1. Introduction

The Deepfake Biomarker Doctrine is an emerging legal and regulatory concept concerning the creation, manipulation, misuse, and circulation of artificially generated or altered biological and health-related indicators through deepfake technologies, artificial intelligence (AI), and synthetic media.

A biomarker is a measurable biological characteristic that indicates a normal biological process, disease condition, pathological process, or response to medical treatment. Examples include:

DNA and genetic markers.

Blood glucose and cholesterol levels.

Cancer biomarkers.

Hormonal indicators.

Brain activity and neurological signals.

Facial, vocal, and behavioural indicators used for health-related predictions.

Deepfake technology can manipulate images, videos, voices, medical records, biological datasets, and AI-generated diagnostic representations to create false impressions about a person's health, genetic identity, disease status, or biological characteristics.

For example, a person could use AI to fabricate a medical report showing that another individual has a genetic disorder, create synthetic brain-scan results suggesting neurological impairment, or manipulate biometric information to falsely establish someone's identity.

The doctrine examines the legal consequences of such conduct through privacy law, medical negligence, consumer protection, evidence law, intellectual property, cybersecurity, constitutional rights, and AI accountability.

Important legal clarification: Deepfake Biomarker Doctrine is not currently a universally recognised standalone legal doctrine. It is a proposed analytical framework developed by combining established legal principles concerning deepfakes, biometric information, genetic privacy, medical evidence, and AI-generated misinformation.

2. Meaning and Scope of the Doctrine

The doctrine rests on five fundamental legal principles.

A. Biological authenticity

Biological information must be genuine, scientifically reliable, and traceable to its actual source.

Where AI-generated material falsely represents an individual's biological condition, questions arise concerning authenticity, misrepresentation, and potential liability.

B. Biomarker integrity

Biomarker information must not be manipulated, fabricated, or presented as scientifically verified without appropriate validation.

For example, fabricating a genetic test showing a person carries a hereditary disease may result in discrimination, emotional injury, employment consequences, or insurance disputes.

C. Informed consent

Individuals should have meaningful control over the collection, processing, modification, and disclosure of their sensitive biological information.

D. Algorithmic accountability

Developers, laboratories, hospitals, platforms, and organisations using AI-generated biological information may have legal responsibilities depending on their involvement, representations, negligence, and applicable statutory duties.

E. Legal evidentiary reliability

Synthetic biological evidence should not automatically be accepted as genuine medical or scientific evidence. Its provenance, methodology, integrity, and reliability must be independently examined.

3. Major Forms of Deepfake Biomarker Misuse

A. Genetic biomarker deepfakes

AI may generate or modify genetic reports, DNA profiles, or genomic datasets to falsely attribute hereditary conditions to an individual.

Potential legal consequences include:

Genetic privacy violations.

Medical fraud.

Employment discrimination.

Insurance-related misrepresentation.

Defamation and reputational injury.

Fraudulent scientific research.

B. Medical biomarker manipulation

Deepfake technology can fabricate laboratory reports, blood-test results, diagnostic images, and medical certificates.

For example, an individual might digitally alter a cancer biomarker report to show that a healthy person has a malignant condition.

Depending on the circumstances, this may constitute forgery, cheating, fraud, professional misconduct, or medical negligence.

C. Neurobiomarker deepfakes

AI-generated brain scans, EEG recordings, and neurological indicators may falsely suggest that someone has a mental disorder, cognitive impairment, or neurological abnormality.

This raises serious questions concerning:

Mental privacy.

Human dignity.

Freedom from involuntary medical testing.

Reliability of neurological evidence.

Discrimination based on alleged cognitive characteristics.

D. Facial and vocal biomarker manipulation

Deepfake systems can manipulate facial expressions, voice patterns, and behavioural indicators to create false health-related conclusions.

For instance, an AI-generated voice recording might be presented as evidence that someone has a neurological speech disorder.

The legal issue is not merely identity impersonation: it is the fraudulent attribution of a biological or medical characteristic.

E. Synthetic biomarker evidence

Synthetic biological datasets may be introduced into scientific research, clinical trials, medical litigation, or regulatory submissions.

If fabricated information is represented as authentic patient data, it can undermine scientific validity and potentially expose patients to unsafe treatment decisions.

4. Constitutional and Human Rights Foundations

A. Right to privacy

In India, Article 21 of the Constitution protects personal liberty and privacy.

In Justice K.S. Puttaswamy (Retd.) v. Union of India (2017), the Supreme Court recognised privacy as a fundamental right.

The judgment established important principles concerning informational privacy, personal autonomy, dignity, and control over personal information.

Applied to deepfake biomarkers, these principles support protection against unauthorised collection, manipulation, and disclosure of genetic, medical, facial, and neurological information.

B. Right to bodily autonomy

In Selvi v. State of Karnataka (2010), the Supreme Court examined the involuntary administration of narco-analysis, polygraph examinations, and brain electrical activation profile tests.

The Court held that involuntary administration of these techniques violated constitutional protections under Articles 20(3) and 21.

The judgment is particularly relevant to neurobiomarker deepfakes because it recognises the constitutional importance of mental privacy, personal autonomy, and protection against compelled testimonial techniques.

C. Right to dignity and identity

Deepfake biomarker manipulation may damage an individual's dignity by falsely representing their biological identity.

It can create false impressions concerning genetic inheritance, fertility, disease, cognitive capacity, or physical health.

Articles 14 and 21 may become relevant where such information is used by public authorities or produces discriminatory consequences.

5. Important Case Laws

The following cases provide the strongest existing judicial foundations for analysing this proposed doctrine. They do not directly establish a standalone Deepfake Biomarker Doctrine.

1. Justice K.S. Puttaswamy v. Union of India (2017)

Supreme Court of India

Citation: (2017) 10 SCC 1.

Legal principle: Informational privacy is protected under Article 21.

The Supreme Court recognised privacy as an essential component of personal liberty and dignity.

Relevance:

Suppose a biotechnology company collects an individual's genetic information and uses it to train an AI model that generates false medical profiles.

The Puttaswamy principles provide a constitutional foundation for challenging intrusive and unauthorised processing of personal biological information.

2. Selvi v. State of Karnataka (2010)

Supreme Court of India

Citation: (2010) 7 SCC 263.

Legal principle: Protection against involuntary testimonial techniques and invasion of mental privacy.

The Supreme Court considered the constitutional implications of narco-analysis, polygraph tests, and brain electrical activation profile tests.

Relevance:

AI-generated neurological evidence should not be treated as an unquestionable representation of a person's thoughts, memories, or mental condition.

The judgment supports constitutional scrutiny of technologies that interfere with mental privacy and individual autonomy.

3. Shreya Singhal v. Union of India (2015)

Citation: (2015) 5 SCC 1.

Legal principle: Freedom of speech and expression and constitutional limits on online content regulation.

The Supreme Court invalidated Section 66A of the Information Technology Act, 2000.

Relevance:

Deepfake biomarker content may involve defamatory or deceptive speech. However, restrictions on its circulation must comply with constitutional standards.

The judgment is important when considering intermediary liability, online content restrictions, and the balance between individual protection and freedom of expression.

4. Anil Kapoor v. Simply Life India & Ors. (2023)

Court: Delhi High Court.

Legal principle: Protection of personality rights against unauthorised exploitation of identity.

The litigation concerned the unauthorised commercial exploitation and misuse of the actor's name, image, likeness, and other personality attributes.

The Delhi High Court granted protective relief against specified forms of misuse.

Relevance:

If AI systems use a person's face or voice to create false medical endorsements, fabricated diagnostic statements, or synthetic representations of biological conditions, personality-right principles may become relevant.

5. Arijit Singh v. Codible Ventures LLP & Ors. (2024)

Court: Bombay High Court.

Legal principle: Protection of personality rights, including distinctive voice identity, against unauthorised AI exploitation.

The litigation concerned AI-generated imitation and commercial exploitation of the singer's distinctive voice and identity.

Relevance:

Voice is a biometric characteristic. If a synthetic voice is used to fabricate a medical diagnosis, consent statement, or clinical-trial recording, the same identity-protection principles may assist in determining liability.

These decisions are relevant analogies, not judgments specifically about fabricated biomarkers.

6. Arijit Singh v. Codible Ventures LLP (2024)

Bombay High Court

Date: 26 July 2024.

Legal principle: Protection of voice, vocal style, personality, and identity against unauthorised AI-generated exploitation.

The singer challenged AI platforms and other entities allegedly using his recordings and personality traits to create synthetic versions of his voice.

The Court granted an ex parte ad-interim injunction against specified defendants concerning unauthorised use of his personality attributes, including AI voice models, voice-conversion tools, deepfakes, and face morphing.

Indian Kanoon

Relevance:

This case is particularly important for biometric protection.

A person's voice can be used to generate false medical statements, clinical consent recordings, or fabricated neurological assessments.

The judgment provides a useful legal foundation for protecting identifiable biological and behavioural characteristics from AI misuse.

7. Daubert v. Merrell Dow Pharmaceuticals, Inc. (1993)

United States Supreme Court

Citation: 509 U.S. 579 (1993).

Legal principle: Scientific reliability and admissibility of expert evidence.

The US Supreme Court explained that judges must examine whether scientific expert testimony rests on reliable methodology and is relevant to the issue before the court.

Important considerations include:

Whether the scientific technique can be tested.

Whether it has undergone peer review and publication.

Its known or potential error rate.

The existence of standards controlling its operation.

Its acceptance within the relevant scientific community.

Relevance:

Suppose a prosecutor introduces an AI-generated DNA analysis or synthetic neurological report as evidence.

Under the Daubert approach, the scientific foundation and reliability of the methodology must be scrutinised.

Daubert is a US evidentiary precedent, not a binding rule of Indian evidence law.

LII / Legal Information Institute

8. Frye v. United States (1923)

Citation: 293 F. 1013 (D.C. Cir. 1923).

Legal principle: General acceptance of scientific techniques.

The case introduced the general-acceptance approach to scientific evidence in its jurisdiction.

Relevance:

An AI-generated biomarker assessment should not be treated as scientifically established merely because it is produced by sophisticated software.

Scientific acceptance, validation, reproducibility, and error rates are important considerations.

In the United States federal courts, Daubert and Federal Rule of Evidence 702 govern rather than Frye's general-acceptance test alone.

6. Indian Statutory Framework

The proposed doctrine can be implemented through several existing Indian laws.

A. Information Technology Act, 2000

The following provisions may apply depending on the facts:

For example, using another person's biometric identity to obtain medical benefits through fraudulent impersonation may attract Section 66D if its statutory requirements are fulfilled.

Not every deepfake constitutes an offence under these provisions. The ingredients of the particular offence must be established.

B. Digital Personal Data Protection Act, 2023

The DPDP Act provides a framework for processing digital personal data.

Relevant issues include:

Lawful processing of personal data.

Consent and permitted legitimate uses.

Responsibilities of data fiduciaries.

Security safeguards.

Personal data breach obligations.

Individual rights concerning personal data.

Genetic information, facial images, and medical records may constitute personal data when they relate to an identifiable individual and fall within the Act's scope.

An important distinction is that the DPDP Act does not create a separate statutory category called sensitive personal data in the same manner as some other privacy regimes.

Implementation position as of 5 November 2026: The DPDP Rules were notified in November 2025, but implementation is phased. The main provisions governing ordinary processing, consent, data-principal rights, and associated fiduciary obligations are scheduled to commence 18 months after the 13 November 2025 notification, on 13 May 2027. Certain provisions have earlier commencement dates. Therefore, the entire Act should not be treated as already operational.

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C. Bharatiya Nyaya Sanhita, 2023

The following provisions may be relevant:

Section 318: Cheating.

Section 319: Cheating by personation.

Section 336: Forgery.

Section 340: Forged document or electronic record and its use as genuine.

Section 356: Defamation.

For instance, fabricating an electronic medical certificate and knowingly using it to deceive an employer or insurance company may attract applicable cheating or forgery provisions.

The precise charge depends on the intention, conduct, and statutory ingredients established.

D. Bharatiya Sakshya Adhiniyam, 2023

Two provisions are particularly important.

Section 39 – Opinions of experts

Expert opinions concerning scientific questions and digital evidence are relevant.

This allows courts to consider the opinions of appropriately qualified forensic scientists, medical experts, geneticists, and electronic-evidence examiners.

Section 63 – Admissibility of electronic records

This provision governs the admissibility of electronic records subject to statutory requirements, including applicable certification requirements.

Consequently, an AI-generated biomarker report cannot become reliable evidence merely because it exists in digital form. Its authenticity, origin, integrity, and scientific reliability must also be examined.

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7. Civil Liability and Institutional Accountability

The doctrine raises questions about the responsibility of different participants in the biotechnology and AI ecosystem.

A. Liability of AI developers

AI developers may face liability where their conduct satisfies applicable legal requirements, including:

Negligent development or deployment.

Knowing facilitation of unlawful impersonation.

Misrepresentation of model capabilities.

Failure to comply with applicable legal duties.

Unauthorised use of protected personal or proprietary material.

A developer is not automatically liable for every misuse of a general-purpose AI system. Knowledge, control, foreseeability, contractual obligations, and applicable statutory provisions matter.

B. Liability of hospitals and laboratories

Hospitals and laboratories may face liability if they:

Issue fabricated biomarker reports.

Fail to verify suspicious diagnostic information.

Improperly disclose patient records.

Use manipulated datasets in clinical decisions.

Misrepresent AI-generated diagnostic conclusions as laboratory-confirmed results.

Professional negligence, consumer protection, contractual liability, and applicable healthcare regulations may become relevant.

C. Liability of social media platforms

Platforms may face legal obligations concerning complaints about impersonation, unlawful content, and manipulated personal information.

Section 79 of the IT Act and the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, as amended, are relevant to intermediary responsibilities.

However, intermediary liability depends on the applicable statutory conditions, the platform's role, and compliance with due-diligence requirements.

D. Employer and insurance company liability

Employers and insurers may create legal risks if they rely on fabricated genetic or medical information when making decisions.

For example, rejecting an employee's promotion because of an AI-generated report falsely suggesting hereditary illness could raise issues of discrimination, privacy, contractual obligations, and reputational injury.

8. Regulatory and Judicial Remedies

A victim of deepfake biomarker manipulation may consider several remedies depending on the circumstances.

In serious cases, a victim may also seek preservation of digital evidence, identification of the responsible parties, and independent verification of the original biological records.

9. Proposed Deepfake Biomarker Accountability Framework

A comprehensive legal framework should establish the following safeguards.

1. Authentication requirement

Every medically significant synthetic biomarker report should identify its source, generation method, and verification status.

2. Consent and authorisation

Biological and biometric data should not be used for unauthorised identity imitation or deceptive medical representations.

3. Scientific validation

AI-generated biomarker interpretations should undergo appropriate independent validation before clinical or legal reliance.

4. Traceability and auditability

Laboratories and AI developers should maintain appropriate records of data provenance, model versions, alterations, and access.

5. Disclosure of synthetic content

Artificially generated medical or biological representations should be clearly identified as synthetic where necessary to prevent deception.

6. Independent investigation

Suspected biomarker manipulation should be investigated through qualified forensic experts and independent medical assessment.

7. Effective remedies

Individuals should have accessible procedures for correction, removal, compensation, and protection against further misuse.

10. Hypothetical Legal Case Studies

The following illustrations demonstrate how the proposed doctrine could operate.

Case Study 1: Fake genetic disease report

A biotechnology company creates an AI-generated DNA report falsely indicating that an employee carries a hereditary cancer mutation. The employer uses the report to terminate the employee.

Potential legal issues include:

Privacy and informational autonomy.

Forgery and cheating, if their statutory ingredients are established.

Defamation.

Employment-related discrimination.

Civil damages and injunctive relief.

Case Study 2: Deepfake neurological evidence

A person creates a synthetic brain scan suggesting that a political opponent suffers from a serious neurological disorder.

The scan is circulated to undermine the individual's reputation.

Potential legal issues include defamation, privacy violations, fraudulent electronic records, and questions concerning evidentiary authenticity.

Case Study 3: Fabricated clinical-trial biomarker data

A research organisation submits AI-generated patient biomarker results as genuine clinical-trial observations.

This may compromise scientific validity and patient safety.

Depending on the facts, consequences may include regulatory investigation, research misconduct proceedings, contractual liability, and professional disciplinary action.

11. International Legal Perspectives

The doctrine also has important comparative-law dimensions.

The European GDPR is particularly relevant because genetic data and biometric data used for uniquely identifying an individual receive special-category protection under Article 9.

The EU AI Act also establishes obligations for specified high-risk AI systems, including certain systems used in healthcare and biometric applications.

These frameworks demonstrate that biological information requires stronger safeguards than ordinary digital content.

12. Critical Legal Challenges

Despite existing legal protections, several unresolved challenges remain.

First, scientific authenticity: Courts may find it difficult to distinguish genuine biological signals from sophisticated AI-generated representations.

Second, attribution: Identifying the original creator of a synthetic biomarker can be difficult when multiple developers, data providers, and platforms are involved.

Third, cross-border jurisdiction: A fabricated genetic report may be generated in one country, stored in another, and circulated internationally.

Fourth, evidentiary uncertainty: Traditional forensic procedures may not adequately address synthetic biological datasets.

Fifth, regulatory fragmentation: Privacy regulators, medical authorities, cybercrime agencies, and scientific institutions may have overlapping responsibilities.

Sixth, freedom of expression: Satire, research simulations, artistic representations, and legitimate scientific modelling must not automatically be treated as unlawful deepfakes.

13. Conclusion

The Deepfake Biomarker Doctrine represents an important proposed development in biotechnology law, medical jurisprudence, privacy protection, and AI accountability.

Its central purpose is to prevent artificial intelligence from being used to manufacture false biological identities, fabricate medical evidence, manipulate genetic information, or create deceptive representations of human health.

Indian constitutional jurisprudence, particularly Justice K.S. Puttaswamy and Selvi, provides important foundations for protecting informational privacy, mental autonomy, and human dignity.

Similarly, Anil Kapoor v. Simply Life India and Arijit Singh v. Codible Ventures LLP demonstrate how Indian courts are addressing the unauthorised AI exploitation of personal identity and voice.

However, a complete legal doctrine requires more than personality rights and privacy protections. It must also establish scientific authentication standards, forensic verification procedures, institutional accountability, and effective compensation mechanisms.

The fundamental proposition is that artificially generated biological information must never be represented as authentic human biological evidence without appropriate verification.

This principle can form the foundation of a future specialised legal framework governing deepfake biomarkers.

14. Important Case Law Revision Table

Further primary legal reading:

Supreme Court judgment – Puttaswamy (2017).

Supreme Court judgment – Selvi (2010).

Delhi High Court – Anil Kapoor (2023).

Bombay High Court – Arijit Singh (2024).

Bharatiya Sakshya Adhiniyam – Section 39.

Bharatiya Sakshya Adhiniyam – Section 63.

Academic note: The Deepfake Biomarker Doctrine is a proposed interdisciplinary legal framework. The cases discussed establish related legal principles; none has yet recognised this exact doctrine as an independent cause of action.