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Introduction

In today’s time, everything is online, and the world is interconnected. Cases of data breaches and cyberattacks have been a reality for various organisations and industries, In the recent case (of SAS), Scandinavian Airlines experienced a cyberattack that resulted in the exposure of customer details, highlighting the critical importance of preventing customer privacy. The incident is a wake-up call for Airlines and businesses to evaluate their cyber security measures and learn valuable lessons to safeguard customers’ data. In this blog, we will explore the incident and discuss the strategies for protecting customers’ privacy in this age of digitalisation.

Analysing the backdrop

The incident has been a shocker for the aviation industry, SAS Scandinavian Airlines has been a victim of a cyberattack that compromised consumer data. Let’s understand the motive of cyber crooks and the technique they used :

Motive Behind the Attack: Understanding the reasons that may have driven the criminals is critical to comprehending the context of the Scandinavian Airlines cyber assault. Financial gain, geopolitical conflicts, activism, or personal vendettas are common motivators for cybercriminals. Identifying the purpose of the assault can provide insight into the attacker’s aims and the possible impact on both the targeted organisation and its consumers. Understanding the attack vector and strategies used by cyber attackers reveals the amount of complexity and possible weaknesses in an organisation’s cybersecurity defences. Scandinavian Airlines’ cyber assault might have included phishing, spyware, ransomware, or exploiting software weaknesses. Analysing these tactics allows organisations to strengthen their security against similar assaults.

Impact on Victims: The Scandinavian Airlines (SAS) cyber attack victims, including customers and individuals related to the company, have suffered substantial consequences. Data breaches and cyber-attack have serious consequences due to the leak of personal information.

1)Financial Losses and Fraudulent Activities: One of the most immediate and upsetting consequences of a cyber assault is the possibility of financial loss. Exposed personal information, such as credit card numbers, can be used by hackers to carry out illegal activities such as unauthorised transactions and identity theft. Victims may experience financial difficulties and the need to spend time and money resolving these concerns.

2)Concerns about privacy and personal security: A breach of personal data can significantly impact the privacy and personal security of victims. The disclosed information, including names, addresses, and contact information, might be exploited for nefarious reasons, such as targeted phishing or physical harassment. Victims may have increased anxiety about their safety and privacy, which can interrupt their everyday life and create mental pain.

3) Reputational Damage and Trust Issues: The cyber attack may cause reputational harm to persons linked with Scandinavian Airlines, such as workers or partners. The breach may diminish consumers’ and stakeholders’ faith in the organisation, leading to a bad view of its capacity to protect personal information. This lack of trust might have long-term consequences for the impacted people’s professional and personal relationships.

4) Emotional Stress and Psychological Impact: The psychological impact of a cyber assault can be severe. Fear, worry, and a sense of violation induced by having personal information exposed can create emotional stress and psychological suffering. Victims may experience emotions of vulnerability, loss of control, and distrust toward digital platforms, potentially harming their overall quality of life.

5) Time and Effort Required for Remediation: Addressing the repercussions of a cyber assault demands significant time and effort from the victims. They may need to call financial institutions, reset passwords, monitor accounts for unusual activity, and use credit monitoring services. Resolving the consequences of a data breach may be a difficult and time-consuming process, adding stress and inconvenience to the victims’ lives.

6) Secondary Impacts: The impacts of an online attack could continue beyond the immediate implications. Future repercussions for victims may include trouble acquiring credit or insurance, difficulties finding future work, and continuous worry about exploiting their personal information. These secondary effects can seriously affect victims’ financial and general well-being.

Apart from this, the trust lost would take time to rebuild.

Takeaways from this attack

The cyber-attack on Scandinavian Airlines (SAS) is a sharp reminder of cybercrime’s ever-present and increasing menace. This event provides crucial insights that businesses and people may use to strengthen cybersecurity defences. In the lessons that were learned from the Scandinavian Airlines cyber assault and examine the steps that may be taken to improve cybersecurity and reduce future risks. Some of the key points that can be considered are as follows:

Proactive Risk Assessment and Vulnerability Management: The cyber assault on Scandinavian Airlines emphasises the significance of regular risk assessments and vulnerability management. Organisations must proactively identify and fix possible system and network vulnerabilities. Regular security audits, penetration testing, and vulnerability assessments can help identify flaws before bad actors exploit them.

Strong security measures and best practices: To guard against cyber attacks, it is necessary to implement effective security measures and follow cybersecurity best practices. Lessons from the Scandinavian Airlines cyber assault emphasise the importance of effective firewalls, up-to-date antivirus software, secure setups, frequent software patching, and strong password rules. Using multi-factor authentication and encryption technologies for sensitive data can also considerably improve security.

Employee Training and Awareness: Human mistake is frequently a big component in cyber assaults. Organisations should prioritise employee training and awareness programs to educate employees about phishing schemes, social engineering methods, and safe internet practices. Employees may become the first line of defence against possible attacks by cultivating a culture of cybersecurity awareness.

Data Protection and Privacy Measures: Protecting consumer data should be a key priority for businesses. Lessons from the Scandinavian Airlines cyber assault emphasise the significance of having effective data protection measures, such as encryption and access limits. Adhering to data privacy standards and maintaining safe data storage and transfer can reduce the risks connected with data breaches.

Collaboration and Information Sharing: The Scandinavian Airlines cyber assault emphasises the need for collaboration and information sharing among the cybersecurity community. Organisations should actively share threat intelligence, cooperate with industry partners, and stay current on developing cyber threats. Sharing information and experiences can help to build the collective defence against cybercrime.

Conclusion

The Scandinavian Airlines cyber assault is a reminder that cybersecurity must be a key concern for organisations and people. Organisations may improve their cybersecurity safeguards, proactively discover vulnerabilities, and respond effectively to prospective attacks by learning from this occurrence and adopting the lessons learned. Building a strong cybersecurity culture, frequently upgrading security practices, and encouraging cooperation within the cybersecurity community are all critical steps toward a more robust digital world. We may aim to keep one step ahead of thieves and preserve our important information assets by constantly monitoring and taking proactive actions.

Introduction 

Public infrastructure has traditionally served as the framework for civilisation, transporting people, money, and ideas across time and space, from the iron veins of transcontinental railroads to the unseen arteries of the internet. In democracies where free markets and public infrastructure co-exist, this framework has not only facilitated but also accelerated progress. Digital Public Infrastructure (DPI), which powers inclusiveness, fosters innovation, and changes citizens from passive recipients to active participants in the digital age, is emerging as the new civic backbone as we move away from highways and towards high-speed data. 

DPI makes it possible for innovation at the margins and for inclusion at scale by providing open-source, interoperable platforms for identities, payments, and data exchange. Examples of how the Global South is evolving from a passive consumer of technology to a creator of globally replicable governance models are India’s Aadhaar (digital identification), UPI (real-time payments), and DigiLocker (data empowerment). As the ‘digital commons’ emerges, DPI does more than simply link users; it also empowers citizens, eliminates inefficiencies from the past, and reimagines the creation and distribution of public value in the digital era. 

Securing the Digital Infrastructure: A Contemporary Imperative

As humans, we are already the inhabitants of the future, we stand at the temporal threshold for reform. Digital Infrastructure is no longer just a public good. It’s now a strategic asset, akin to oil pipelines in the 20th century. India is recognised globally for the introduction of “India Stack”, through which the face of digital payments has also been changed. The economic value contributed by DPIs to India’s GDP is predicted to reach 2.9-4.2 percent by 2030, having already reached 0.9% in 2022. Its role in India’s economic development is partly responsible for its success; among emerging market economies, it helped propel India to the top of the revenue administrations’ digitalisation index. The other portion has to do with how India’s social service delivery has changed across the board. By enabling digital and financial inclusion, it has increased access to education (DIKSHA) and is presently being developed to offer agricultural (VISTAAR) and digital health (ABDM) services. 

Securing the Foundations: Emerging Threats to Digital Public Infrastructure

The rising prominence of DPI is not without its risks, as adversarial forces are developing with comparable sophistication. The core underpinnings of public digital systems are the target of a new generation of cyber threats, ranging from hostile state actors to cybercriminal syndicates. The threats pose a great risk to the consistent development endeavours of the government. To elucidate, targeted attacks on Biometric databases, AI-based Misinformation and Psychological Warfare, Payment System Hacks, State-sponsored malware, cross-border phishing campaigns, surveillance spyware and Sovereign Malware are modern-day examples of cyber threats. 

To secure DPI, a radical rethink beyond encryption methods and perimeter firewalls is needed. It requires an understanding of cybersecurity that is systemic, ethical, and geopolitical. Democracy, inclusivity, and national integrity are all at risk from DPI. To preserve the confidence and promise of digital public infrastructure, policy frameworks must change from fragmented responses to coordinated, proactive and people-centred cyber defence policies.

CyberPeace Recommendations

Powering Progress, Ignoring Protection: A Precarious Path

The Indian government is aware that cyberattacks are becoming more frequent and sophisticated in the nation. To address the nation’s cybersecurity issues, the government has implemented a number of legislative, technical, and administrative policy initiatives. While the initiatives are commendable, there are a few Non-Negotiables that need to be in place for effective protection: 

• DPIs must be declared Critical Information Infrastructure. In accordance with the IT Act, 2000, the DPI (Aadhaar, UPI, DigiLocker, Account Aggregator, CoWIN, and ONDC) must be designated as Critical Information Infrastructure (CII) and be supervised by the NCIIPC, just like the banking, energy, and telecom industries. Give NCIIPC the authority to publish required security guidelines, carry out audits, and enforce adherence to the DPI stack, including incident response protocols tailored to each DPI. 
• To solidify security, data sovereignty, and cyber responsibility, India should spearhead global efforts to create a Global DPI  Cyber Compact through the “One Future Alliance” and the G20. To ensure interoperable cybersecurity frameworks for international DPI projects, promote open standards, cross-border collaboration on threat intelligence, and uniform incident reporting guidelines. 
• Establish a DPI Threat Index to monitor vulnerabilities, including phishing attacks, efforts at biometric breaches, sovereign malware footprints, spikes in AI misinformation, and patterns in payment fraud. Create daily or weekly risk dashboards by integrating data from state CERTs, RBI, UIDAI, CERT-In, and NPCI. Use machine learning (ML) driven detection systems. 
• Make explainability audits necessary for AI/ML systems used throughout DPI to make sure that the decision-making process is open, impartial, and subject to scrutiny (e.g., welfare algorithms, credit scoring). Use the recently established IndiaAI Safety Institute in line with India’s AI mission to conduct AI audits, establish explanatory standards, and create sector-specific compliance guidelines. 

References

• https://orfamerica.org/newresearch/dpi-catalyst-private-sector-innovation?utm_source=chatgpt.com 
• https://www.institutmontaigne.org/en/expressions/indias-digital-public-infrastructure-success-story-world 
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2116341 
• https://www.pib.gov.in/PressReleaseIframePage.aspx?PRID=2033389 
• https://www.governancenow.com/news/regular-story/dpi-must-ensure-data-privacy-cyber-security-citizenfirst-approach 

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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/