Indian stock market in terms of losing its integrity and transparency has been facing a big issue that is insider trading. As there is a shift of trading and communication towards the online mode, the nature of this crime is also changing. In essence, before the introduction of the SEBI (Prohibition of Insider Trading) Regulations, 2015, most of the insider trading enforcement under SEBI had been after the act was done, derived from the use of trade records, phone logs, and other physical evidence. Currently, information sharing is through encrypted chats, messaging apps, and unofficial digital networks, so detecting the breach becomes more challenging. The author believes that Artificial Intelligence (AI) can be a powerful tool to find and solve insider trading crimes in India.

According to the paper, there are many ways AI can be used to detect unusual behaviour in the stock market, to locate digital footprints, and even to prevent violation of laws. At the same time, it poses issues such as privacy, bias, accuracy, and legal acceptance of AI-generated evidence. The main question the paper tries to answer is whether technology and due process can be effectively merged. Finally, the paper proposes a balanced or hybrid enforcement model that is able to raise the standards of market regulation while, at the same time, protecting the rights of the individual.

Keywords: Insider Trading Detection, Artificial Intelligence in Securities Regulation, Digital Market Surveillance, Privacy and Due Process Balance

India's financial markets where the fair trading of billions of government securities remains vulnerable to insider trading, which is a cancerous illness that ruins the market fairness and transparency. Traditionally, enforcement under the SEBI (Prohibition of Insider Trading) Regulations, 2015, has been based on physical evidence and eyewitness testimonies. However, with the advent of digital trading, the enforcement regime is now gradually evolving into incorporating digital footprints left behind by users of messaging apps. Besides, a different and very important dimension might even be added if such enforcers decide to use artificial intelligence (AI). It is a promising technology with the capability of sealing leaks not only after them but before their occurrence, by scanning a diverse range of data promptly for anomalous behaviours, which, when ignored, typically result in illicit gains. However, there is yet this big consideration of whether or not AI is the right tool for the job of law enforcement. People may feel uncomfortable about it because it might be inaccurate, it might be biased, or if it is turned against them, their rights might be violated. The article at present discusses the potential impacts of AI on the law of insider trading in India from the 

perspective of the co-existence between law enforcement and the rights of the accused, and finally deliver a proposal for a hybrid model that balances the opposing interests, thus, leveraging the legal discussion on technology and market supervision.

SEBI's once reactive enforcement of insider trading laws has been reconstructed by the agency's investigations of illicit cases. Initially, the enforcement teams used evidence from trade records, work logs, and telephone records to build a case after the violation. Now, The Regulations 2015 explicitly include the term "unpublished price sensitive information (UPSI)" and if connecting persons "influencing" or having "access" to such information, then the communication or usage of it is in prohibition. Recently, a few cases have been exposed to the public, where an earnings leak was done through WhatsApp, which shows that digital accounting has made various markets to be very vulnerable. In India, with more than 4, 000 companies listed on the NSE and BSE, such tools could identify "whisper networks", conversations encoded by traders that always predate announcements. Instead of being based on static, rules-based surveillance, AI develops and learns from past SEBI orders to refine its models. This kind of proactive approach is needed in the contemporary times to keep the market competition fair too.

SEBI's current Market Surveillance System in India could be integrated with AI, which might shorten the time for investigation from several months to just a few days. Yet, such a radical change requires a revamp of legalities. Present-day rules do not explicitly cover the use of AI as evidence. According to the Indian Evidence Act, 1872, electronic records can be used as evidence if they are proven to be authentic. However, AI results, which are probabilistic rather than deterministic, put a strain on the traditional concept of reliability. Suppose an AI determines that a particular trade is suspicious with a confidence level of 70%. The court may require that the AI "black box" be opened, that is, the AI system being explainable or comprehensible, so that there is a clear trail of how decisions are made from the data. A proposal to amend the PIT Regulations: Add an "AI Evidence Clause" that would require a regulator to make public its model training data, validation metrics, and human oversight procedures. This would be a preventive measure against false positives.

Bias amplification is indeed one more problem. AI systems, which rely on historical data, will continue to be biased if this data exhibits disparities such as unfairly flagging trades of smaller companies or less digitally active regions. As a result, the trade of a diverse economy like India might be negatively impacted. One way to address this is by requiring a "Fairness Audit Framework". SEBI may partner with institutions to come up with Hindi and regional language multilingual NLP-based domestic models representing the prevalent corporate 

communications. Moreover, invoking blockchain for audit trails that cannot be altered could go a long way in restoring faith in AI an appeal to the Securities Appellate Tribunal (SAT) would be justifiable if any anomaly detected and flagged was logged immutably.

Privacy issues appear to be the primary concern, the Supreme Court case K.S. Puttaswamy (2017) being the touchstone for proportionality. Monitoring devices or social media through AI would be a violation of the individual's Article 21 rights; thus, there should be consent-taking or anonymous data usage. How about this idea: Let SEBI introduce "Data Sandboxes" where AI developers work on anonymized market data without a breach of personal privacy. This is the same principle as RBI's regulatory sandboxes for fintech, which encourage innovation and contain the risks at the same time. Furthermore, the corporate world has to get ready as well: The AI literacy training must be a part of compliance code which will not only educate employees on the recognition of algorithmic monitoring but will also enable them to practice the use of encrypted and audited channels that prevent them from being mistakenly flagged.

On ethical grounds, the involvement of AI in extracting intention makes the situation even more complicated. Insider trading is mainly about the guilty mind, but IR machines are good at finding correlations, not causes. Some might wish to criticize overdependence of the system on AI as the solution to all problems, referring to the possible hacking or adversarial attacks capable of tricking the models. Analysing the industries where the practice is more prevalent and taking measures to curb those is the way forward. 

CONCLUSION

Therefore, with the continuing digitalization of the markets, emerging technologies such as artificial intelligence, the metaverse, and quantum computing are slowly becoming reality. They can be the tools for SEBI to be ahead of the game in protecting a trillion-dollar economy's integrity. The future of securities law will focus less on trying to track obscure digital footprints and more on implementing intelligent systems that offer transparency, accountability, and better market supervision.