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Executive Summary:

Given that AI technologies are evolving at a fast pace in 2024, an AI-oriented phishing attack on a large Indian financial institution illustrated the threats. The documentation of the attack specifics involves the identification of attack techniques, ramifications to the institution, intervention conducted, and resultant effects. The case study also turns to the challenges connected with the development of better protection and sensibilisation of automatized threats. 

Introduction

Due to the advancement in AI technology, its uses in cybercrimes across the world have emerged significant in financial institutions. In this report a serious incident that happened in early 2024 is analysed, according to which a leading Indian bank was hit by a highly complex, highly intelligent AI-supported phishing operation. Attack made use of AI’s innate characteristic of data analysis and data persuasion which led into a severe compromise of the bank’s internal structures.

Background

The chosen financial institution, one of the largest banks in India, had a good background regarding the extremity of its cybersecurity policies. However, these global cyberattacks opened up new threats that AI-based methods posed that earlier forms of security could not entirely counter efficiently. The attackers concentrated on the top managers of the bank because it is evident that controlling such persons gives the option of entering the inner systems as well as financial information. 

Attack Execution

The attackers utilised AI in sending the messages that were an exact look alike of internal messages sent between employees. From Facebook and Twitter content, blog entries, and lastly, LinkedIn connection history and email tenor of the bank’s executives, the AI used to create these emails was highly specific. Some of these emails possessed official formatting, specific internal language, and the CEO’s writing; this made them very realistic. 

 It also used that link in phishing emails that led the users to a pseudo internal portal in an attempt to obtain the login credentials. Due to sophistication, the targeted individuals thought the received emails were genuine, and entered their log in details easily to the bank’s network, thus allowing the attackers access. 

Impact

It caused quite an impact to the bank in every aspect. Numerous executives of the company lost their passwords to the fake emails and compromised several financial databases with information from customer accounts and transactions. The break-in permitted the criminals to cease a number of the financial’s internet services hence disrupting its functions and those of its customers for a number of days. 

 They also suffered a devastating blow to their customer trust because the breach revealed the bank’s weakness against contemporary cyber threats. Apart from managing the immediate operations which dealt with mitigating the breach, the financial institution was also toppling a long-term reputational hit. 

Technical Analysis and Findings

1. The AI techniques that are used in generation of the phishing emails are as follows: 

•  The attack used powerful NLP technology, which was most probably developed using the large-scaled transformer, such as GPT (Generative Pre-trained Transformer). Since these models are learned from large data samples they used the examples of the conversation pieces from social networks, emails and PC language to create quite credible emails. 

 Key Technical Features: 

•  Contextual Understanding: The AI was able to take into account the nature of prior interactions and thus write follow up emails that were perfectly in line with prior discourse. 
•  Style Mimicry: The AI replicated the writing of the CEO given the emails of the CEO and then extrapolated from the data given such elements as the tone, the language, and the format of the signature line. 
•  Adaptive Learning: The AI actively adapted from the mistakes, and feedback to tweak the generated emails for other tries and this made it difficult to detect. 

2. Sophisticated Spear-Phishing Techniques

Unlike ordinary phishing scams, this attack was phishing using spear-phishing where the attackers would directly target specific people using emails. The AI used social engineering techniques that significantly increased the chances of certain individuals replying to certain emails based on algorithms which machine learning furnished. 

 Key Technical Features: 

•  Targeted Data Harvesting: Cyborgs found out the employees of the organisation and targeted messages via the public profiles and messengers were scraped. 
•  Behavioural Analysis: The latest behaviour pattern concerning the users of the social networking sites and other online platforms were used by the AI to forecast the courses of action expected to be taken by the end users such as clicking on the links or opening of the attachments. 
•  Real-Time Adjustments: These are times when it was determined that the response to the phishing email was necessary and the use of AI adjusted the consequent emails’ timing and content. 

3. Advanced Evasion Techniques

The attackers were able to pull off this attack by leveraging AI in their evasion from the normal filters placed in emails. These techniques therefore entailed a modification of the contents of the emails in a manner that would not be easily detected by the spam filters while at the same time preserving the content of the message. 

Key Technical Features: 

•  Dynamic Content Alteration: The AI merely changed the different aspects of the email message slightly to develop several versions of the phishing email that would compromise different algorithms. 
•  Polymorphic Attacks: In this case, polymorphic code was used in the phishing attack which implies that the actual payloads of the links changed frequently, which means that it was difficult for the AV tools to block them as they were perceived as threats. 
•  Phantom Domains: Another tactic employed was that of using AI in generating and disseminating phantom domains, that are actual web sites that appear to be legitimate but are in fact short lived specially created for this phishing attack, adding to the difficulty of detection. 

4. Exploitation of Human Vulnerabilities

This kind of attack’s success was not only in AI but also in the vulnerability of people, trust in familiar language and the tendency to obey authorities. 

Key Technical Features: 

•  Social Engineering: As for the second factor, AI determined specific psychological principles that should be used in order to maximise the chance of the targeted recipients opening the phishing emails, namely the principles of urgency and familiarity. 
•  Multi-Layered Deception: The AI was successfully able to have a two tiered approach of the emails being sent as once the targeted individuals opened the first mail, later the second one by pretext of being a follow up by a genuine company/personality. 

Response

On sighting the breach, the bank’s cybersecurity personnel spring into action to try and limit the fallout. They reported the matter to the Indian Computer Emergency Response Team (CERT-In) to find who originated the attack and how to block any other intrusion. The bank also immediately started taking measures to strengthen its security a bit further, for instance, in filtering emails, and increasing the authentication procedures. 

 Knowing the risks, the bank realised that actions should be taken in order to enhance the cybersecurity level and implement a new wide-scale cybersecurity awareness program. This programme consisted of increasing the awareness of employees about possible AI-phishing in the organisation’s info space and the necessity of checking the sender’s identity beforehand.

Outcome

Despite the fact and evidence that this bank was able to regain its functionality after the attack without critical impacts with regards to its operations, the following issues were raised. Some of the losses that the financial institution reported include losses in form of compensation of the affected customers and costs of implementing measures to enhance the financial institution’s cybersecurity. However, the principle of the incident was significantly critical of the bank as customers and shareholders began to doubt the organisation’s capacity to safeguard information in the modern digital era of advanced artificial intelligence cyber threats. 

 This case depicts the importance for the financial firms to align their security plan in a way that fights the new security threats. The attack is also a message to other organisations in that they are not immune from such analysis attacks with AI and should take proper measures against such threats.

Conclusion

The recent AI-phishing attack on an Indian bank in 2024 is one of the indicators of potential modern attackers’ capabilities. Since the AI technology is still progressing, so are the advances of the cyberattacks. Financial institutions and several other organisations can only go as far as adopting adequate AI-aware cybersecurity solutions for their systems and data. 

 Moreover, this case raises awareness of how important it is to train the employees to be properly prepared to avoid the successful cyberattacks. The organisation’s cybersecurity awareness and secure employee behaviours, as well as practices that enable them to understand and report any likely artificial intelligence offences, helps the organisation to minimise risks from any AI attack.

Recommendations

1. Enhanced AI-Based Defences: Financial institutions should employ AI-driven detection and response products that are capable of mitigating AI-operation-based cyber threats in real-time. 
2.  Employee Training Programs: CYBER SECURITY: All employees should undergo frequent cybersecurity awareness training; here they should be trained on how to identify AI-populated phishing. 
3.  Stricter Authentication Protocols: For more specific accounts, ID and other security procedures should be tight in order to get into sensitive ones. 
4.  Collaboration with CERT-In: Continued engagement and coordination with authorities such as the Indian Computer Emergency Response Team (CERT-In) and other equivalents to constantly monitor new threats and valid recommendations. 
5.  Public Communication Strategies: It is also important to establish effective communication plans to address the customers of the organisations and ensure that they remain trusted even when an organisation is facing a cyber threat. 

Through implementing these, financial institutions have an opportunity for being ready with new threats that come with AI and cyber terrorism on essential financial assets in today’s complex IT environments.

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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 Senate bill introduced on 19 March 2024 in the United States would require online platforms to obtain consumer consent before using their data for Artificial Intelligence (AI) model training. If a company fails to obtain this consent, it would be considered a deceptive or unfair practice and result in enforcement action from the Federal Trade Commission (FTC) under the AI consumer opt-in, notification standards, and ethical norms for training (AI Consent) bill. The legislation aims to strengthen consumer protection and give Americans the power to determine how their data is used by online platforms. 

The proposed bill also seeks to create standards for disclosures, including requiring platforms to provide instructions to consumers on how they can affirm or rescind their consent. The option to grant or revoke consent should be made available at any time through an accessible and easily navigable mechanism, and the selection to withhold or reverse consent must be at least as prominent as the option to accept while taking the same number of steps or fewer as the option to accept.

The AI Consent bill directs the FTC to implement regulations to improve transparency by requiring companies to disclose when the data of individuals will be used to train AI and receive consumer opt-in to this use. 
The bill also commissions an FTC report on the technical feasibility of de-identifying data, given the rapid advancements in AI technologies, evaluating potential measures companies could take to effectively de-identify user data.

The definition of ‘Artificial Intelligence System’ under the proposed bill

ARTIFICIALINTELLIGENCE SYSTEM- The term artificial intelligence system“ means a machine-based system that—

1. Is capable of influencing the environment by producing an output, including predictions, recommendations or decisions, for a given set of objectives; and
2. 2. Uses machine or human-based data and inputs to

(i) Perceive real or virtual environments;

(ii) Abstract these perceptions into models through analysis in an automated manner (such as by using machine learning) or manually; and

(iii) Use model inference to formulate options for outcomes.

Importance of the proposed AI Consent Bill USA

1. Consumer Data Protection: The AI Consent bill primarily upholds the privacy rights of an individual. Consent is necessitated from the consumer before data is used for AI Training; the bill aims to empower individuals with unhinged autonomy over the use of personal information. The scope of the bill aligns with the greater objective of data protection laws globally, stressing the criticality of privacy rights and autonomy.

2. Prohibition Measures: The proposed bill intends to prohibit covered entities from exploiting the data of consumers for training purposes without their consent. This prohibition extends to the sale of data, transfer to third parties and usage. Such measures aim to prevent data misuse and exploitation of personal information. The bill aims to ensure companies are leveraged by consumer information for the development of AI without a transparent process of consent.

3. Transparent Consent Procedures: The bill calls for clear and conspicuous disclosures to be provided by the companies for the intended use of consumer data for AI training. The entities must provide a comprehensive explanation of data processing and its implications for consumers. The transparency fostered by the proposed bill allows consumers to make sound decisions about their data and its management, hence nurturing a sense of accountability and trust in data-driven practices.

4. Regulatory Compliance: The bill's guidelines call for strict requirements for procuring the consent of an individual. The entities must follow a prescribed mechanism for content solicitation, making the process streamlined and accessible for consumers. Moreover, the acquisition of content must be independent, i.e. without terms of service and other contractual obligations. These provisions underscore the importance of active and informed consent in data processing activities, reinforcing the principles of data protection and privacy.

5. Enforcement and Oversight: To enforce compliance with the provisions of the bill, robust mechanisms for oversight and enforcement are established. Violations of the prescribed regulations are treated as unfair or deceptive acts under its provisions. Empowering regulatory bodies like the FTC to ensure adherence to data privacy standards. By holding covered entities accountable for compliance, the bill fosters a culture of accountability and responsibility in data handling practices, thereby enhancing consumer trust and confidence in the digital ecosystem.

Importance of Data Anonymization

Data Anonymization is the process of concealing or removing personal or private information from the data set to safeguard the privacy of the individual associated with it. Anonymised data is a sort of information sanitisation in which data anonymisation techniques encrypt or delete personally identifying information from datasets to protect data privacy of the subject. This reduces the danger of unintentional exposure during information transfer across borders and allows for easier assessment and analytics after anonymisation. When personal information is compromised, the organisation suffers not just a security breach but also a breach of confidence from the client or consumer. Such assaults can result in a wide range of privacy infractions, including breach of contract, discrimination, and identity theft.

The AI consent bill asks the FTC to study data de-identification methods. Data anonymisation is critical to improving privacy protection since it reduces the danger of re-identification and unauthorised access to personal information. Regulatory bodies can increase privacy safeguards and reduce privacy risks connected with data processing operations by investigating and perhaps implementing anonymisation procedures.

The AI consent bill emphasises de-identification methods, as well as the DPDP Act 2023 in India, while not specifically talking about data de-identification, but it emphasises the data minimisation principles, which highlights the potential future focus on data anonymisation processes or techniques in India.

Conclusion

The proposed AI Consent bill in the US represents a significant step towards enhancing consumer privacy rights and data protection in the context of AI development. Through its stringent prohibitions, transparent consent procedures, regulatory compliance measures, and robust enforcement mechanisms, the bill strives to strike a balance between fostering innovation in AI technologies while safeguarding the privacy and autonomy of individuals.

References:

• https://fedscoop.com/consumer-data-consent-training-ai-models-senate-bill/#:~:text=%E2%80%9CThe%20AI%20CONSENT%20Act%20gives,Welch%20said%20in%20a%20statement
• https://www.dataguidance.com/news/usa-bill-ai-consent-act-introduced-house#:~:text=USA%3A%20Bill%20for%20the%20AI%20Consent%20Act%20introduced%20to%20House%20of%20Representatives,-ConsentPrivacy%20Law&text=On%20March%2019%2C%202024%2C%20US,the%20U.S.%20House%20of%20Representatives
• https://datenrecht.ch/en/usa-ai-consent-act-vorgeschlagen/
• https://www.lujan.senate.gov/newsroom/press-releases/lujan-welch-introduce-billto-require-online-platforms-receive-consumers-consent-before-using-their-personal-data-to-train-ai-models/

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