#FactCheck - Old Ajman Fire Video Falsely Linked to Iran Drone Attack on Dubai Airport

Introduction

In India, the rights of children with regard to protection of their personal data are enshrined under the Digital Personal Data Protection Act, 2023 which is the newly enacted digital personal data protection law of India. The DPDP Act requires that for the processing of children's personal data, verifiable consent of parents or legal guardians is a necessary requirement. If the consent of parents or legal guardians is not obtained then it constitutes a violation under the DPDP Act. Under section 2(f) of the DPDP act, a “child” means an individual who has not completed the age of eighteen years. 

Section 9 under the DPDP Act, 2023

With reference to the collection of children's data section 9 of the DPDP Act, 2023 provides that for children below 18 years of age, consent from Parents/Legal Guardians is required. The Data Fiduciary shall, before processing any personal data of a child or a person with a disability who has a lawful guardian, obtain verifiable consent from the parent or the lawful guardian. Section 9 aims to create a safer online environment for children by limiting the exploitation of their data for commercial purposes or otherwise. By virtue of this section, the parents and guardians will have more control over their children's data and privacy and they are empowered to make choices as to how they manage their children's online activities and the permissions they grant to various online services. 

Section 9 sub-section (3) specifies that a Data Fiduciary shall not undertake tracking or behavioural monitoring of children or targeted advertising directed at children. However, section 9 sub-section (5) further provides room for exemption from this prohibition by empowering the Central Government which may notify exemption to specific data fiduciaries or data processors from the behavioural tracking or target advertising prohibition under the future DPDP Rules which are yet to be announced or released. 

Impact on social media platforms

Social media companies are raising concerns about Section 9 of the DPDP Act and upcoming Rules for the DPDP Act. Section 9 prohibits behavioural tracking or targeted advertising directed at children on digital platforms. By prohibiting intermediaries from tracking a ‘child's internet activities’ and ‘targeted advertising’ - this law aims to preserve children's privacy. However, social media corporations contended that this limitation adversely affects the efficacy of safety measures intended to safeguard young users, highlighting the necessity of monitoring specific user signals, including from minors, to guarantee the efficacy of safety measures designed for them.

Social media companies assert that tracking teenagers' behaviour is essential for safeguarding them from predators and harmful interactions. They believe that a complete ban on behavioural tracking is counterproductive to the government's objectives of protecting children. The scope to grant exemption leaves the door open for further advocacy on this issue. Hence it necessitates coordination with the concerned ministry and relevant stakeholders to find a balanced approach that maintains both privacy and safety for young users.

Furthermore, the impact on social media platforms also extends to the user experience and the operational costs required to implement the functioning of the changes created by regulations. This also involves significant changes to their algorithms and data-handling processes. Implementing robust age verification systems to identify young users and protect their data will also be a technically challenging step for the various scales of platforms. Ensuring that children’s data is not used for targeted advertising or behavioural monitoring also requires sophisticated data management systems. The blanket ban on targeted advertising and behavioural tracking may also affect the personalisation of content for young users, which may reduce their engagement with the platform.

For globally operating platforms, aligning their practices with the DPDP Act in India while also complying with data protection laws in other countries (such as GDPR in Europe or COPPA in the US) can be complex and resource-intensive. Platforms might choose to implement uniform global policies for simplicity, which could impact their operations in regions not governed by similar laws. On the same page, competitive dynamics such as market shifts where smaller or niche platforms that cater specifically to children and comply with these regulations may gain a competitive edge. There may be a drive towards developing new, compliant ways of monetizing user interactions that do not rely on behavioural tracking.

CyberPeace Policy Recommendations

A balanced strategy should be taken into account which gives weightage to the contentions of social media companies as well as to the protection of children's personal information. Instead of a blanket ban, platforms can be obliged to follow and encourage openness in advertising practices, ensuring that children are not exposed to any misleading or manipulative marketing techniques. Self-regulation techniques can be implemented to support ethical behaviour, responsibility, and the safety of young users’ online personal information through the platform’s practices. Additionally, verifiable consent should be examined and put forward in a manner which is practical and the platforms have a say in designing the said verification. Ultimately, this should be dealt with in a manner that behavioural tracking and targeted advertising are not affecting the children's well-being, safety and data protection in any way. 

Final Words

Under section 9 of the DPDP Act, the prohibition of behavioural tracking and targeted advertising in case of processing children's personal data - will compel social media platforms to overhaul their data collection and advertising practices, ensuring compliance with stricter privacy regulations. The legislative intent behind this provision is to enhance and strengthen the protection of children's digital personal data security and privacy. As children are particularly vulnerable to digital threats due to their still-evolving maturity and cognitive capacities, the protection of their privacy stands as a priority. The innocence of children is a major cause for concern when it comes to digital access because children simply do not possess the discernment and caution required to be able to navigate the Internet safely. Furthermore, a balanced approach needs to be adopted which maintains both ‘privacy’ and ‘safety’ for young users.

References

• https://www.meity.gov.in/writereaddata/files/Digital%20Personal%20Data%20Protection%20Act%202023.pdf 
• https://www.firstpost.com/tech/as-govt-of-india-starts-preparing-rules-for-dpdp-act-social-media-platforms-worried-13789134.html#google_vignette
• https://www.business-standard.com/industry/news/social-media-platforms-worry-new-data-law-could-affect-child-safety-ads-124070400673_1.html

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Introduction

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In the hyperconnected world, cyber incidents can no longer be treated as sporadic disruptions; such incidents have become an everyday occurrence. The attack landscape today is very consequential and shows significant multiplication in its frequency, with ransomware attacks incapacitating a health system, phishing attacks hitting a financial institution, or state-sponsored attacks on critical infrastructures. Towards counteracting such threats, traditional ways alone are not enough, they gravely rely on manual research and human intellect. Attackers exercise speed, scale, and stealth, and defenders are always four steps behind. With such a widening gap, it is deemed necessary to facilitate incident response and crisis management with the intervention of automation and artificial intelligence (AI) for faster detection, context-driven decision-making, and collaborative response beyond human capabilities.

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Incident Response and Crisis Management

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Incident response is the structured way in which organisations deal with responding to detecting, segregating, and recovering from security incidents. Crisis management takes this even further, dealing not only with the technical fallout of a breach but also its business, reputation, and regulatory implications. Echelon used to depend on manual teams of people sorting through logs, cross-correlating alarms, and generating responses, a paradigm effective for small numbers but quickly inadequate in today's threat climate. Today's opponents attack at machine speed, employing automation to launch attacks. Under such circumstances, responding with slow, manual methods means delay and draconian consequences. The AI and automation introduction is a paradigm change that allows organisations to equate the pace and precision with which attackers initiate attacks in responding to incidents.

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How Automation Reinvents Response

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Cybercrime automation liberates cybercrime analysts from boring and repetitive tasks that consume time. An analyst manually detects potential threats from a list of hundreds each day, while automated systems sift through noise and focus only on genuine threats. Malware can automatically cause infected computers to be disconnected from the network to avoid spreading or may automatically have its suspicious account permissions removed without human intervention. The security orchestration systems move further by introducing playbooks, predefined steps describing how incidents of a certain type (e.g., phishing attempts or malware infections) should be handled. This ensures fast containment while ensuring consistency and minimising human error amid the urgency of dealing with thousands of alerts.

Automation takes care of threat detection, prioritisation, and containment, allowing human analysts to refocus on more complex decision-making. Instead of drowning in the sea of trivial alerts, security teams can now devote their efforts to more strategic areas: threat hunting and longer-term resilience. Automation is a strong tool of defence, cutting response times down from hours to minutes.

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The Intelligence Layer: AI in Action

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If automation provides speed, then AI is what allows the brain to be intelligent and flexible. Working with old and fixed-rule systems, AI-enabled solutions learn from experiences, adapt to changes in threats, and discover hidden patterns of which human analysts themselves would be unaware. For instance, machine learning algorithms identify normal behaviour on a corporate network and raise alerts on any anomalies that could indicate an insider attack or an advanced persistent threat. Similarly, AI systems sift through global threat intelligence to predict likely attack vectors so organisations can have their vulnerabilities fixed before they are exploited.

AI also boosts forensic analysis. Instead of searching forever for clues, analysts let AI-driven systems trace back to the origin of an event, identify vulnerabilities exploited by attackers, and flag systems that are still under attack. During a crisis, AI is a decision support that predicts outcomes of different scenarios and recommends the best response. In response to a ransomware attack, for example, based on context, AI might advise separating a single network segment or restoring from backup or alerting law enforcement. 

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Real-World Applications and Case Studies

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Already, this mitigation has been provided in the form of real-world applications of automation and AI. Consider, for example, IBM Watson for Cybersecurity, which has been applied in analysing unstructured threat intelligence and providing analysts with actionable results in minutes, rather than days. Like this, systems driven by AI in DARPA's Cyber Grand Challenge demonstrated the ability to automatically identify an instant vulnerability, patch it, and reveal the potential of a self-healing system. AI-powered fraud detection systems stop suspicious transactions in the middle of their execution and work all night to prevent losses. What is common in all these examples is that automation and AI lessen human effort, increase accuracy, and in the event of a cyberattack, buy precious time.

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Challenges and Limitations

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While promising, the technology is still not fully mature. The quality of an AI system is highly dependent on the training data provided; poor training can generate false positives that drown teams or worse false negatives that allow attackers to proceed unabated. Attackers have also started targeting AI itself by poisoning datasets or designing malware that does not get detected. Aside from risks that are more technical, the operational and financial costs involved in implementing advanced AI-based systems present expensive threats to any company. Organisations will have to make expenditures not only on technology but also for the training of staff to best utilise these tools. There are some ethical and privacy issues to consider as well because systems may be processing sensitive personal data, so global data protection laws such as the GDPR or India's DPDP Act could come into conflict.

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Creating a Human-AI Collaboration

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The future is not going to be one of substitution by machines but of creating human-AI synergy. Automation can do the drudgery, AI can provide smarts, and human professionals can use judgment, imagination, and ethical decisions. One would want to build AI-fuelled Security Operations Centres where technology and human experts work in tandem. Continuous training must be provided to AI models to reduce false alarms and make them most resistant against adversarial attacks. Regular conduct of crisis drills that combine AI tools and human teams can ensure preparedness for real-time events. Likewise, it is worth integrating ethical AI guidelines into security frameworks to ensure a stronger defence while respecting privacy and regulatory compliance.

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Conclusion

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Cyber-attacks are an eventuality in this modern time, but the actual impact need not be so harsh. The organisations can maintain the programmatic method of integrating automation and AI into incident response and crisis management so that the response against the very threat can be shifted from reactive firefighting to proactive resilience. Automation gives speed and efficiency while AI gives intelligence and foresight, hence putting the defenders on par and possibly exceeding the speed and sophistication of the attackers. But an utmost system without human inquisitiveness, ethical reasoning, and strategic foresight would remain imperfect. The best defence is in that human-machine relationship symbiotic system wherein automation and AI take care of how fast and how many cyber threats come in, whereas human intellect ensures that every response is aligned with larger organizational goals. This synergy is where cybersecurity resiliency will reside in the future-the defenders won't just be reacting to emergencies but will rather be driving the way.

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References

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• https://www.sisainfosec.com/blogs/incident-response-automation/
• https://stratpilot.ai/role-of-ai-in-crisis-management-and-its-critical-importance/
• https://www.juvare.com/integrating-artificial-intelligence-into-crisis-management/
• https://www.motadata.com/blog/role-of-automation-in-incident-management/

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Executive Summary

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A video is being shared on social media showing a little girl crying while holding onto a pole amid muddy water covering a road. The video is being shared with the claim that it shows the situation during the recent floods in Nepal. A research by CyberPeace’s Research Wing found the viral claim to be false. Our research revealed that the viral video is AI-generated and is being shared with a misleading claim.

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Claim:

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A user on the social media platform Instagram shared the viral video with the caption: “The situation in Nepal looks extremely frightening after the floods. Floodwater has covered the area, and the frightened face of a little girl amid the water is moving everyone emotionally. Seeing the innocent girl’s condition in this difficult situation has raised concerns among people.”

https://www.instagram.com/reels/DctRY9OF0Th/ 

https://archive.ph/tQRdb 

Fact Check

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A reverse image search of keyframes from the viral video on Google did not yield any credible media reports related to the video. Upon closely examining the viral video, we suspected that it might be AI-generated. We subsequently scanned the video using the AI detection tool Hive Moderation. According to the results, the video has a 99 percent probability of being AI-generated.

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As part of the next step of ourresearch, we scanned the viral video using another AI detection tool, Sightengine. According to the results, the video has a 99 percent probability of being AI-generated.

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Conclusion

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Our research found that the viral video is AI-generated and is being shared with a misleading claim.

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