AI agents bolstering security for biometric authentication

By Anshu Raj, Director of Operations at Chetu

While biometric authentication has gone mainstream across countless industries, we have seen recent technological advancements guaranteeing even more widespread usage in the decades to come.

The global biometric market, valued at $47.8 billion in 2023, is expected to expand at a CAGR of approximately 12 percent through 2028. Security is more critical now than ever because of increasing cyberattacks and fraudsters. Many industries, such as banking, healthcare, and government, contain sensitive information vulnerable to complex cyber threats, so implementing new technologies is a key method to defeat the constant onslaught.

In the past several years, many organizations have adopted artificial intelligence (AI) to power biometric authentication platforms; today, they see agentic AI creating next-gen security systems because it offers advanced features and continuous learning capabilities. In an era where security threats are becoming more sophisticated, keeping ahead of criminals using the most advanced technology is imperative.

From AI to AI agents

AI agents are a giant step forward compared to traditional AI applications. Because of their autonomous nature, intelligent agents make decisions without human intervention. Instead, they constantly learn from the latest biometric data, adapting their decision-making based on physical and behavioral traits, some of which may change over time.

Although AI reacts in “real time,” AI agents react faster, enabling them to identify patterns and anomalies, anticipate potential data breaches, and deliver a secure authentication process. This process can leverage multiple avenues — facial, fingerprint, and voice patterns — when authentication is crucial. This technology can more accurately monitor user behavior to flag unusual activity.

Key features of AI agents in biometric authentication

Agentic AI-driven algorithms analyze biometric data more effectively, leading to improved recognition rates. Deep learning and reinforcement learning algorithms identify patterns in large datasets to distinguish and determine anomalies like impostors. Since intelligent agents respond to data in real-time, the authentication process speeds up, leading to higher user satisfaction and increased adoption of multimodal authentication systems.

The 2025 State of Security and Identity report reveals that industry leaders believe that the benefits of AI agents include improved efficiency and speed of security processes (50 percent) and enhanced real-time data analysis capabilities (47 percent). Its flexibility allows for the most accurate assessment of data. By minimizing human intervention, these agents reduce the likelihood of errors that can occur during manual verification.

AI addresses biometric challenges

Artificial Intelligence has been massively transformative across industries. In the case of biometric authentication, agentic AI, an emerging technology, addresses many issues that biometric authentication has had with security, privacy concerns, and scalability.

Enhanced protection

With real-time continuous monitoring and adaptive learning, AI agents can spot discrepancies and detect fraud. AI-powered liveness detection analyzes micro-movements, skin texture, blood flow, and blinking to ensure authenticity. This immediate identification enables organizations to take corrective actions to prevent potential fraud, minimize financial losses, and protect the integrity of their organization.

Privacy preservation

While AI-powered authentication significantly boosts protection, privacy issues are still problematic. For example, traditional biometric programs store sensitive user data centrally, which remains a major vulnerability.

In the last 20 years, nearly one-fifth of reported cyber incidents have affected the worldwide financial industry, causing $12 billion in direct losses to financial firms; the average data breach costs the financial sector in 2024 is $4.88 million.  New biometric authentication models like federated learning have emerged to mitigate these risks, reducing reliance on centralized data storage.

Traditional authentication systems centralize biometric data, creating potential vulnerabilities where large-scale breaches could expose sensitive information. Federated learning, a machine learning method that uses decentralized data, enables AI agents to train autonomous systems without transferring raw biometric data to centralized servers. Instead, they are trained locally on user devices, with only encryption model updates shared with central servers. Deepak Gupta, a recognized cybersecurity expert, cites a 2023 paper in the Journal of Cybersecurity that federated learning-based authentication systems could reduce false acceptance rates by 40 percent compared to traditional centralized models.

Scalability and interoperability

Since AI agents are modular solutions, they are highly scalable for large-scale biometric authentication systems. This technology dynamically adapts to growing demands, ensuring efficient processing without sacrificing accuracy. Their modular nature allows for seamless platform integration, enabling interoperability between biometric systems, devices, and security infrastructures.

Airports are obvious examples of mass-scale biometric scanning. The agentic AI system can scale its operation to handle large numbers of individuals, reducing airport resource allocation and improving traveler satisfaction.

In an article from Tech-Informed, Matt Gilkeson, the TSA’s CTO, said agentic AI can play a significant role in airport security: “Agentic AI could help us handle complex scenarios quickly and efficiently and automatically start escalating the incident to local law enforcement or our coordination center.”

Best practices and implementation strategies

AI Agents attract businesses looking to fortify their security and authentication methods. However, obtaining and implementing autonomous systems requires careful planning and expert-driven support. Keep these best practices in mind:

• Prioritize privacy-preserving AI models: Implement federated learning, use differential privacy techniques, and ensure compliance with GDPR, CCPA, and other privacy regulations.
• Enhance protection with multi-layered AI defense: Combine multiple biometric factors to increase authentication accuracy and deploy continuous authentication.
• Optimize AI training with real-world data: Adaptive AI models must train on diverse datasets.
• Ensure interoperability and scalability: Enable on-device biometric processing for mobile and IoT applications to enhance performance and privacy.

Keeping these best practices will help guide your implementation plan and strategy. Businesses should assess their needs, choose the right AI model, develop and test prototypes, deploy and monitor in phases, and implement continuous improvement. Businesses can opt for a third-party platform, build their own proprietary system, or take a hybrid approach. An experienced customer software development partner can customize or build their systems, ensuring seamless integration and implementation. Just make sure you find a provider with the skill and industry experience that aligns with AI agents and biometric authentication.

About the author

Anshu Raj is the Director of Operations at Chetu, where he leads the Artificial Intelligence and Machine Learning team. His prior decade of experience as a Technical Project Manager includes work in the supply chain and energy sectors. Anshu holds certifications in PMP, Agile, and NetSuite Foundation.

Article Topics

AI  |  AI agents  |  biometric authentication  |  biometrics  |  Chetu  |  continuous authentication  |  cybersecurity  |  interoperability