Artificial Intelligence (AI) has rapidly transformed industries across the globe, and the legal sector is one out of them. Starting with various researches, document scanning and contract drafting to analysis of facts, incidents and laws pertaining to a dispute, AI is supposed to be efficient, expeditious and cost savings.
However, though loaded with these benefits AI also accompany significant hazards that must be considered and dealt carefully. The legal profession operates within a framework of accountability, trust, confidentiality, and multiple options as strategic implementation to get desired results. This analysis is mainly contemplating that the usage of AI in legal framework may be disastrous, if it is not implemented carefully. While, we will demonstrate the potential risks which AI carries while implementing into legal practice, however at inception, it is truly indispensable to understand how does AI makes decision.
To examine & understand the hazards of AI in legal technology, it is paramount to first explore how does AI systems collects data, process information and comes out with conclusion.
AI systems rely on diverse data sources to generate and among the varied data sources, automated crawlers, such as Common Crawl (a 501(c)(3) non–profit founded in 2007) is the primary source to gather huge amounts of available web data in public domain. Since 2008, Common Crawl has archived billions of web pages, which are mainly stored in Amazon S3 buckets, which is freely accessible to researchers, developers, and organizations worldwide. Researchers and developers use this database to train machine learning models, conduct web scraping, and perform large-scale data mining. The bulky size and diversity of content available with Common Crawl makes it extremely valuable, though the inherent risk, such as errors in content, outdated information, or biased content, contained in it can easily be swamped by AI.
Various data providers offer curated, high-quality, and categorically tailor-made databases for specific applications or requirements, based upon requisitions or situation. Essentially, these databases are often imbibed with high quality but expensive, limiting accessibility to deep pocket organizations.
In today’s scenario there are companies which are making rigorous efforts to leverage their internal datapoints to build customized AI models. This ensures alignment with specific business needs and ensures higher data quality, but it also raises concerns about privacy and security, there is any data breach or theft happens.
Search engines and digital libraries such as Wikipedia generally provide open-source information. While these databases might be useful, however these sources are not always reliable, categorically for legal matters requiring expertise and bonafide skill.
Further, various social media platforms, discussion forums (viz google groups) and online communities contribute extensive insights based upon human behaviours, opinion, and preferences. However, such content is often subjective, biased, or manipulated and is always open for debate or comments from the experts or critics.
Since AI does not perform any action on its own in isolation and is absolutely dependent upon the data, hence AI’s decision-making is only as good as the data it processes. If flawed or misleading information is being given greater weight, the resulting conclusions can be dangerously inaccurate. In legal landscape, where the facts and legal provisions are analysed with every comma and full stop, the analysis is extremely difficult for AI to come out with precision and accuracy, and if there is a small gap or error in data inputs, the risk in result gets magnified.
With backdrop of basic, and broader understanding of the reasoning and process of decision making by Artificial Intelligence, now we can understand the hazards of AI in Legal Tech.
Accountability is most important cornerstone of the legal profession. Lawyers, judges, corporates, and institutions are held responsible for their actions, drafting, and submissions. A lawyer is taught to think twice before finalising a legal document, however, AI operates without personal liability. If an AI system drafts a contract and omits a critical clause, who would be responsible? As of now, we don’t have a credible answer, might be subscriptions to AI model may come out with some basic assurance, but simultaneously AI will take the shelter of “AS IS” warranty without providing any recourse to user in case of error, omission, negligence, or manipulation in results being generated by AI.
On 25 April 2026, a pushed command from Agentic AI ‘Claude’ (the most trusted and advanced AI in extant time), wiped out an entire dataset of a Software-as-a-Service (SaaS) platform PocketOS, in less than ten seconds in USA. Such incidents highlight the delicacy of relying on AI model without clear accountability structures. In legal practice, a single error could jeopardize a case, nullify an agreement, or expose clients to significant risk because of the failure to the compliance regime.
Confidentiality is fundamental to legal content and work produced under legal department. Strategic information, business launches, commercial pricing, deal valuations, case strategies, clients’ disclosures, and corporate transactions must remain secure, till it is finally accomplished and announced (with mutual consent). However, AI systems often process data through external servers or cloud-based applications. A single breach in system securities, could expose sensitive details, damaging reputations, commercial structure, and financial interests. Term sheets executed with AI assistance may inadvertently compromise confidentiality, unless strictly controlled within secure enterprise frameworks. Even enterprise-controlled models are vulnerable to hacking, leaving confidentiality at risk perpetually.
AI is completely creating ambiguity among the boundaries of intellectual property (IP). It can generate music resembling a famous singer, artwork mimicking an established painter, or legal documents echoing a lawyer’s style. These raises pressing questions:
- Who owns AI-generated content?
- Does copyright belong to the developer, the user, or the AI itself?
- How should courts adjudicate disputes involving AI-created works?
The ambiguity surrounding ownership creates challenges for businesses and legal systems. Until clear frameworks are established, disputes over AI-generated IP will be most pressing issues in the court rooms, in coming years. AI can create videos showing individuals saying or doing things they never did. These can damage reputations, extort victims, or mislead the public. Recently, one of the startup’s co-founder, Mr. Aman Gupta, approached High Court of Delhi for protection of his personality rights.
Various integrated factors using AI technologies are becoming tools and means to spread misinformation and disinformation, influencing and manipulating people’s behaviours, actions, and decision in a pre-meditated framework. AI-generated robocalls imitating President Joe Biden’s voice were made to discourage voters from participating and casting their votes in elections.
Legal systems have already encountered AI-related misinformation and have been critically examined in USA and India. In 2023, a U.S. lawyer, in a personal injury lawsuit in Manhattan, had submitted non-existing & fabricated case laws generated by ChatGPT, which were rebuked by the Federal Court.
During March 2026, even Apex Court of India has cautioned the legal consequences after a judge was found to have adjudicated on a property dispute matter in Andhra Pradesh, using fake judgements generated by artificial intelligence. Such incidents demonstrate how AI can undermine trust in legal institutions.
Most importantly, AI outputs may arise not only from flawed data but also from programming errors. Algorithms may inadvertently favor certain outcomes, producing skewed results. In law, biased inputs or erroneous programming can distort case analysis, misrepresent precedents, or unfairly disadvantage parties. Unlike human bias, which can be scrutinized and challenged, algorithmic bias is often non-traceable within complex construct of code. Example- E-commerce companies have encountered multiple bugging scenarios, where e-commerce portals have tagged the Cash On Delivery products order, inadvertently as Pre Paid orders and the delivery were made to the customer without collection of cash.
Legal work is inherently complex, involving multiple variables of strategies, alternatives, negotiations, disclosures, and pleadings. Unlike formula-driven fields such as excel sheet in finance or coding, law requires balanced and analysed judgment with contextual understanding in every option applied with different strategy. Multiplicity of options are the tough nuts to b cracked by AI in Legal strategy. AI models are generally more efficient & effective with fewer variables (lesser options); whenever, AI is forced to push with too many variables, it may miss to analyse the all-meaningful patterns succinctly. This malfunctioning reduces efficiency and can lead to poor performance. For example, AI may misinterpret client disclosures during litigation, resulting in flawed pleadings. The dynamic nature of law makes it difficult for AI to replicate human reasoning and eyes for details.
Practically, if AI misses a single important clause in contract with minute details, then technically user of AI is not mitigating risk, rather the inherent risk is simply getting deferred and it may explode at any point in time. It’s paramount to highlight that, we are living in the age where a comma can cost millions. In 2017, the U.S. Court of Appeals for the First Circuit handed down a decision in O’Connor v. Oakhurst Dairy, that cost the dairy company $5 million, all because of a missing comma.
Artificial Intelligence offers immense potential for the legal industry in basic researches, promising efficiency, speed, and innovation while analysing data. Nonetheless, its hazards viz; absence of absolute of accountability, confidentiality risks, intellectual property chaos, misinformation, tech errors or bias, and difficulty handling complex variables, must be considered appropriately beforehand.
Lawyers must remain actively involved in reviewing AI outputs, ensuring accuracy with greater details. The legal profession must approach AI with caution, balancing innovation with responsibility.
(Views are personal)
