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The world of Artificial Intelligence is entering a new phase with the rise of Agentic AI, often described as the third wave of AI evolution. Unlike earlier systems that relied on static models (that learn from the information that is fed) and reactive outputs, Agentic AI introduces intelligent agents that can make decisions, take initiative, and act autonomously in real time. These systems are designed to require minimal human oversight while actively collaborating and learning continuously. Such capabilities indicate an incoming shift, especially in the ways in which Indian businesses can function. For better understanding, Agentic AI is capable of streamlining operations, personalising services, and driving innovations at scale.

India and Agentic AI

Building as we go, India is making continuous strides in the AI revolution- deliberating on government frameworks, and simultaneously adapting. At Microsoft's Pinnacle 2025 summit in Hyderabad, India's pivotal role in shaping the future of Agentic AI was brought to the spotlight. With over 17 million developers on GitHub and ambitions to become the world's largest developer community by 2028, India's tech talent is gearing up to lead global AI innovations. Microsoft's Azure AI Foundry, also highlighted the country's growing influence in the AI landscape.

Indian companies are actively integrating Agentic AI into their operations to enhance efficiency and customer experience. Zomato is leveraging AI agents to optimise delivery logistics, ensuring timely and efficient service. Infosys has developed AI-driven copilots to assist developers in code generation, reducing development time, requiring fewer people to work on a particular project, and improving software quality. 

As per a report by Deloitte, the Indian AI market is projected to grow potentially $20 billion by 2028. However, this is accompanied by significant challenges. 92% of Indian executives identify security concerns as the primary obstacle to responsible AI usage. Additionally, regulatory uncertainties and privacy risks associated with sensitive data were also highlighted.

Challenges in Adoption

Despite the enthusiasm, several barriers hinder the widespread adoption of Agentic AI in India:

• Skills Gap: While the AI workforce is expected to grow to 1.25 million by 2027, the current growth rate of 13% is considered to be insufficient with respect to the demands of the market. 
• Data Infrastructure: Effective AI systems require robust, structured, and accessible datasets. Many organisations lack the necessary data maturity, leading to flawed AI outputs and decision-making failures.
• Trust and Governance: Building trust in AI systems is crucial. Concerns over data privacy, ethical usage, and regulatory compliance require robust governance frameworks to ensure the adoption of AI in a responsible manner.
• Looming fear of job loss: As AI continues to take up more sophisticated roles, a general feeling of hesitancy with respect to the loss of employment/human labour might come in the way of adopting such measures.
• Outsourcing: Currently, most companies prefer outsourcing or buying AI solutions rather than building them in-house. This gives rise to the issue of adapting to evolving needs.

The Road Ahead

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To fully realise the potential of Agentic AI, India must address the following challenges :

• Training the Workforce: Initiatives and workshops tailored for employees that provide AI training can prove to be helpful. Some relevant examples are Microsoft’s commitment to provide AI training to 2 million individuals by 2025 and Infosys's in-house AI training programs.
• Data Readiness: Investing in modern data infrastructure and promoting data literacy are essential to improve data quality and accessibility.
• Establishing Governance Frameworks: Developing clear regulatory guidelines and ethical standards will foster trust and facilitate responsible AI adoption. Like the IndiaAI mission, efforts regarding evolving technology and keeping up with it are imperative.

Agentic AI holds unrealised potential to transform India's business landscape when coupled with innovation and a focus on quality that enhances global competitiveness. India is at a position where by proactively addressing the existing challenges, this potential can be realised and set the foundation for a new technological revolution (along with in-house development), solidifying its position as a global AI leader.

References

• https://economictimes.indiatimes.com/tech/artificial-intelligence/india-facing-shortage-of-agentic-ai-professionals-amid-surge-in-demand/articleshow/120651512.cms?from=mdr
• https://economictimes.indiatimes.com/tech/artificial-intelligence/india-a-global-leader-in-agentic-ai-adoption-deloitte-report/articleshow/119906474.cms?from=mdr
• https://inc42.com/features/from-zomato-to-infosys-why-indias-biggest-companies-are-betting-on-agentic-ai/
• https://www.hindustantimes.com/india-news/agentic-ai-next-big-leap-in-workplace-automation-101742548406693.html
• https://www.deloitte.com/in/en/about/press-room/india-rides-the-agentic-ai-wave.html
• https://www.businesstoday.in/tech-today/news/story/ais-next-chapter-starts-in-india-microsoft-champions-agentic-ai-at-pinnacle-2025-474286-2025-05-01
• https://www.hindustantimes.com/opinion/calm-before-ai-storm-a-moment-to-prepare-101746110985736.html‍
• https://www.financialexpress.com/life/technology/why-agentic-ai-is-the-next-big-thing/3828357/
  

Introduction

Privacy has become a concern for netizens and social media companies have access to a user’s data and the ability to use the said data as they see fit. Meta’s business model, where they rely heavily on collecting and processing user data to deliver targeted advertising, has been under scrutiny. The conflict between Meta and the EU traces back to the enactment of GDPR in 2018. Meta is facing numerous fines for not following through with the regulation and mainly failing to obtain explicit consent for data processing under Chapter 2, Article 7 of the GDPR. ePrivacy Regulation, which focuses on digital communication and digital data privacy, is the next step in the EU’s arsenal to protect user privacy and will target the cookie policies and tracking tech crucial to Meta's ad-targeting mechanism. Meta’s core revenue stream is sourced from targeted advertising which requires vast amounts of data for the creation of a personalised experience and is scrutinised by the EU. 

Pay for Privacy Model and its Implications with Critical Analysis

Meta came up with a solution to deal with the privacy issue - ‘Pay or Consent,’ a model that allows users to opt out of data-driven advertising by paying a subscription fee. The platform would offer users a choice between free, ad-supported services and a paid privacy-enhanced experience which aligns with the GDPR and potentially reduces regulatory pressure on Meta. 

Meta presently needs to assess the economic feasibility of this model and come up with answers for how much a user would be willing to pay for the privacy offered and shift Meta’s monetisation from ad-driven profits to subscription revenues. This would have a direct impact on Meta’s advertisers who use Meta as a platform for detailed user data for targeted advertising, and would potentially decrease ad revenue and innovate other monetisation strategies.

For the users, increased privacy and greater control of data aligning with global privacy concerns would be a potential outcome. While users will undoubtedly appreciate the option to avoid tracking, the suggestion does beg the question that the need to pay might become a barrier. This could possibly divide users between cost-conscious and privacy-conscious segments. Setting up a reasonable price point is necessary for widespread adoption of the model.

For the regulators and the industry, a new precedent would be set in the tech industry and could influence other companies’ approaches to data privacy. Regulators might welcome this move and encourage further innovation in privacy-respecting business models. 

The affordability and fairness of the ‘pay or consent’ model could create digital inequality if privacy comes at a digital cost or even more so as a luxury. The subscription model would also need clarifications as to what data would be collected and how it would be used for non-advertising purposes. In terms of market competition, competitors might use and capitalise on Meta’s subscription model by offering free services with privacy guarantees which could further pressure Meta to refine its offerings to stay competitive. According to the EU, the model needs to provide a third way for users who have ads but are a result of non-personalisation advertising.

Meta has further expressed a willingness to explore various models to address regulatory concerns and enhance user privacy. Their recent actions in the form of pilot programs for testing the pay-for-privacy model is one example. Meta is actively engaging with EU regulators to find mutually acceptable solutions and to demonstrate its commitment to compliance while advocating for business models that sustain innovation. Meta executives have emphasised the importance of user choice and transparency in their future business strategies.

Future Impact Outlook

• The Meta-EU tussle over privacy is a manifestation of broader debates about data protection and business models in the digital age. 
• The EU's stance on Meta’s ‘pay or consent’ model and any new regulatory measures will shape the future landscape of digital privacy, leading to other jurisdictions taking cues and potentially leading to global shifts in privacy regulations. 
• Meta may need to iterate on its approach based on consumer preferences and concerns. Competitors and tech giants will closely monitor Meta’s strategies, possibly adopting similar models or innovating new solutions. And the overall approach to privacy could evolve to prioritise user control and transparency.

Conclusion

Consent is the cornerstone in matters of privacy and sidestepping it violates the rights of users. The manner in which tech companies foster a culture of consent is of paramount importance in today's digital landscape. As the exploration by Meta in the ‘pay or consent’ model takes place, it faces both opportunities and challenges in balancing user privacy with business sustainability. This situation serves as a critical test case for the tech industry, highlighting the need for innovative solutions that respect privacy while fostering growth with the specificity of dealing with data protection laws worldwide, starting with India’s Digital Personal Data Protection Act, of 2023.

Reference:

• https://ciso.economictimes.indiatimes.com/news/grc/eu-tells-meta-to-address-consumer-fears-over-pay-for-privacy/111946106 
• https://www.wired.com/story/metas-pay-for-privacy-model-is-illegal-says-eu/ 
• https://edri.org/our-work/privacy-is-not-for-sale-meta-must-stop-charging-for-peoples-right-to-privacy/ 
• https://fortune.com/2024/04/17/meta-pay-for-privacy-rejected-edpb-eu-gdpr-schrems/ 

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In Delhi there is a bank branch where a lot of money was stolen from people over the country. This bank branch is where all the money disappeared. The people who did this did not wear masks. Break in at midnight. They just used a passbook a rubber stamp and a form that nobody checked carefully. This is the truth that the people who investigate cybercrime keep finding. The way that cybercriminals get away with the money is not by using a computer it is by using a bank account. The police in Delhi who investigate cybercrime have found that a lot of accounts were opened at bank branches. These accounts were opened using identity documents that were borrowed bought or stolen. Then these accounts were rented out to groups of criminals. One bank branch keeps coming up in complaints. This is not bad luck it is a sign of a bigger problem with how banks check who is opening an account.

These fake accounts, which are called " accounts" are controlled by criminal groups, not the people whose names are on the accounts. These accounts are a part of the cybercrime problem in India. The mistakes that bank branches make which allow these accounts to be opened raise a lot of questions. These questions are about how banks check who is opening an account how they prevent money laundering and how they work with groups to stop cybercrime. The bank accounts are the way that cybercriminals in India get away with the money they steal from people. The cybercrime investigators keep finding bank accounts like the ones at the bank branch, in Delhi, where the money was stolen.

The Anatomy of a Mule Account Network

The pattern is now familiar to investigators. A fraud complaint on the National Cyber Crime Reporting Portal traces a victim's stolen money to a beneficiary account. When police pull the account-opening file, the person named on the KYC documents often denies ever visiting the branch or signing the forms; signature verification frequently shows a mismatch. In one recent Delhi case, a cooperative bank's deputy manager was arrested after a single account he had helped open surfaced in 159 separate cyber fraud complaints from across the country, with transactions worth nearly Rs 68 crore routed through it before detection. Similar investigations have uncovered supply gangs that procure dozens of accounts at a time using POS machines, stacks of ATM cards, and cheque books belonging to different people and rent them out to fraudsters as ready-made conduits for stolen money.

What makes a single branch or a small cluster of accounts significant is what it reveals about entry-point failure. Investigators do not describe these as sophisticated hacking operations; they describe them as verification failures as are accounts opened without the mandatory in-person checks, video KYC, or document authentication that RBI rules require. When 96, or 700, or 8.5 lakh mule accounts are traced back through a handful of branches and intermediaries, the story is not really about the fraudsters at the far end of the chain. It is about the choke point where honest oversight should have stopped the account from ever existing.

Where the KYC Framework Is Breaking Down

The RBI's Know Your Customer Master Direction requires banks to establish customer identity, verify a genuine business relationship, and apply risk-based due diligence before allowing an account to operate. In practice, investigators have repeatedly found accounts opened through complicit or negligent bank staff, business correspondents, and third-party agents who bypass these checks entirely. Analysts note that mule accounts systematically exploit gaps in customer onboarding, KYC verification, transaction monitoring, and dormant-account surveillance, with criminals using forged or stolen identity documents and layering funds across multiple accounts to escape detection. Economically vulnerable individuals who are daily-wage workers, students, the unemployed are frequently paid a small commission to hand over their documents or existing accounts, often without understanding that they could face criminal liability for transactions they never authorised.

This is compounded by a financial-inclusion paradox that regulators themselves acknowledge: India has expanded banking access faster than it has expanded financial and digital literacy, leaving a population that is easy to recruit knowingly or unknowingly  into mule networks. The result is a KYC regime that looks robust on paper but is only as strong as its weakest branch-level implementation, and weak implementation has proved trivially easy for organised networks to locate and exploit at scale.

The Regulatory and Institutional Response

RBI: From Static Compliance to Active Detection

The Reserve Bank of India has moved beyond periodic KYC audits toward technology-driven detection. It has directed banks to tighten onboarding controls, strengthen transaction monitoring, and report suspicious activity more proactively, and it has proposed additional safeguards, including limits on aggregate credits into accounts where a satisfactory business relationship has not yet been established. Its most significant intervention is MuleHunter.ai, an AI and machine-learning system built to flag suspected mule accounts from transaction-behaviour patterns rather than static KYC data alone; the platform is already operational across roughly two dozen banks and is being expanded. The RBI Innovation Hub has also begun working directly with the Indian Cyber Crime Coordination Centre (I4C) to share fraud-risk intelligence and coordinate detection in near real time.

FIU-IND and the PMLA Framework

The Prevention of Money Laundering Act, 2002 (PMLA) is the backbone of India's AML architecture. It mandates KYC verification, Customer Due Diligence, record maintenance, and timely reporting of suspicious transactions to the Financial Intelligence Unit–India (FIU-IND). Banks are required to file Suspicious Transaction Reports (STRs) and Cash Transaction Reports with FIU-IND, which in turn analyses financial intelligence and shares it with law enforcement and regulators. On paper, this creates a feedback loop between banks, the RBI, and enforcement agencies; in practice, the sheer volume of mule-linked transactions are hundreds of thousands of accounts flagged nationally  has strained the capacity of this reporting chain to generate timely, actionable freezes before funds are withdrawn or converted to cryptocurrency.

The IT Act, CERT-In, and Cyber Enforcement

The Information Technology Act, 2000, together with provisions of the Bharatiya Nyaya Sanhita, provides the criminal-law basis for prosecuting mule account operators, aggregators, and the fraudsters who direct them. CERT-In's role sits slightly upstream of the banking layer: it issues advisories on phishing, fake payment gateways, and compromised digital infrastructure that fraud syndicates use to recruit mule account holders and move money. The Ministry of Home Affairs' I4C coordinates the National Cyber Crime Reporting Portal and the 1930 helpline, which allow victims to report fraud and trigger a limited window for freezing beneficiary accounts. I4C has also issued direct public alerts against illegal payment gateways built on mule accounts, warning citizens not to rent or sell their bank credentials to intermediaries.

The Coordination Gap

None of these institutions is short of legal authority. The gap is operational: banks, the RBI, FIU-IND, state police cyber cells, the CBI, and I4C each hold a piece of the picture, but no single agency has a real-time, end-to-end view of an account from opening to fraud to freeze. A mule account can be flagged by one bank's internal monitoring, reported through a completely different victim's complaint in another state, and investigated by a third jurisdiction's cyber police with each step introducing delay. The Indian Banks' Association has publicly pushed for the RBI to be given clearer power to directly freeze accounts flagged as mule accounts, rather than requiring each bank to act unilaterally or wait for a police request, precisely because this fragmentation lets fraudsters withdraw or launder funds within hours of a transaction.

Policy Recommendations

1. Mandatory video-KYC and biometric re-verification for all new accounts opened through business correspondents and third-party agents, with personal liability for verifying bank officials found complicit.

2. A statutory, RBI-backed mechanism allowing banks to freeze accounts flagged by MuleHunter.ai-type systems or FIU-IND intelligence within hours, rather than only after a formal police complaint.

3. A unified, interoperable case database linking the National Cyber Crime Reporting Portal, FIU-IND's STR system, and state cyber cells, so that an account flagged once is visible to every agency instantly.

4. Stronger due-diligence audits of banking correspondents and cooperative banks, which recur disproportionately in mule account cases relative to their share of total accounts.

5. Public financial-literacy campaigns targeted at the economically vulnerable groups most often recruited as unwitting mule account holders, paired with clear legal guidance distinguishing victims from willing participants.

Conclusion

The branch-level mule account cases surfacing across Delhi and other cities are not isolated policing stories; they are a live audit of India's AML and KYC architecture. The RBI, FIU-IND, CERT-In, and law enforcement agencies each have credible tools and legal mandates like MuleHunter.ai, PMLA reporting, IT Act prosecutions, and I4C's coordination portal chief among them but fraud syndicates continue to outpace the system by exploiting the seams between institutions rather than any single point of failure. Closing that gap requires less new law and more operational integration: faster account freezes, verified accountability at the point of account opening, and a shared, real-time picture of mule networks across every agency involved. Until banks, regulators, and investigators can act as one system rather than several disconnected ones, every dismantled racket will simply be replaced by the next.

References

1. https://aninews.in/news/national/general-news/delhi-police-arrests-bank-deputy-manager-in-83776792-crore-mule-account-case-linked-to-159-cyber-fraud-complaints20260610130737/
2. https://the420.in/delhi-bank-manager-mule-account-cyber-fraud-case/
3. https://www.business-standard.com/finance/news/what-are-mule-accounts-cybercrime-banking-layer-india-fraud-rbi-126062400855_1.html
4. https://www.business-standard.com/india-news/centre-freezes-450-000-mule-bank-accounts-used-in-cyber-fraud-schemes-124111200320_1.html
5. https://www.medianama.com/2025/04/223-iba-rbi-cyber-fraud-measures-freeze-bank-accounts-cybercrime/
6. https://www.deccanherald.com/amp/story/india%2Fcentre-warns-of-illegal-payment-gateways-and-mule-accounts-3252723
7. https://www.deccanherald.com/india/over-85-lakh-mule-accounts-in-700-bank-branches-used-by-cyber-criminals-cbi-3604229
8. https://website.rbi.org.in/en/web/rbi/-/notifications/master-direction-know-your-customer-kyc-direction-2016-updated-as-on-may-04-2023-lt-span-gt-11566
9. https://www.indiacode.nic.in/bitstream/123456789/15402/1/moneylaunderingact2002.pdf
10. https://www.indiacode.nic.in/bitstream/123456789/13116/1/it_act_2000_updated.pdf
11. https://www.mha.gov.in/en/division_of_mha/cyber-and-information-security-cis-division/Details-about-Indian-Cybercrime-Coordination-Centre-I4C-Scheme

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