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The Future of Biometric Data Protection: Securing Data Privacy

Biometric technology is quickly emerging as one of the most used authentication methods and is widely used across the world today. While the benefits of biometric technology are numerous, it also presents a new set of risks that must be accounted for. With cybersecurity threats on the rise, the future of biometric data protection lies in how organizations and users manage the use of biometric data safely and effectively.

This article explores the specifics of biometric data protection, upcoming technological advancements in the field, what the future holds, and how evolving landscapes affects its use and security.

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How does biometric data protection work?

Biometric data protection refers to the strategies, technologies, and processes used to secure biometric information from unauthorized access, theft, or misuse. Biometric data or information can refer to any personally identifiable, biological characteristics unique to each person, including:

  • Fingerprint scanning
  • Facial recognition technology
  • Palm print recognition
  • Retinal or iris scans
  • Voice recognition
  • DNA matching
  • Hand geometry (size and placement of fingers and hand)

Biometric data is largely used as a more secure access method of identity verification over other methods such as password protection because the data stored is encrypted, meaning it is harder to steal or fake and is far more convenient since its data is unique to each user. With much of the world owning some type of smartphone today, identity verification can be completed using facial or fingerprint scanning technology through our own devices or third-party apps.

As a subset of data privacy, biometric data is unique in its immutability and also significant security implications should biometric databases become compromised. Effective biometric data protection mechanisms must ensure that this sensitive information is collected, stored, and processed while safeguarding individual privacy rights and in compliance with relevant legal frameworks.

What are the risks of using biometric data?

Biometric data offers many advantages in its security and convenience, allowing for seamless user authentication processes that are nearly impossible to replicate or fake. However, its use comes with inherent risks, mainly surrounding its privacy concerns, potential misuse, and the increased impact in the event of a breach or compromise.

Some of the main concerns with the adoption of biometric technology surround data privacy and how biometric data is safely and properly stored and managed. Personal data stored in biometric databases are highly coded and identifiable to each individual and if any of the databases were breached, it could be far more detrimental to users than if a password were stolen.

Of course, there are always risks when new data types are being stored. It’s important to stick to the basic data security principles and allow teams and organizations to adjust protocols to adapt to changing technologies. Below are seven key principles of biometric data security compliance that businesses should attempt to follow:

7 Key Characteristics of Biometric Security Compliance

Biometric data usage is defined by seven key pillars of security:

  1. Consent and transparency: Individuals must be informed about the collection and use of their biometric data, with explicit consent obtained.
  2. Data minimization: Only the necessary amount of biometric data should be collected, limiting the scope for misuse.
  3. Storage limitation: Biometric data should be stored only as long as necessary, with secure deletion practices in place.
  4. Accuracy and quality: Ensuring the integrity and accuracy of biometric data to prevent misidentification and errors.
  5. Security measures: Implementing adequte cybersecurity measures and training to protect biometric data from unauthorized access and security breaches.
  6. Compliance: Adhering to all relevant regulations governing biometric data, including GDPR, CCPA, and more, ensuring legal and ethical use.
  7. Scope: The use of biometric data must be defined and limited to its exact, legitimate purposes. Expectations and limits must be set to avoid misuse of data.

How will AI impact biometric data protection in the future?

Artificial Intelligence (AI) and machine learning (ML) stand at the forefront of transforming biometric data protection. AI-driven technologies can enhance the accuracy and security of biometric systems through adaptive algorithms that learn and evolve to detect and fend off sophisticated cyber threats.

Additionally, AI can streamline compliance with privacy regulations by automating common biometric data protection strategies, such as data minimization, consent management, and breach detection processes. However, the integration of AI also necessitates additional protections against AI-specific threats, such as deepfakes and algorithmic bias, ensuring a balanced approach to innovation and privacy.

Some key areas that AI and ML are expected to impact biometric technology and data usage in the future are:

  • Improved accuracy and reliability - As of now (in 2024), one of the short-term drawbacks of biometric technology is that it is still susceptible to errors or false positives. While this may eventually be eliminated as technology continues to evolve, AI tools can bridge this gap by providing enhanced biometric detection abilities to speed up this process.
  • Enhanced security measures - AI can assist in the encryption process and store data safely to avoid any potential cyber attacks. Using advanced encryption methods, AI can ensure that data is safe, whether it is at rest or in transit. Additionally, AI can also optimize the management of encryption keys, which allows for the overall improvement of biometric data protection.
  • Continuous authentication systems- AI technology can perform dynamic, real-time assessments for any potential unusual activity and adapt authentication processes based on a perceived threat level. If unusual activity is detected, the system can request additional verification methods to ensure the user is authorized for entry. Furthermore, users are subject to “biometric” checkpoints, which the AI systems can continually monitor behavioral patterns for any anomalies.

How will biometric technology evolve in the future?

The future of biometric technology will be marked by various innovations aimed at increasing security, enhancing the user experience, and expanding applicability. Primarily, biometric technology will be used in two ways: preventing unauthorized access and protecting user data.

Advancements such as multi-modal biometric systems, which combine several biometric identifiers to increase accuracy and security, are on the rise. Additionally, there are already plans for the development of contactless biometrics, which has the potential to redefine the accessibility of biometric solutions. As these technologies evolve, so too will the applications of biometrics.

In many ways, biometric technology is already making its way through modern society and through all industries, in the following use cases:


  • Using biometric authentication to verify patient identities and secure EHRs (electronic health records) or other medical records
  • Use of AI with biometric data to gauge patient responses and assist with diagnostics
  • Speed up patient treatment with more efficient biometric processes, such as improved fingerprint recognition or facial recognition systems

Financial services

  • Provide additional methods for online multi-factor authentication (MFA)
  • Use of facial recognition for banking or ATM services
  • Using biometrics to make independent financial transactions, whether sending or receiving

Critical infrastructure (CI)

  • Many CI facilities already implement biometric security to limit entry to their physical sites
  • CI organizations also use biometric security to control access to highly sensitive data


  • Many facets of travel already use biometrics in part of their operations, such as facial recognition for security lines or live facial scanning for faster immigration
  • Incorporating biometrics into ID cards or passports for faster biometric detection
  • Customers can use their mobile devices to check-in to hotels or car rentals through biometric verification

Law enforcement

  • Combined with AI technology, some law enforcement agencies around the world have been using biometric scanning to detect possible criminals

What regulations currently govern biometric data protection?

The regulatory landscape for biometric data protection currently lies in a mix of international, national, and state laws. Although it is not a main area of focus (such as AI regulation), many cyber laws have subsections related to biometric data security.

Key frameworks like the EU General Data Protection Regulation (GDPR) set strict guidelines for biometric data processing, while other regulations like the Illinois Biometric Information Privacy Act (BIPA) in the United States make up one of the first laws to specifically address biometric privacy.

In the recently passed EU Artificial Intelligence Act (EU AI Act), part of its broader strategy is to regulate biometric use within the context of artificial intelligence. The use of AI in biometrics includes automated facial recognition or immediate analysis of user behaviors using biometrics. However, the EU AI Act attempts to set limits on the use of biometrics with AI systems to protect individual privacy and freedom of rights.

Within the next decade, users and businesses can expect more biometric technology regulation to pass as data privacy becomes a major topic of discussion amidst rapid technological innovation. It’s even more important to get ahead of the technology as development continues to ensure that rules are established to prevent abuse and exploitation of users.

How can businesses improve their biometric data protection?

Enhancing biometric data protection is a multifaceted endeavor that requires businesses to adopt a proactive, comprehensive approach to cybersecurity and privacy. Key strategies include:

  • Implementing strong encryption: Ensuring biometric data is encrypted at all levels, both at rest and in transit to prevent unauthorized access.
  • Regular security audits: Conducting frequent audits and assessments to identify vulnerabilities and address them promptly.
  • Employee training: All staff who are charged with handling, processing, or storing biometric data must be trained on best practices and how to properly manage those assets.
  • Privacy by design: Integrating privacy considerations into the development and deployment of biometric systems, ensuring they are secure by default.
  • Compliance vigilance: Staying informed about new laws and maintaining compliance with existing regulations governing biometric data to mitigate legal risks and build trust.

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