AI-GENERATED EVIDENCE IN INDIAN COURTS: ADMISSIBILITY, RELIABILITY AND EVIDENTIARY CHALLENGES 

AUTHOR: Nikita Prajapati, DIMT Law College

Abstract 

The rapid development of Artificial Intelligence (AI) and generative AI has transformed the manner in which information can be created, processed, analysed and presented. Courts increasingly encounter digital material such as photographs, videos, audio recordings, documents, computer-generated reports and other electronic records. At the same time, AI tools can generate synthetic content that may appear authentic even when it has no reliable connection with an actual event. This creates a difficult question for evidence law: when AI is involved in the creation, alteration or analysis of digital material, how should a court determine whether that material is legally admissible and sufficiently reliable to be relied upon? 

The Bharatiya Sakshya Adhiniyam, 2023 (BSA) recognizes electronic and digital records and provide a statutory framework for their admissibility. However, the legislation does not contain a detailed and technology-specific regime dealing with generative AI, synthetic media or AI-generative material. This article examines whether the existing provisions of the BSA, particularly sections 39 and 61-63, are sufficient to address these emerging challenges. It also analyses the principles developed by the Supreme Court in Anvar P.V. v P.K. Basheer and Arjun Panditrao Khotkar v Kailash Kushanrao Gorantyal, and considers the Supreme Court’s recent decision in Pooja Ramesh Singh v Jammu and Kashmir Bank Ltd. The article argues that admissibility alone cannot guarantee evidentiary reliability. A future-ready framework should combine statutory requirements, digital forensic examination, disclosur of AI involvement, preservation of metadata and hash values, expert verification and meaningful human oversight. 

Keywords 

Artificial Intelligence, Digital Evidence; Electronic Records; Bharatiya Sakshya Adhiniyam; Authentication; Digital Forensic; Generative AI 

1. INTRODUCTION

Evidence is the foundation upon which judicial findings are constructed. A court may possess jurisdiction, legal authority and procedural safeguards, but the ultimate determination of disputed facts depends upon the quality and reliability of the evidence placed before it. The transformation of technology has consequently transformed evidence law. Documents, photographs, CCTV footage, emails, call records, social-media communications, mobile-phone data and other electronic material have become routine components of modern litigation and criminal investigation. 

The increasing use of Artificial Intelligence has introduced another layer of complexity. AI systems are now capable of generating text, images, audio, video and other forms of digital content. Generative AI can produce material that resembles genuine human-created content to such an extent that an ordinary observer may find it difficult to identify whether it is authentic. AI can also be used in a different manner: investigators, lawyers and experts may employ AI to analyse existing evidence without AI itself becoming the source of that evidence. These situations should not automatically be treated as legally identical. 

For example, a CCTV recording may be genuine evidence even if an AI system is subsequently used to improve its resolution. By contrast, a completely synthetic video generated by an AI system may not represent any real-world event at all. Similarly, an AI-generated transcription may be used as an analytical aid while the original audio recording remains the primary material requiring examination. The legal question therefore cannot simply be whether AI was involved. The relevant questions are how AI was involved, what the underlying source was, whether the material has been altered, whether the process can be independently verified and whether the material satisfies the requirements of evidence law. 

Indian evidence law has recently undergone a major statutory transition through the Bharatiya Sakshya Adhiniyam, 2023. The BSA came into force on 1 July 2024 and expressly recognizes electronic or digital records within its evidentiary framework. Section 61 provides that an electronic or digital record cannot be denied admissibility merely because it is electronic or digital, subject to section 63. Section 62 and 63 establish the special framework governing proof and admissibility of electronic records. Section 39 additionally recognizes the relevance of expert opinion concerning information transmitted or stored in computer resources or other electronic or digital forms.

However, the BSA was enacted at a time when the technology surrounding generative AI was developing rapidly. It does not expressly create a separate category for AI-generated evidence or establish a detailed authentication protocol for synthetic media. Consequently, courts may have to apply existing principles of electronic evidence to technologically novel material. 

This issue is particularly significant because authenticity and reliability are not identical concepts. An electronic record may satisfy formal requirements for admission while still raising serious questions regarding its accuracy or probative value. The Supreme Court’s jurisprudence on electronic evidence demonstrates the importance of source and authenticity. In Anvar P.V. v P.K. Basheer, the Court treated the statutory requirements concerning electronic records as a special evidentiary regime. In Arjun Panditrao Khotkar v Kailash Kushanrao Gorantyal, the Court reaffirmed the importance of the statutory certification framework and emphasized the vulnerability of electronic records to manipulation.

The emerging concern is therefore not merely whether courts can admit AI-related material. It is whether courts can assess such material without allowing technological complexity to undermine procedural fairness and the search for truth. 

The issue has become particularly visible in 2026. In Pooja Ramesh Singh v Jammu and Kashmir Bank Ltd, the Supreme Court dealt with judgments that relied upon non-existent or hallucinated legal material generated through AI. The Court set aside the affected decisions and stressed the importance of maintaining human control over adjudication while using AI as an aid. Although the case concerned AI-generated legal material rather than conventional forensic evidence, its reasoning provides an important warning: technological output cannot replace independent verification. 

This article therefore examines the admissibility, authenticity and reliability of AI-generated and AI-assisted evidence under Indian law. It argues that the existing BSA framework provides a useful foundation but requires judicial and institutional safeguards specifically addressing AI involvement. 

2. UNDERSTANDING AI-GENERATED EVIDENCE

The expression ‘AI-generated evidence’ requires careful definition because not every piece of digital evidence connected with AI is actually generated by AI. 

1. AI-Generated Material 

AI-generated material is content created substantially through an artificial intelligence system rather than directly recorded from a real-world event. Examples include synthetic photographs, fabricated audio recordings, generated videos, AI-written documents and other forms of synthetic media. 

The legal difficulty arises because such material may visually or acoustically resemble genuine evidence. A fabricated video may appear to show a person making a particular statement even though the event never occurred. A synthetic voice may resemble that of a real individual. A generated document may contain realistic formatting and language. The appearance of authenticity, therefore, cannot by itself establish authenticity. 

2. AI-Assisted Evidence 

AI-assisted evidence is different. In this category, the underlying evidence originates from a real source, but AI is used as a tool to process or analyse it. For example, AI may assist in organizing large quantities of CCTV footage, enhancing an image, transcribing an audio recording, identifying patterns or searching through large datasets. 

Here, the legal focus should remain on the original evidence and the methodology used to process it. AI assistance should not automatically destroy admissibility. However, the party relying on the output should be able to explain what tool was used, what input was provided, what transformation occurred and whether the original material remains available for independent verification. 

3. AI-Analysed Evidence

A third category is evidence that is independently generated but interpreted with the assistance of AI. Digital forensic systems may use algorithms to detect anomalies, compare images or identify patterns. In such circumstances, the AI output is essentially an analytical opinion or investigative lead rather than an unquestionable statement of fact. 

This distinction is important because an algorithmic conclusion should not automatically become a substitute for judicial evaluation. The human expert must remain capable of explaining the methodology, limitations and basis of the conclusion. 

The three categories can therefore be represented as follows: 

1. AI-generated material – AI creates the substantive content. 
2. AI-assisted evidence – genuine evidence is processed or enhanced using AI. 
3. AI-analysed evidence – genuine evidence is interpreted with the assistance of AI. 

Indian evidence law would benefit from expressly recognizing these distinctions because the risks associated with each category are different. 

3. EVOLUTION OF ELECTRONIC EVIDENCE IN INDIA 

The Indian legal system has gradually adapted to the increasing importance of electronic information. The information Technology Act, 2000 provided the initial statutory foundation for recognizing electronic transactions and electronic records. The Indian Evidence Act, 1872 was subsequently amended to introduce specific provisions dealing with electronic records. 

The Supreme Court’s decision in Anvar P.V. v P.K. Basheer represented a significant development. The Court held that secondary evidence relating to electronic records was subject to the special statutory requirements governing electronic records. The decision emphasized that electronic evidence cannot simply be treated in the same manner as ordinary documentary evidence because its production and reproduction involve distinctive technological characteristics. 

The Court subsequently considered the issue in Arjun Panditrao Khotkar v Kailash Kushanrao Gorantyal. The Court reaffirmed the special nature of the statutory framework governing electronic evidence and highlighted the importance of safeguards directed towards source and authenticity. The decision demonstrates an underlying principle that remains relevant even after the enactment of the BSA: digital material requires a reliable connection between the information presented to the court and its original source. 

The BSA has now replaced the Indian Evidence Act as the principal central legislation governing evidence. Rather than treating electronic material as an exceptional technological category, the BSA expressly incorporates electronic and digital records into its structure. 

This development is important for AI-related evidence. The law no longer needs to answer whether an electronic record can theoretically be evidence. The more difficult question is whether the existing requirements are sufficient to determine the authenticity and reliability of technologically sophisticated AI-generated material. 

4. LEGAL FRAMEWORK UNDER THE BHARATIYA SAKSHYA ADHINIYAM, 2023

4.1 Section 39: Expert Opinion 

Section 39 of the BSA deals with opinions of experts. Its significance in AI-related litigation is considerable because courts may require specialist assistance to understand the technical characteristics of digital material. 

Section 39(2) provides that when a court has to form an opinion concerning information transmitted or stored in a computer resource or another electronic or digital form, the opinion of an Examiner of Electronic Evidence referred to in section 79A of the Information Technology Act, 2000 is a relevant fact.

This provision recognizes that courts cannot reasonably be expected to possess specialized technical knowledge concerning every aspect of digital technology. AI authentication may involve examination of metadata, compression patterns, file structure, source information, manipulation indicators, forensic artefacts and other technical characteristics. 

However, expert evidence should assist the court rather than replace judicial decision-making. An expert may explain why a recording appears to have been manipulated, how a file was processed or what technical limitations affect an AI detection system. The ultimate question of whether evidence should be accepted and what weight should be given to it remains for the court. 

4.2 Section 61: Electronic or Digital Record 

Section 61 establishes an important principle: an electronic or digital record is not inadmissible merely because it exists in electronic or digital form.

This provision prevents technological form from becoming an artificial barrier to evidence. At the same time, section 61 is expressly subject to section 63. Therefore, recognition of an electronic record does not mean that every digital file is automatically admissible. 

For AI-generated material, this distinction is crucial, A synthetic video cannot become reliable merely because it is stored as a digital file. The court must still determine whether the statutory requirements are satisfied and whether the material has sufficient evidentiary value. 

4.3 Section 62: Special Provisions 

Section 62 states that the contents of electronic records may be proved in accordance with section 63. This reinforces the special statutory mechanism for proving electronic records. 

The existence of a specific procedure is important because digital material can be copied, modified, transmitted and reproduced without obvious physical signs of alteration. A proper evidentiary system therefore needs to establish a dependable relationship between the material produced in court and the information originally stored or generated. 

4.4 Section 63: Admissibility of Electronic Records 

Section 63 is central to the present discussion. It provides the conditions under which information contained in an electronic record or computer output may be admitted as evidence.

The provision is designed around the reliability of the computer or communication device and the circumstances in which the information was produced or stored. It also incorporates certification requirements for specified electronic records. 

Is an AI-related case, compliance with section 63 should not be treated as the end of the inquiry. Certification may establish procedural compliance concerning the electronic record, but it does not necessarily prove that the substantive content is truthful. 

For instance, a perfectly preserved AI-generated video may satisfy requirements concerning its electronic form while remaining a synthetic representation of an event that never happened. Thus, admissibility and probative reliability must remain analytically distinct. 

5. ADMISSIBILITY OF AI-GENERATED EVIDENCE BEFORE INDIAN COURTS

The central legal question can be divided into four stages: relevance, admissibility, authenticity and evidentiary weight. 

First, the material must be relevant to a fact in issue or a relevant fact. AI involvement does not make otherwise irrelevant material relevant. 

Second, the material must satisfy the applicable admissibility requirements. For electronic records, the BSA provides the principal statutory framework. 

Third, authenticity must be examined. The court must ask whether the material is what the party presenting it claims it to be. This question becomes especially difficult where generative AI can produce convincing synthetic content. 

Fourth, even where admitted, the court must decide what weight the evidence deserves. 

The traditional distinction between admissibility and weight becomes particularly important in the AI context. A court should not reason that because a digital file has been properly produced and certified, every factual assertion contained in it must therefore be true. 

The Supreme Court’s electronic evidence jurisprudence supports careful attention to authenticity, In Anvar, the Court stressed the special evidentiary treatment of electronic records. In Arjun Panditrao, the Court again highlighted the susceptibility of electronic records to manipulation and the importance of safeguards concerning their source and authenticity.

These principles can be adapted to AI- generated evidence. Where a party relies upon a synthetic or AI-processed file, the court should require information sufficient to understand: 

• The original source of the material; 
• Whether the original file still exists; 
• The identity and version of the AI tool used; 
• The purpose for which AI was used; 
• The nature of the processing or generation; 
• The date and circumstances of processing;
• Relevant metadata and hash values; 
• The chain of custody; 
• The methodology used for authentication; and 
• The qualifications of any expert providing an opinion. 

  Such information would allow the court to distinguish between a genuine digital record, a          manipulated record and an entirely synthetic creation. 

6. RELIABILITY AND AUTHENTICATION CHALLENGES 

6.1 AI Hallucination

Generative AI systems can produce outputs that appear authoritative but are factually incorrect. The problem is commonly described as AI hallucination. In a legal context, such outputs are dangerous because their sophisticated presentation can create a false impression of reliability. 

The Supreme Court’s 2026 decision in Pooja Ramesh Singh illustrates this danger. The Court considered a situation in which AI-generated, non-existent or hallucinated legal material had been relied upon in adjudication. The decision is significant beyond the specific facts because it is relied upon in a judicial process. 

6.2 Deepfakes and Synthetic Media 

AI-generated images, audio and video present Particularly serious authentication challenge. Traditional assumptions that a photograph or recording represents an actual event are increasingly unsafe. 

Authentication should therefore involve examination of the original source, metadata, file history and surrounding circumstances. Technical analysis may also be required to identify inconsistencies suggesting manipulation. 

No single AI-detection tool should be regarded as infallible. Detection technologies themselves may produce false positives and false negatives. A court should therefore prefer a multi-layered authentication approach rather than relying on one automated score. 

6.3 Metadata and Hash Values

Metadata can provide information about the creation, modification and handling of a digital file. Hash values can function as digital fingerprints and assist in determining whether a file has changed after acquisition. 

However, metadata should not be treated as conclusive proof. Metadata can sometimes be altered or removed, and the absence of metadata does not automatically establish fabrication. It should be considered alongside other evidence. 

6.4 Chain of Custody 

The chain of custody is essential to digital evidence because evidence may pass through investigators, forensic laboratories, devices, storage systems and court processes. 

Where AI processing is performed, the chain of custody should record not merely the physical or digital movement of the file but also the technological transformations applied to it. The original should be preserved wherever possible, and processed copies should be clearly identified as such. 

6.5 The Black-Box Problem 

Some AI systems operate through complex models that are difficult for non-specialists to understand. If an expert tells the court that an AI system identified a particular image as manipulated, the court should be able to understand the basis and limitations of that conclusion. 

An unexplained algorithmic result should not receive automatic judicial confidence merely because it is produced by advanced technology. 

7. ROLE OF FORENSIC EXPERTS AND HUMAN VERIFICATION 

Digital forensic experts have an increasingly important role in evaluating AI-related evidence. Section 39 of the BSA and section 79A of the Information Technology Act provide a statutory basis for expert involvement in electronic evidence.  

The role of the expert should include preservation, examination, explanation and interpretation. An expert should ideally be able to identify the source material, explain the tools used, describe the methodology, disclose limitations and provide a reasoned conclusion. 

The government framework concerning Examiners of Electronic Evidence also reflects the importance of technical competence in digital forensics. The notified framework covers areas including computer forensics, network forensics, mobile-device forensics and digital video, image and audio examination.

For AI-generated material, expertise may require additional specialization in machine learning, synthetic-media detection and AI systems. Courts should therefore avoid treating all digital forensic expertise as automatically equivalent to expertise in every form of AI technology. 

Most importantly, human verification should remain central. An AI system may assist an expert, but the expert should remain responsible for explaining the conclusion to the court. The Supreme Court’s 2026 approach in Pooja Ramesh Singh strongly supports this principle of maintaining human control while using AI as an aid.

8. JUDICIAL APPROACH: FROM ELECTRONIC EVIDENCE TO AI-GENERATED MATERIAL 

Indian judicial treatment of electronic evidence provides a foundation for future AI-related cases.

 In Anvar P.V. v P.K. Basheer, the Supreme Court recognized the special statutory treatment of electronic evidence and rejected an approach that would bypass the specific safeguards governing such material.

In Arjun Panditrao Khotkar v Kailash Kushanrao Gorantyal, the Court reaffirmed the importance of the statutory framework and emphasized the dangers associated with electronic records being susceptible to alteration and manipulation.

These decisions were not concerned with generative AI, but their underlying reasoning remains relevant. They demonstrate that the court’s concern is not technology for its own sake, it is the reliability and authenticity of the information presented through technology. 

The most direct contemporary development is Pooja Ramesh Singh v Jammu and Kashmir Bank Ltd. The Supreme Court noted that the tribunal had relied upon non-existent, fake and hallucinated material generated through AI. The Court set aside the affected decisions and emphasized the importance of human control over adjudication.

The case should not be misunderstood as a judicial rejection of AI. The Court expressly recognized the potential of AI as an aid while simultaneously insisting that judicial decision-making remain under human control.

This approach provides a useful model for AI-generated evidence. The appropriate response to AI is neither unconditional acceptance nor complete rejection. Instead, courts should ask whether the particular AI-related material has been independently verified and whether the process through which it was created or analysed is sufficiently transparent. 

9. CONSTITUTIONAL AND PROCEDURAL CONCERNS 

AI-related evidence also raises constitutional and procedural concerns. 

9.1 Fair Trial 

In criminal proceedings, unreliable evidence may directly affect the liberty of an accused person. If an AI-generated image, recording or document is falsely attributed to an accused, the consequences can be severe. 

The right to a fair procedure therefore requires meaningful opportunity to challenge the authenticity and reliability of AI-related evidence. The defence should be able to examine the original material, challenge the methodology and, where appropriate, seek independent expert analysis. 

9.2 Article 21 and Procedural Fairness 

Article 21 of the Constitution protects life and personal liberty and has been interpreted to require fairness in legal procedure. Evidence produced through opaque technological processes should not be allowed to undermine basic procedural fairness. 

A technologically sophisticated system cannot be treated as a substitute for due process. The more consequential the evidence, the stronger the justification for transparency and independent verification. 

9.3 Privacy 

AI systems may process large amounts of personal information. Digital evidence can include photographs, communications, location information, biometric information and other sensitive data. 

Consequently, investigators and courts should consider whether the collection and processing of AI-related evidence is lawful and proportionate. The evidentiary value of material should not automatically eliminate privacy considerations. 

9.4 Right of Confrontation and Challenge 

Where expert or AI-assisted conclusions substantially influence a case, the opposing party should have a meaningful opportunity to challenge the basis of those conclusions. This include access, subject to lawful restrictions, to relevant underlying material and methodology. 

10. COMPARATIVE PERSPECTIVE 

The challenge presented by AI-generated evidence is not unique to India. Other legal systems are similarly attempting to reconcile technological innovation with evidentiary reliability.  

The broader international approach increasingly emphasizes transparency, accountability, risk management and human oversight in the deployment of AI. The European regulatory framework, for example, adopts a risk-based approach to artificial intelligence and imposes stronger obligations in high-risk contexts. The United Kingdom has generally pursued a more principles-based and sector-specific approach rather than creating one comprehensive evidence statutes specifically for AI. 

For India, the most useful lesson is not to copy another jurisdiction’s legislation mechanically. Instead, India should develop a framework suitable for its own evidence system. 

Three principles are particularly relevant.

First, provenance should be prioritized. Courts must know where digital material originated and what happened to it. 

Second, transparency should be encouraged. Parties relying upon AI-generated or AI-processed evidence should disclose material information about the AI system and processing methodology where necessary to challenge reliability.

Third, human oversight should remain mandatory for consequential judicial decisions. 

A comparative approach therefore supports a principle-based model rather than complete dependence upon technological certification.

11. CRITICAL ANALYSIS AND RECOMMENDATIONS 

The BSA provides a useful foundation but does not completely resolve the challenges created by generative AI. The principal gap is that the statute regulates electronic records generally but does not expressly differentiate between an authentic digital record and content generated artificially through an AI system. 

The following reforms would strengthen the Indian framework. 

11.1 Mandatory Disclosure of AI Involvement

Where AI has materially contributed to the creation, alteration or analysis of evidence, the party relying on it should disclose that fact. Concealing material AI involvement can make meaningful challenge difficult. 

11.2 Preservation of Original Evidence 

Investigators should preserve the original file wherever possible and create forensic copies for analysis. AI-enhanced or processed versions should not replace the original. 

11.3 Standardised Forensic Protocols 

India should develop standard protocols for AI-related digital evidence covering acquisition, preservation, metadata, hashing, examination, reporting and courtroom presentation. 

11.4 Independent Expert Verification

High-risk AI-generated material should be independently examined by a qualified digital forensic expert, particularly where it is central to a criminal prosecution or defence. 

11.5 Explainability 

Where an AI system materially contributes to an evidentiary conclusion, the court should be given sufficient information to understand the system’s function, limitations and error possibilities. Absolute disclosure of proprietary source code may not always be necessary, but meaningful procedural transparency is essential. 

11.6 Multiple Authentication Methods

Courts should avoid treating one AI-detection score as conclusive. Authentication should ideally combine source verification, metadata examination, forensic analysis, contextual evidence and expert opinion. 

11.7 Judicial Training 

Judges, prosecutors, defence lawyers and investigators require specialized training in AI-generated content, synthetic media, digital forensics and algorithmic limitations. 

11.8 Clear Distinction between Admissibility and Weight 

The court should expressly distinguish: 

• whether evidence is legally admissible; 
• whether it is authentic; 
• whether it is reliable; and 
• what evidentiary weight should be assigned to it. 

This distinction would prevent formal compliance from being mistaken for factual truth. 

11.9 Human-in-the-Loop Principle 

AI should assist judicial processes but should not become an autonomous decision-maker. The Supreme Court’s recent approach provides strong support for retaining meaningful human control.

11.10 Periodic Legislative Review 

Technology changes more rapidly than legislation. The evidentiary framework should therefore be reviewed periodically rather than assuming that a single statutory amendment will permanently resolve AI-related challenges. 

12. CONCLUSION 

Artificial Intelligence is transforming the nature of digital information and consequently challenging traditional assumptions underlying evidence law. The principal difficulty is not that AI-generated material is electronic. Indian law already recognizes electronic and digital records. The difficulty is determining whether the information presented to the court is authentic, reliable and sufficiently connected to a real-world source. 

The Bharatiya Sakshya Adhiniyam, 2023 provides an important foundation through sections 39 and 61-63. The provisions recognize electronic records and provide mechanisms for their proof and expert examination. The Supreme Court’s decisions in Anvar P.V. and Arjun Panditrao establish important principles concerning the authenticity and procedural treatment of electronic evidence. These principles remain relevant in the age of generative AI. 

At the same time, AI-generated material creates risks that ordinary electronic evidence rules were not specifically designed to address. Synthetic images, audio and video can create convincing but entirely artificial representations. AI-generated text can contain fabricated information. Automated forensic systems can produce conclusions that may be difficult for non-specialists to evaluate. 

The appropriate legal response is therefore neither complete rejection of AI nor unconditional reliance upon it. Courts should adopt a layered approach based on provenance, preservation, authentication, expert examination, transparency and human oversight. 

The recent Supreme Court decision in Pooja Ramesh Singh v Jammu and Kashmir Bank Ltd is particularly significant because it demonstrates that technological assistance must remain subordinate to independent verification and human adjudicatory responsibility. AI can improve efficiency, assist analysis and support the justice system, but it cannot itself establish truth. 

India should therefore move towards a specialized evidentiary framework for AI-related material while preserving the basic principles of relevance, admissibility, authenticity and fair trial. The ultimate objective should not be to make courts resistant to technology, but to ensure that technological advancement strengthens rather than weakens the reliability and fairness of judicial decision-making. 

BIBLIOGRAPHY 

PRIMARY SOURCES 

Legislation 

• Bharatiya Sakshya Adhiniyam 2023.
• Information Technology Act 2000. 
• Constitution of India 1950.

Cases

• Anvar P.V. v P.K. Basheer (2014) 10 SCC 473. 
• Arjun Panditrao Khotkar v Kailash Kushanrao Gorantyal (2020) 7 SCC 1.
• Pooja Ramesh Singh v Jammu and Kashmir Bank Ltd 2026 INSC 668. 

SECONDARY SOURCES

• Oxford University, OSCOLA: Oxford University Standard for the Citation of Legal Authorities (4^th edn, Hart Publishing 2012). 
• Standardisation Testing and Quality Certification Directorate, Government of India, ‘Digital Forensics’. 
• Indian Code, ‘The Bharatiya Sakshya Adhiniyam, 2023’.
• Indian Code, ‘The Information Technology Act, 2000’. 

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