Marcus Lauren on battling Deepfakes with Active Thermal tech

It is no secret that our mobile phones and laptops are home to sensitive data, all the way from corporate budgets and colleague details, to personal banking information.

The responsibility to protect this data rests with biometric technology, which is relied upon constantly, dozens of times each day.

As Gen AI and deepfakes become more capable than ever before, the digital security perimeter has altered and modern cybersecurity architecture must adapt promptly to counter these threats. Zero Trust frameworks address this requirement.

John Kindervag, Chief Evangelist at Illumio –who is widely acknowledged as the original creator of the Zero Trust framework – states the core strategy must be to "never trust, always verify".

In this Q&A, Marcus Lauren, Chief Product Officer at NEXT Biometrics, discusses how the company is redefining identity and access management (IAM), including the critical role of physical biometrics in an AI-driven world. 

How does NEXT Biometrics provide a more reliable verification layer for IAM compared to traditional optical or capacitive sensors?

Cyber attacks are increasing in both frequency and impact, affecting hospitals, governments and critical infrastructure. 

As digital identity becomes foundational to financial services and public systems, the reliability of authentication is critical – especially in a Zero Trust model built on “never trust, always verify.” 

Traditional optical fingerprint sensors primarily capture surface level images or simple expressed 2D patterns of a fingerprint. While effective in many scenarios, they can be more vulnerable to spoofing using high quality replicas. 

NEXT Biometrics takes a fundamentally different approach. 

Our Active Thermal technology not only captures the fingerprint patterns in 3D but also detects liveness by measuring the heat transfer, conductivity and tissue characteristics. This makes it significantly more difficult for attackers to use fake fingerprints.

While standards can define frameworks for presentation attack detection, compliance alone is not enough. 

True resilience comes from combining advanced hardware-based sensing with software and algorithmic defenses, creating a layered verification approach.

This directly strengthens IAM in a Zero Trust architecture, where every authentication request must be continuously verified. 

Importantly, this isn’t just our claim, organisations such as the Federal Bureau of Investigation, Aadhaar and multiple national ID programmes have certified NEXT Biometrics for use in highly sensitive applications.

Can you explain how your technology uses Active Thermal technology and 3D imaging to ensure “liveness” and why this hardware-based approach is superior to software-only anti-spoofing measures?

Biometric authentication has evolved beyond simple pattern matching. 

As deepfake and spoofing attacks become more sophisticated, the critical challenge is no longer just recognising a fingerprint but verifying that it comes from a real human being.

NEXT Biometrics addresses this by measuring heat flow and thermal dynamics at the point of contact. 

This enables the sensor to distinguish and differentiate genuine human tissue from artificial materials by detecting how heat is transferred and absorbed.

In addition, our sensors capture depth and structural detail, effectively adding a 3D dimension to the fingerprint. 

This allows the system to analyse not just the surface pattern but also the physical characteristics of the finger, making spoofing significantly more difficult. 

The key advantage of this approach is that liveness detection starts at the hardware level. 

Unlike software-only anti-spoofing, which relies only on analysing the images after capture, our sensors also verify authenticity during the capture process itself. This prevents many attacks from ever entering the system. 

On top of that, software and AI-driven algorithms further analyse the data to confirm both identity and liveness, creating a layered defence model. Hardware establishes trust at the source, and software reinforces it. 

From your perspective as CPO, what are the biggest hurdles for Tier-1 original equipment manufacturers (OEMs) when trying to integrate biometric IAM into laptops and tablets, and how is NEXT making that integration seamless?

From a CPO perspective, the biggest hurdles for Tier-1 OEMs integrating biometric IAM into laptops and tablets are complexity, compliance and time-to-market. 

Integration itself is rarely a quick and simple process, OEMs need to align hardware and software across multiple platforms, ensure compatibility with operating systems like Windows, and meet strict security and biometric standards. 

Plus, delivering a seamless user experience, as well as fast, accurate and reliable authentication, means that the deployment can slow down and increase costs.
That’s why we focus on removing that completely. We combine high-performance hardware with intuitive software, making integration significantly more efficient. 

Our software development kit is designed to be developer friendly, reducing engineering effort and accelerating time to market. 

We also ease the compliance burden. Our FAP20 and FAP30 fingerprint sensors are already certified to leading standards, including personal identity verification issued by the FBI and Aadhaar, so OEMs don’t have to start from scratch. 

Beyond technology, we support integration directly. 

Our global support team of skilled field application engineers and system integration support works closely with OEMs to resolve challenges quickly, effectively shortening what is typically a time-intensive process.

And for OEMs looking for minimal integration effort, solutions like the Oyster III provide true plug-and-play functionality via USB, with immediate compatibility with the Windows Biometric Framework, including both Windows Hello and Windows Hello for Business.

How do you balance the Zero Trust requirement for high security with the need for a frictionless user experience that works for different people? 

Balancing Zero Trust security with a frictionless user experience comes down to removing dependency on what users know, like passwords, and focusing on what they are, like biometrics.

Traditional approaches can increase security by enforcing stricter password policies but often that comes at the expense of usability. 

Extremely complex or frequently changing passwords may improve theoretical security, but they can create friction and lead to poor user behaviour. 

Biometrics solve this tension. 

Fingerprints, for example, are inherently unique and are always available to the user, they can’t be forgotten or easily shared. This allows for strong, continuous verification without adding steps or complexity to the users’ journey.

At NEXT, we then take this a step further, by supporting flexible deployment models that align with different Zero Trust strategies. 

Our solutions are agnostic to the underlying architecture, with proven deployments ranging from the secure on-device matching in handheld systems, to large-scale centralised platforms like Aadhar. 

This flexibility allows OEMs and solution providers to choose the right balance for their use case – whether prioritising privacy with local matching or scalability with a centralised system, while still delivering fast, reliable and low friction authentication. 

There is a growing trend of moving biometric processing away from central servers and onto the device itself. How does this shift improve privacy and security within a Zero Trust framework, and how does your technology support this edge authentication model?

The biometric industry is growing and with growth comes diversity, both in execution and application. This diversity includes: how and where to match and store biometric data.

These are two different ways of working, where the biometric matching is either handled centralised on a server or locally on your device.

Within a Zero Trust framework, on-device (edge) authentication offers clear advantages, Biometric data remains local, which significantly reduces exposure to network based attacks and removes the need to transmit sensitive information. 

This minimises the attack surface, enhances privacy and gives users greater control over their data. It also enables fast and reliable authentication without dependency on network connectivity.

That said, there isn’t a one-size-fits-all model. 

Centralised systems still play an important role in large-scale identity programs and cross-platform interoperability.

We support both approaches. 

Our solutions are architecture agnostic, with proven deployments ranging from secure on-device matching in handheld devices to large scale centralised platforms, such as India’s Aadhaar. 

This flexibility allows OEMs and solution providers to choose the model that best suits their security, privacy and scalability requirements, while consistently delivering strong biometric performance.

Where do you see physical biometrics fitting into a world where AI agents are acting on behalf of humans? 

As AI continues to expand what’s possible in the digital world, one thing becomes increasingly critical: knowing when a real person is behind an interaction. 

Physical biometrics, especially fingerprint recognition, provide that anchor to reality. 

While AI can now replicate faces, voices and behaviours, it is yet to convincingly replicate physical, human fingerprints. That makes biometric authentication, particularly fingerprint recognition, a highly reliable layer of assurance in an AI-driven world. 

From our perspective, it is essential to stay one step ahead.

Naturally, we don’t just view AI as a threat, we actively use it to enhance biometric performance – from improved matching accuracy to stronger spoof detection.

As autonomous agents continue to grow, especially in emerging fields such as agentic payments, physical biometrics will remain a critical and trusted layer for authentication, ensuring that actions can always be traced back to a verified human presence.