the computations for Gen AI take place are a significant source of carbon emissions if they rely on the use of fossil fuels for their energy needs for the training and deployment of the models. According to a study by MIT, training an AI can produce emissions that are equivalent to around 300 round-trip flights between New York and San Francisco. According to a report by Goldman Sachs, Data Companies will use 8% of US power by 2030, compared to 3% in 2022 as their energy demand grows by 160%.
• The production and disposal of hardware (GPUs, servers) necessary for AI contribute to environmental degradation. Mining for raw materials and disposing of electronic waste (e-waste) are additional environmental concerns. E-waste contains hazardous chemicals, including lead, mercury, and cadmium, that can contaminate soil and water supplies and endanger both human health and the environment. 

Efforts by the Industry to reduce the environmental risk posed by Gen AI 

There are a few examples of how companies are making efforts to reduce their carbon footprint, reduce energy consumption and overall be more environmentally friendly in the long run. Some of the efforts are as under:

• Google's TPUs in particular the Google Tensor are designed specifically for machine learning tasks and offer a higher performance-per-watt ratio compared to traditional GPUs, leading to more efficient AI computations during the shorter periods requiring peak consumption. 
• Researchers at Microsoft, for instance, have developed a so-called “1 bit” architecture that can make LLMs 10 times more energy efficient than the current leading system. This system simplifies the models’ calculations by reducing the values to 0 or 1, slashing power consumption but without sacrificing its performance.
• OpenAI has been working on optimizing the efficiency of its models and exploring ways to reduce the environmental impact of AI and using renewable energy as much as possible including the research into more efficient training methods and model architectures.

Policy Recommendations

We advocate for the sustainable product development process and press the need for Energy Efficiency in AI Models to counter the environmental impact that they have. These improvements would not only be better for the environment but also contribute to the greater and sustainable development of Gen AI.  Some suggestions are as follows:

• AI needs to adopt a Climate justice framework which has been informed by a diverse context and perspectives while working in tandem with the UN’s (Sustainable Development Goals) SDGs.
• Working and developing more efficient algorithms that would require less computational power for both training and inference can reduce energy consumption. Designing more energy-efficient hardware, such as specialized AI accelerators and next-generation GPUs, can help mitigate the environmental impact. 
• Transitioning to renewable energy sources (solar, wind, hydro) can significantly reduce the carbon footprint associated with AI. The World Economic Forum (WEF) projects that by 2050, the total amount of e-waste generated will have surpassed 120 million metric tonnes.
• Employing techniques like model compression, which reduces the size of AI models without sacrificing performance, can lead to less energy-intensive computations. Optimized models are faster and require less hardware, thus consuming less energy.
• Implementing scattered learning approaches, where models are trained across decentralized devices rather than centralized data centers, can lead to a better distribution of energy load evenly and reduce the overall environmental impact.
• Enhancing the energy efficiency of data centers through better cooling systems, improved energy management practices, and the use of AI for optimizing data center operations can contribute to reduced energy consumption.

Final Words

The UN Sustainable Development Goals (SDGs) are crucial for the AI industry just as other industries as they guide responsible innovation. Aligning AI development with the SDGs will ensure ethical practices, promoting sustainability, equity, and inclusivity. This alignment fosters global trust in AI technologies, encourages investment, and drives solutions to pressing global challenges, such as poverty, education, and climate change, ultimately creating a positive impact on society and the environment. The current state of AI is that it is essentially utilizing enormous power and producing a product not efficiently utilizing the power it gets. AI and its derivatives are stressing the environment in such a manner which if it continues will affect the clean water resources and other non-renewable power generation sources which contributed to the huge carbon footprint of the AI industry as a whole.  

References

• https://cio.economictimes.indiatimes.com/news/artificial-intelligence/ais-hunger-for-power-can-be-tamed/111302991
• https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/ 
• https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
• https://www.scientificamerican.com/article/ais-climate-impact-goes-beyond-its-emissions/ ‍
• https://insights.grcglobalgroup.com/the-environmental-impact-of-ai/