Behavioral Biometrics Governance — Detailed Explanation with Case Laws

Jurisdiction: India, with comparative EU/UK principles

1. Meaning of Behavioral Biometrics

Behavioral biometrics refers to technology that identifies or authenticates a person from patterns in how they interact with a device or service rather than from a conventional physical characteristic.

Examples include:

• typing rhythm and keystroke dynamics;
• mouse movements;
• touchscreen gestures;
• touchscreen pressure and timing;
• device interaction patterns;
• navigation behaviour;
• scrolling patterns;
• login behaviour;
• transaction behaviour;
• voice characteristics in some contexts; and
• patterns used to distinguish genuine users from automated or fraudulent users.

For banking, behavioral biometrics can be used as an additional fraud-detection or authentication signal.

For example:

A customer normally logs in from a recognized device and types/interacts in a particular pattern. A transaction suddenly occurs from a new device with a substantially different interaction pattern. The bank's system may assign the transaction a higher fraud risk score and request additional verification.

The important legal question is not merely whether the technology works. It is:

How should an institution collect, analyse, retain and use behavioral data while respecting privacy, security, transparency and due-process requirements?

2. Behavioral Biometrics vs Physical Biometrics

The distinction is useful.

Behavioral biometrics can therefore be particularly useful for continuous authentication.

Instead of authenticating a user only when they log in, a system can continually assess whether the ongoing interaction appears consistent with the legitimate account holder.

3. Why Governance Is Necessary

Behavioral biometric systems can create several risks.

Privacy risk

A person's interaction patterns can potentially become a persistent identifier.

Accuracy risk

A legitimate user may be incorrectly classified as suspicious.

Discrimination risk

Some users may interact differently because of disability, age, temporary injury, language or technology familiarity.

Surveillance risk

Continuous behavioural monitoring can become excessive if the organization collects more information than necessary.

Security risk

If behavioural templates are compromised, replacing them may be more difficult than replacing a password.

Transparency risk

Users may not understand that their behaviour is being analysed.

Therefore, behavioral biometrics should be governed as a risk-management system, not simply as an IT product.

4. Indian Constitutional Foundation

The most important starting point is Article 21 of the Constitution of India, particularly the constitutional right to privacy.

Justice K.S. Puttaswamy (Retd.) v. Union of India, (2017) 10 SCC 1

This is the leading Indian privacy judgment.

The Supreme Court unanimously recognized privacy as a constitutionally protected right.

The judgment is fundamental to any biometric-governance analysis.

The Court's reasoning establishes that privacy protection is not limited to secrecy. It encompasses important aspects of personal autonomy, dignity and control over personal information.

Relevance to behavioral biometrics

A bank or technology company using behavioral biometrics should therefore consider:

• why the data is being collected;
• whether collection is necessary;
• whether the purpose is clearly defined;
• whether the processing is proportionate;
• what safeguards exist; and
• how long the information is retained.

The fact that a person interacts with a digital service does not automatically eliminate privacy considerations.

5. Puttaswamy and Proportionality

The privacy jurisprudence developed following Puttaswamy places significant importance on legality, legitimate purpose and proportionality.

In practical terms, an organization should be able to explain:

Purpose: Why is behavioral data needed?

Necessity: Is the processing actually required for that purpose?

Proportionality: Is the intrusion proportionate to the security or business objective?

For example, detecting payment fraud may be a legitimate purpose.

But collecting extensive behavioural information unrelated to fraud prevention could raise much more serious proportionality questions.

6. K.S. Puttaswamy (Aadhaar) v. Union of India, (2019) 1 SCC 1

The Supreme Court's Aadhaar judgment is particularly relevant to biometric governance.

The Court examined issues concerning biometric information, authentication, privacy, statutory purpose and proportionality.

Although Aadhaar primarily concerns physical biometrics, its principles are highly relevant to behavioral biometrics.

Key lesson

The legal framework should identify:

• the purpose for which authentication is undertaken;
• the authority for collecting information;
• safeguards against misuse;
• retention arrangements;
• access controls; and
• limits on disclosure.

A behavioral-biometric system should therefore not operate on an assumption that “fraud prevention” gives unlimited authority to collect every available behavioural signal.

7. Digital Personal Data Protection Act, 2023

India's Digital Personal Data Protection Act, 2023 (DPDP Act) provides the principal modern statutory framework for digital personal-data processing.

The exact obligations applicable to a particular behavioral-biometric deployment depend on:

• whether the information constitutes personal data;
• the entity processing it;
• the purpose of processing;
• the applicable consent or other lawful basis;
• whether the organization is a Significant Data Fiduciary;
• applicable rules; and
• the stage of implementation of the statutory framework.

Behavioral data that can be linked to an identifiable individual can fall within the broader concept of personal data.

8. Data Fiduciary and Data Processor

Under the DPDP framework, the organization determining the purpose and means of processing generally operates as the Data Fiduciary, while an entity processing personal data on its behalf may function as a Data Processor.

Consider:

Bank → Behavioral-biometrics vendor

The bank may determine:

“Use behavioral analytics to detect account takeover and payment fraud.”

The vendor processes the relevant data under the bank's instructions.

The contractual relationship should therefore clearly allocate:

• security obligations;
• permitted processing;
• subcontracting;
• retention;
• incident response;
• deletion;
• audit rights; and
• regulatory cooperation.

9. Consent and Notice

Behavioral biometric processing requires careful consideration of transparency.

A good privacy notice should explain, in understandable language:

• that behavioral information is being analysed;
• the purpose;
• categories of information involved;
• relevant user rights;
• security measures;
• retention principles; and
• circumstances in which information may be shared.

A vague statement such as:

“We may collect information about how you use our services”

may not provide meaningful transparency if the actual system continuously analyses detailed interaction patterns.

10. Continuous Authentication

One important application is continuous authentication.

Traditional authentication:

Login → authentication → access

Behavioral biometric authentication:

Login → authentication → continuous behavioural assessment

If the behaviour changes substantially, the system may:

• request additional authentication;
• temporarily restrict a transaction;
• trigger fraud investigation; or
• terminate a session.

This can improve security but also creates a significant governance issue:

What happens when the algorithm is wrong?

11. False Positives

Suppose a bank's system normally observes:

• typing rhythm;
• touchscreen movement;
• transaction sequence; and
• device characteristics.

The legitimate customer is travelling and uses a different device.

The algorithm identifies the activity as suspicious.

If the bank automatically blocks the account, the customer may suffer inconvenience or financial harm.

Governance should therefore include:

• human review where appropriate;
• escalation mechanisms;
• rapid account recovery;
• customer communication;
• fraud investigation procedures; and
• mechanisms to challenge erroneous decisions.

12. Algorithmic Bias

Behavioral models may perform differently across populations.

For example, interaction patterns may differ because of:

• accessibility technologies;
• motor disabilities;
• language settings;
• device types;
• age;
• temporary physical conditions; or
• unfamiliarity with a particular interface.

A governance programme should therefore conduct model validation and fairness testing.

The objective is not to guarantee zero error—an unrealistic standard—but to identify and manage material differential error rates.

13. Data Minimisation

A sound governance framework should collect only what is reasonably necessary.

Suppose a fraud model needs:

• typing intervals;
• transaction timing; and
• device-level signals.

There should be a documented reason for collecting additional information such as unrelated browsing behaviour.

Data minimisation reduces:

• privacy risk;
• cyberattack impact;
• storage costs;
• regulatory exposure; and
• misuse risk.

14. Purpose Limitation

Data collected for fraud detection should not automatically be repurposed for unrelated commercial profiling.

For example:

Behavioral data collected to detect account takeover

should not automatically become:

Behavioral data used to infer a user's personality and sell targeted advertising.

Any additional processing must have an appropriate legal basis and comply with the applicable data-protection framework.

15. Retention

Behavioral biometric information should not automatically be stored indefinitely.

A governance policy should establish:

Collection → active use → retention period → archival/deletion

The retention period should be connected to the legitimate purpose and applicable legal requirements.

Longer retention requires stronger justification.

16. Security of Behavioral Templates

One major advantage of behavioral biometrics is also a potential weakness.

A password can be changed.

A fingerprint cannot easily be replaced.

Behavioral characteristics may change, but a long-term behavioural profile can still become a persistent identifier.

Therefore, organizations should avoid unnecessarily storing raw behavioural information.

Where technically appropriate, they should consider:

• encrypted templates;
• pseudonymisation;
• access controls;
• tokenisation;
• secure storage;
• separation of identifiers from behavioural features;
• restricted administrator access; and
• secure deletion.

17. Banking-Sector Governance

Behavioral biometrics has particular importance in banking because it can detect:

• account takeover;
• credential theft;
• unusual payment behaviour;
• automated attacks;
• mule-account activity;
• anomalous transaction sequences; and
• suspicious device changes.

However, it should normally function as one component of a broader risk-control system.

A bank should not assume:

“Behavioral biometrics detected the customer, therefore the transaction is safe.”

Authentication and transaction authorization are related but distinct questions.

18. RBI Regulatory Perspective

Indian banks and regulated entities must consider RBI requirements concerning:

• cybersecurity;
• digital-payment security;
• customer protection;
• fraud risk management;
• outsourcing;
• information security;
• authentication; and
• technology governance.

The precise RBI requirements depend upon the type of regulated entity and service.

Behavioral biometrics should therefore be integrated into the institution's broader IT governance, cyber-risk management and fraud-risk frameworks rather than treated as a standalone technology.

19. Outsourcing Risk

Suppose:

Bank → Fintech vendor → Behavioral-biometric engine

The bank cannot necessarily eliminate its regulatory responsibilities simply because a third party operates the technology.

The contract should address:

• data ownership/control;
• permitted processing;
• security standards;
• breach notification;
• audit access;
• model changes;
• subcontractors;
• business continuity;
• regulatory inspection;
• data deletion; and
• termination assistance.

20. Important Case: KS Puttaswamy

The constitutional privacy judgment should be regarded as the foundational case.

Its practical message for behavioral biometrics is:

Security objectives matter, but security does not create unlimited authority to collect or process personal information.

The institution should be able to demonstrate a legitimate legal basis and proportionate processing.

21. People's Union for Civil Liberties v. Union of India, (1997) 1 SCC 301

The Supreme Court considered telephone interception and privacy-related safeguards.

Although the case concerns interception rather than biometrics, it is important because the Court recognized the need for procedural safeguards where technologies capable of intruding upon privacy are used.

Relevance

Behavioral monitoring should similarly have:

• defined purposes;
• controlled access;
• procedural safeguards;
• accountability; and
• appropriate oversight.

The more intrusive the monitoring, the stronger the governance framework should be.

22. Selvi v. State of Karnataka, (2010) 7 SCC 263

This Supreme Court judgment dealt with involuntary techniques such as narco-analysis, polygraph tests and brain-mapping.

The case is not directly about behavioral biometrics.

However, it is relevant to the broader principle that personal information and bodily/mental autonomy cannot be treated as unlimited resources for governmental or institutional use.

Relevance

Behavioral biometrics should not be understood as giving institutions unrestricted authority to infer intimate characteristics about individuals.

23. K.S. Puttaswamy v. Union of India and Informational Privacy

The Aadhaar litigation strengthened the legal importance of:

• informational privacy;
• authentication;
• data security;
• purpose limitation; and
• institutional safeguards.

For behavioral biometrics, the central question becomes:

How much behavioural information is genuinely necessary to achieve the stated authentication or fraud-prevention objective?

24. European Union: GDPR Comparison

The GDPR provides an especially important comparative framework.

Under GDPR, biometric data used for uniquely identifying a natural person is treated as a special category of personal data under Article 9, subject to specified exceptions.

Not every piece of behavioral data will automatically be “biometric data” for GDPR purposes. The purpose and technical processing matter.

This distinction is important because organizations sometimes incorrectly assume:

“It is behavioural data, so biometric-data rules do not apply.”

That is not necessarily correct.

25. Schrems II, C-311/18

The CJEU's Schrems II decision dealt with international transfers of personal data.

Relevance

A behavioral-biometric vendor may operate cloud infrastructure outside India.

Where data is transferred internationally, organizations must examine:

• applicable transfer rules;
• contractual protections;
• government-access risks;
• encryption;
• jurisdictional exposure; and
• regulatory requirements.

Therefore, outsourcing behavioral biometrics to an overseas cloud provider requires more than a simple commercial contract.

26. Google Spain v AEPD and Mario Costeja González, C-131/12

The CJEU's landmark right-to-be-forgotten decision emphasized individual interests in controlling certain personal information.

Relevance

Behavioral profiles create persistent records about individuals.

This reinforces the importance of considering:

• retention;
• deletion;
• accuracy;
• lawful purpose; and
• individual rights.

The exact application depends on the applicable Indian legal framework, so EU case law should be treated as comparative rather than directly binding in India.

27. UK Case Law: Bridges v South Wales Police

In R (Bridges) v Chief Constable of South Wales Police [2020] EWCA Civ 1058, the UK Court of Appeal examined automated facial-recognition technology.

Although facial recognition is different from behavioral biometrics, the case is highly instructive for technology governance.

The court considered questions including:

• legal basis;
• proportionality;
• safeguards;
• discretion;
• privacy impact; and
• equality considerations.

Behavioral-biometric lesson

Automated identification technologies require specific and effective safeguards, not merely a general assertion that the technology improves security.

28. Automated Decision-Making

Behavioral biometrics becomes legally more sensitive where it does not merely provide a risk signal but automatically determines a person's rights or access.

For example:

“The algorithm says suspicious → account permanently closed.”

That is substantially more consequential than:

“The algorithm says suspicious → request additional authentication.”

A strong governance architecture therefore uses risk-based escalation.

Low risk

Allow transaction.

Moderate risk

Request additional authentication.

High risk

Temporarily hold transaction and conduct additional verification.

Serious suspected fraud

Escalate to specialist review.

This reduces the consequences of false positives.

29. Governance Framework

A bank or fintech deploying behavioral biometrics should ideally establish:

1. Board oversight

Define responsibility for biometric and behavioural-data risk.

2. Legal assessment

Document the lawful basis and applicable regulatory requirements.

3. Privacy impact assessment

Identify collection, processing, retention and disclosure risks.

4. Security assessment

Test encryption, access controls and attack resistance.

5. Model validation

Measure accuracy, false positives and false negatives.

6. Fairness testing

Assess performance across relevant user groups.

7. Human escalation

Provide review mechanisms for consequential decisions.

8. Vendor governance

Audit third-party providers.

9. Retention controls

Delete or anonymise information when no longer required.

10. Incident response

Establish procedures for breaches and compromised behavioural templates.

30. Example: Banking Fraud Detection

Suppose a customer normally:

• logs in from one device;
• uses a predictable interaction pattern;
• makes small domestic transactions.

Suddenly:

• a new device appears;
• behavioural characteristics change significantly;
• multiple high-value transactions are initiated.

The behavioral system assigns a high risk score.

A well-governed system might:

Step 1: temporarily hold the transaction;

Step 2: request stronger authentication;

Step 3: notify the customer;

Step 4: conduct fraud checks;

Step 5: release or reject the transaction according to established rules.

A poorly governed system might permanently freeze the customer's account solely because of an opaque algorithmic score.

The first model is generally more defensible because it combines automated detection with proportionate intervention.

31. Case-Law Principles in Summary

32. Key Legal Risks

A behavioral-biometric programme can create liability through:

Unlawful collection
Data is collected without an appropriate legal basis.

Insufficient notice
Users do not understand what is being analysed.

Excessive collection
The institution collects substantially more information than necessary.

Security failure
Behavioural profiles are exposed.

Algorithmic error
Legitimate users are incorrectly classified.

Discrimination
The system performs materially worse for particular groups.

Unlawful secondary use
Fraud-detection data is repurposed for unrelated profiling.

Vendor failure
The third-party technology provider mishandles data.

Poor deletion practices
Behavioural records remain indefinitely.

Conclusion

Behavioral biometrics governance is fundamentally a combination of privacy governance, cybersecurity, algorithmic-risk management and financial-sector regulation.

In India, the constitutional foundation is principally the right to privacy recognized in K.S. Puttaswamy, supplemented by the statutory personal-data framework and sector-specific regulatory requirements. The Aadhaar judgment is particularly useful because it demonstrates how courts approach biometric authentication, purpose limitation, safeguards and proportionality.

For banks and fintechs, the safest approach is not to treat behavioral biometrics as an infallible authentication mechanism. It should be governed as a risk signal supported by strong security, transparency, model validation, human escalation, vendor controls and appropriate retention limits.

The central legal principle can be summarized as:

The stronger the institution's security objective, the more important it remains to demonstrate that behavioral-biometric collection and automated decision-making are lawful, necessary, proportionate, secure and appropriately supervised.