#FactCheck-Amarnath Yatra Video Falsely Shared as Chinese Incursion in Arunachal Pradesh

What are Decentralised Autonomous Organizations (DAOs)?

A Decentralised Autonomous Organisation or a DAO, is a unique take on democracy on the blockchain. It is a set of rules encoded into a self-executing contract (also known as a smart contract) that operates autonomously on a blockchain system. A DAO imitates a traditional company, although, in its more literal sense, it is a contractually created entity. In theory, DAOs have no centralised authority in making decisions for the system; it is a communally run system whereby all decisions (be it for internal governance or for the development of the blockchain system) are voted upon by the community members. DAOs are primarily characterised by a decentralised form of operation, where there is no one entity, group or individual running the system. They are self-sustaining entities, having their own currency, economy and even governance, that do not depend on a group of individuals to operate. Blockchain systems, especially DAOs are characterised by pure autonomy created to evade external coercion or manipulation from sovereign powers. DAOs follow a mutually created, agreed set of rules created by the community, that dictates all actions, activities, and participation in the system’s governance. There may also be provisions that regulate the decision-making power of the community.

Ethereum’s DAO’s White Paper described DAO as “The first implementation of a [DAO Entity] code to automate organisational governance and decision making.” Can be used by individuals working together collaboratively outside of a traditional corporate form.  It can also be used by a registered corporate entity to automate formal governance rules contained in corporate bylaws or imposed by law.”  The referred white paper proposes an entity that would use smart contracts to solve governance issues inherent in traditional corporations.  DAOs attempt to redesign corporate governance with blockchain such that contractual terms are “formalised, automated and enforced using software.”

Cybersecurity threats under DAOs

While DAOs offer increased transparency and efficiency, they are not immune to cybersecurity threats. Cybersecurity risks in DAO, primarily in governance, stem from vulnerabilities in the underlying blockchain technology and the DAO's smart contracts. Smart contract exploits, code vulnerabilities, and weaknesses in the underlying blockchain protocol can be exploited by malicious actors, leading to unauthorised access, fund manipulations, or disruptions in the governance process. Additionally, DAOs may face challenges related to phishing attacks, where individuals are tricked into revealing sensitive information, such as private keys, compromising the integrity of the governance structure. As DAOs continue to evolve, addressing and mitigating cybersecurity threats is crucial to ensuring the trust and reliability of decentralised governance mechanisms.

Centralisation/Concentration of Power

DAOs today actively try to leverage on-chain governance, where any governance votes or transactions are directly taken on the blockchain. But such governance is often plutocratic in nature, where the wealthy hold influences, rather than democracies, since those who possess the requisite number of tokens are only allowed to vote and each token staked implies that many numbers of votes emerge from the same individual. This concentration of power in the hands of “whales” often creates disadvantages for the newer entrants into the system who may have an in-depth background but lack the funds to cast a vote. Voting, presently in the blockchain sphere, lacks the requisite concept of “one man, one vote” which is critical in democratic societies.

Smart contract vulnerabilities and external threats

Smart contracts, self-executing pieces of code on a blockchain, are integral to decentralised applications and platforms. Despite their potential, smart contracts are susceptible to various vulnerabilities such as coding errors, where mistakes in the code can lead to funds being locked or released erroneously. Some of them have been mentioned as follows;

Smart Contracts are most prone to re-entrance attacks whereby an untrusted external code is allowed to be executed in a smart contract. This scenario occurs when a smart contract invokes an external contract, and the external contract subsequently re-invokes the initial contract. This sequence of events can lead to an infinite loop, and a reentrancy attack is a tactic exploiting this vulnerability in a smart contract. It enables an attacker to repeatedly invoke a function within the contract, potentially creating an endless loop and gaining unauthorised access to funds.

Additionally, smart contracts are also prone to oracle problems. Oracles refer to third-party services or mechanisms that provide smart contracts with real-world data. Since smart contracts on blockchain networks operate in a decentralised, isolated environment, they do not have direct access to external information, such as market prices, weather conditions, or sports scores. Oracles bridge this gap by acting as intermediaries, fetching and delivering off-chain data to smart contracts, enabling them to execute based on real-world conditions. The oracle problem within blockchain pertains to the difficulty of securely incorporating external data into smart contracts. The reliability of external data poses a potential vulnerability, as oracles may be manipulated or provide inaccurate information. This challenge jeopardises the credibility of blockchain applications that rely on precise and timely external data.

Sybil Attack: A Sybil attack involves a single node managing multiple active fake identities, known as Sybil identities, concurrently within a peer-to-peer network. The objective of such an attack is to weaken the authority or influence within a trustworthy system by acquiring the majority of control in the network. The fake identities are utilised to establish and exert this influence. A successful Sybil attack allows threat actors to perform unauthorised actions in the system.

Distributed Denial of Service Attacks: A Distributed Denial of Service (DDoS) attack is a malicious attempt to disrupt the regular functioning of a network, service, or website by overwhelming it with a flood of traffic. In a typical DDoS attack, multiple compromised computers or devices, often part of a botnet (a network of infected machines controlled by a single entity), are used to generate a massive volume of requests or data traffic. The targeted system becomes unable to respond to legitimate user requests due to the excessive traffic, leading to a denial of service.

Conclusion

Decentralised Autonomous Organisations (DAOs) represent a pioneering approach to governance on the blockchain, relying on smart contracts and community-driven decision-making. Despite their potential for increased transparency and efficiency, DAOs are not immune to cybersecurity threats. Vulnerabilities in smart contracts, such as reentrancy attacks and oracle problems, pose significant risks, and the concentration of voting power among wealthy token holders raises concerns about democratic principles. As DAOs continue to evolve, addressing these challenges is essential to ensuring the resilience and trustworthiness of decentralised governance mechanisms. Efforts to enhance security measures, promote inclusivity, and refine governance models will be crucial in establishing DAOs as robust and reliable entities in the broader landscape of blockchain technology.

References:

‍https://www.imperva.com/learn/application-security/sybil-attack/

‍https://www.tripwire.com/state-of-security/blockchain-security-understanding-vulnerabilities-and-mitigating-risks

‍https://medium.com/@RedStone_Finance/the-oracle-problem-and-its-implications-for-blockchains-c0c0ad9cf5a7

‍https://www.linkedin.com/posts/satish-kulkarni-bb96193_what-are-cybersecurity-risk-to-dao-and-how-activity-7048286955645677568-B3pV/ https://www.geeksforgeeks.org/what-is-ddosdistributed-denial-of-service/ Report of Investigation Pursuant to Section 21 (a) of the Securities Exchange Act of 1934: The DAO, Securities and Exchange Board, Release No. 81207/ July 25, 2017

‍https://www.sec.gov/litigation/investreport/34-81207.pdf https://www.legalserviceindia.com/legal/article-10921-blockchain-based-decentralized-autonomous-organizations-daos-.html

Introduction

The United Nations General Assembly (UNGA) has unanimously adopted the first global resolution on Artificial Intelligence (AI), encouraging countries to take into consideration human rights, keeping personal data safe, and further monitoring the threats associated with AI. This non-binding resolution proposed by the United States and co-sponsored by China and over 120 other nations advocates the strengthening of privacy policies. This step is crucial for governments across the world to shape how AI grows because of the dangers it carries that could undermine the protection, promotion, and right to human dignity and fundamental freedoms. The resolution emphasizes the importance of respecting human rights and fundamental freedoms throughout the life cycle of AI systems, highlighting the benefits of digital transformation and safe AI systems.

Key highlights

● This is indeed a landmark move by the UNGA, which adopted the first global resolution on AI. This resolution encourages member countries to safeguard human rights, protect personal data, and monitor AI for risks.

● Global leaders have shown their consensus for safe, secure, trustworthy AI systems that advance sustainable development and respect fundamental freedom.

● Resolution is the latest in a series of initiatives by governments around the world to shape AI. Therefore, AI will have to be created and deployed through the lens of humanity and dignity, Safety and Security, human rights and fundamental freedoms throughout the life cycle of AI systems.

● UN resolution encourages global cooperation, warns against improper AI use, and emphasizes the issues of human rights.

● The resolution aims to protect from potential harm and ensure that everyone can enjoy its benefits. The United States has worked with over 120 countries at the United Nations, including Russia, China, and Cuba, to negotiate the text of the resolution adopted.

Brief Analysis

AI has become increasingly prevalent in recent years, with chatbots such as the  Chat GPT taking the world by storm. AI has been steadily attempting to replicate human-like thinking and solve problems. Furthermore, machine learning, a key aspect of AI, involves learning from experience and identifying patterns to solve problems autonomously. The contemporary emergence of AI has, however, raised questions about its ethical implications, potential negative impact on society, and whether it is too late to control it.

While AI is capable of solving problems quickly and performing various tasks with ease, it also has its own set of problems. As AI continues to grow, global leaders have called for regulations to prevent significant harm due to the unregulated AI landscape to the world and encourage the use of trustworthy AI. The European Union (EU) has come up with an AI act called the “European AI Act”.  Recently, a Senate bill called “The AI Consent Bill” was introduced in the US. Similarly, India is also proactively working towards setting the stage for a more regulated Al landscape by fostering dialogues and taking significant measures. Recently, the Ministry of Electronics and Information Technology (MeitY) issued an advisory on AI, which requires explicit permission to deploy under-testing or unreliable AI models related to India's Internet. The following advisory also indicates measures advocating to combat deepfakes or misinformation.

AI has thus become a powerful tool that has raised concerns about its ethical implications and the potential negative influence on society. Governments worldwide are taking action to regulate AI and ensure that it remains safe and effective. Now, the groundbreaking move of the UNGA, which adopted the global resolution on AI, with the support of all 193 U.N. member nations, shows the true potential of efforts by countries to regulate AI and promote safe and responsible use globally.

New AI tools have emerged in the public sphere, which may threaten humanity in an unexpected direction. AI is able to learn by itself through machine learning to improve itself, and developers often are surprised by the emergent abilities and qualities of these tools. The ability to manipulate and generate language, whether with words, images, or sounds, is the most important aspect of the current phase of the ongoing AI Revolution. In the future, AI can have several implications. Hence, it is high time to regulate AI and promote the safe, secure and responsible use of it.

Conclusion

The UNGA has approved its global resolution on AI, marking significant progress towards creating global standards for the responsible development and employment of AI. The resolution underscores the critical need to protect human rights, safeguard personal data, and closely monitor AI technologies for potential hazards. It calls for more robust privacy regulations and recognises the dangers associated with improper AI systems. This profound resolution reflects a unified stance among UN member countries on overseeing AI to prevent possible negative effects and promote safe, secure and trustworthy AI.

References

● https://www.reuters.com/technology/cybersecurity/un-adopts-first-global-artificial-intelligence-resolution-2024-03-21/

● https://www.thehindu.com/sci-tech/technology/the-un-adopts-a-resolution-backing-efforts-to-ensure-artificial-intelligence-is-safe/article67979196.ece

‍

Introduction

‍

A digital forensic investigation can start with a question: what really happened? The tricky part is that the answer might be hidden in thousands of files, system logs, browser records, messages, application artefacts and timestamps. Traditional forensic practice gives a method to find, collect, check and report such evidence yet the amount of digital data keeps growing. NIST’s forensic guidance says that evidence must be kept safe its integrity checked and investigative steps written down so that results can be examined and repeated.[4] This is where Large Language Models (LLMs) draw interest. LLMs can. Summarise large amounts of text, spot connections between pieces of information and help an examiner move through evidence faster. However, an important question remains: can an LLM truly become an investigator or should it stay an assistant to one?

Understanding the basic idea: What is an LLM?

A Large Language Model is an AI system trained on collections of text so that it can learn language patterns and produce answers. In terms an LLM does not think like a human investigator. An LLM creates language from patterns it learned during training and from the details it receives. This makes an LLM handy for summarisation, classification, question answering and pulling information from text.

‍

Where LLMs can actually help a forensic examiner

‍

‍

‍

Finding relevant evidence faster

‍

Consider a case involving a suspected phishing incident. A forensic examiner may have email headers, message bodies, attachments, browser history, DNS records and system logs. An LLM can help organize text-based evidence, spot repeated terms pull out indicators such as domains or IP addresses and cluster related events for review. Research on LLM-assisted forensics says that pattern recognition and early evidence analysis are promising use cases.[1][3]

Connecting the timeline

Investigations often rely on order: what happened first what followed and which artefacts back that order. An LLM can turn amounts of timestamped data into a clear timeline or point out records that seem related. The key point is that the LLM helps an examiner see relationships; it does not independently prove that one event caused another.

‍

Making forensic reporting easier to understand

‍

A good forensic report should be understandable to technical and non-technical readers. LLMs can help turn examiner notes or structured findings into clearer draft language, summaries or executive explanations. This is particularly useful when an investigation contains technical terms that need to be explained without losing their meaning. Research also identifies evidence presentation and reporting as a potential area for LLM assistance.[1]

‍

A realistic example

‍

Imagine an organisation reports that an employee account may have been compromised. The examiner collects the relevant disk image, authentication logs, browser history and email data using established procedures. After preservation and examination with validated tools, a controlled set of extracted text or structured artefacts could be given to an LLM. It might identify unusual login patterns, highlight a suspicious domain appearing in multiple sources, and draft questions for further examination.

The examiner then checks those observations against the original evidence and forensic tool outputs. If the model says a login occurred at 10:14, that timestamp must be verified in the source log. If it suggests that two events are linked, the relationship must be supported by evidence. The model can accelerate the search, but the evidence remains the foundation of the conclusion.

‍

Why an LLM cannot simply replace the investigator

‍

The biggest challenge is reliability. LLMs can produce fluent answers that sound convincing even when the answer is wrong. The 2026 systematic review of 33 peer-reviewed works on LLMs in digital forensics highlights hallucination, explainability, reproducibility and legal admissibility as major concerns.[1] In forensic work, an incorrect sentence is not just a minor inconvenience; it can change how a case is understood.

There is also a reproducibility problem. Traditional forensic practice depends on validated processes, integrity checks and documentation. NIST recommends verifying acquired data and recording actions and tools so work can be repeated.[4] LLM output can vary with the model, settings, context and system version. That makes raw model output unsuitable as forensic proof on its own.

Another concern is confidentiality. Evidence may contain personal information, credentials, private communications or sensitive organisational data. Sending it to an external AI service without proper controls can create a privacy and governance risk. NIST’s Generative AI Profile stresses managing risks across the AI lifecycle.[5]

‍

What responsible LLM-assisted forensics could look like

‍

A practical approach is to keep the examiner in control. The LLM should receive only the information needed for the task, preferably through a controlled environment with access restrictions and logging. Critical findings should always link back to the source artefact rather than being accepted because the model sounds confident.

‍

A simple operational principle is: -

‍

‍

In practice, this means preserving and hashing evidence, using validated forensic tools for acquisition and examination, recording what data was supplied to the AI system, retaining relevant prompts and outputs in working notes, and independently verifying material claims. SWGDE guidance likewise emphasises protecting the integrity of evidence and documenting handling throughout the evidence lifecycle.[6]

A further possibility is a local or specialised forensic model. Research on “ForensicLLM” demonstrates this direction, including source attribution and retrieval-augmented approaches.[2] Such designs may provide a more controlled environment, but they still require testing and validation before operational use.

‍

Conclusion

‍

LLMs are unlikely to make the forensic examiner irrelevant. Their more realistic value is in helping the examiner deal with the scale and complexity of modern evidence. They can search, summarise, classify, correlate and help communicate findings, but these capabilities come with limitations that matter deeply in forensic work. A forensic conclusion must remain traceable to evidence, repeatable through a documented process and open to independent verification.[1][4]

The future of AI-assisted digital forensics is therefore less about replacing investigators and more about designing a disciplined partnership between human expertise and machine assistance. The strongest model is one in which AI speeds up the routine parts of an investigation while the examiner remains responsible for validation, interpretation and final conclusions. As research develops, the real question will not simply be whether an LLM is intelligent enough to analyse evidence, but whether the complete system around it is controlled, explainable and trustworthy enough for forensic use.

‍

References / Endnotes

‍

[1] Chernyshev, M., Baig, Z., Syed, N., Doss, R., & Shore, M. (2026). “Large language models in digital forensics: capabilities, challenges and future directions.” Forensic Science International: Digital Investigation, 56, 302043. https://doi.org/10.1016/j.fsidi.2025.302043

[2] “ForensicLLM: A local large language model for digital forensics.” Forensic Science International: Digital Investigation, 52, Supplement, 301872 (2025). https://doi.org/10.1016/j.fsidi.2025.301872

[3] Wickramasekara, A., Breitinger, F., & Scanlon, M. (2025). “Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency.” Forensic Science International: Digital Investigation, 52, 301859. https://doi.org/10.1016/j.fsidi.2024.301859

[4] Kent, K., Chevalier, S., Grance, T., & Dang, H. (2006). Guide to Integrating Forensic Techniques into Incident Response, NIST Special Publication 800-86. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.800-86

[5] Autio, C., Schwartz, R., Dunietz, J., Jain, S., Stanley, M., Tabassi, E., Hall, P., & Roberts, K. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.600-1

[6] Scientific Working Group on Digital Evidence (SWGDE). Best Practices for Digital Evidence Collection, 18-F-002-2.0. https://www.swgde.org/documents/published-complete-listing/18-f-002-2-0/

‍