By Mikkel Nielsen, CPO at Verifymy
Artificial intelligence plays an increasingly pivotal role in online verification processes, but it is at a fascinating crossroads. On one hand, AI acts as a powerful enabler, facilitating seamless verification, protecting users, and ensuring compliance with regulations. However, on the other hand, AI is also at the center of emerging threats that undermine trust in the very systems it helps to secure. The paradox is striking, and as the rise of deepfakes and synthetic identities challenges the security of platforms worldwide, it becomes more difficult to trust what we see, hear, or even verify online. As industries grapple with these complexities, the key question becomes: How can AI be leveraged to secure digital verification processes while mitigating the risks it introduces?
The transformative power of AI in verification
AI is fundamentally reshaping identity verification by automating processes, detecting fraud in real-time, and improving user experiences. Technologies like liveness detection and multimodal biometrics ensure that the person verifying their identity is authentic and present, and not a spoofed or synthetic version. However, the same technologies can be manipulated. The challenge is not just in adopting AI, but in ensuring it evolves faster than the threats it seeks to counter.
Mitigating risks while maintaining user experience
To balance security and usability, many industries are moving towards risk-based authentication, where AI systems dynamically assess risk by evaluating factors such as user behavior, device data, and location. For most users, this results in seamless experiences, so when something appears suspicious, the system can escalate verification steps without affecting the majority of users. However, for deepfake threats, AI-powered anti-spoofing technologies are key. These systems can detect minor inconsistencies that signal a deepfake, such as unnatural movements or lighting irregularities. Additionally, liveness detection ensures that the person interacting with the system is a live human, not a pre-recorded or synthetic entity.
The rising importance of behavioral biometrics
Behavioral biometrics offer an additional layer of security by analyzing unique user patterns—such as typing speed, mouse movements, or how someone holds their device. These patterns are incredibly difficult to replicate or spoof, making them a powerful tool in fraud detection.
The advantage of behavioral biometrics is that they operate in the background, continuously monitoring for inconsistencies without disrupting the user’s experience. So, if a bad actor is using stolen credentials or biometrics, their behavioral profile likely won’t match that of the legitimate user, prompting further verification steps. Similarly, the analysis of a user’s digital footprint, as seen in behavioral age assurance techniques, such as email address age estimation, is an evolving aspect of behavioral data that enhances security without introducing friction. And when combined, behavioral biometrics and digital footprint analysis can significantly strengthen AI-driven verification processes.
Ensuring compliance with privacy regulations
To ensure compliance with privacy regulations in AI-driven verification, many industries are adopting federated learning and zero-knowledge proofs— a method that allows one party to prove they know certain information without revealing the information itself— to uphold privacy while still leveraging the full power of AI. Federated learning allows AI models to improve by learning from decentralized data without requiring that sensitive information leave a user’s device. Similarly, zero-knowledge proofs allow verification to occur without exposing any underlying data, making them an essential tool for privacy-preserving verification in today’s regulatory landscape.
Addressing bias and fairness in AI-powered verification systems
As AI-driven solutions become central to verification processes, they face the challenge of potential bias—unintended disparities in accuracy across different demographic groups. This concern is particularly relevant in age assurance, where even small biases could lead to unfair access restrictions or inaccurate results for specific user groups.
Building a fair and accurate AI model starts with data diversity. Age estimation models that draw on a broad range of ages, backgrounds, and behavioral patterns are more reliable and consistent across user demographics. This inclusive approach ensures that no single group is disproportionately affected, making age verification results both accurate and equitable. By embedding diversity into the foundation of the model, AI-powered verification systems can mitigate the risk of biased outcomes that affect specific populations more than others.
The future of AI in verification
Looking forward, industries will need to prioritize transparency, ensuring that AI systems are fair, unbiased, and respect user privacy. As AI becomes more embedded in verification, maintaining trust will be crucial—not just through technological advancements but through clear communication about how data is used and protected. With the continued evolution of AI in verification, industries should expect to see more dynamic verification systems—where the level of security adapts based on the context of the transaction or interaction. In high-risk cases, an additional layer of human moderation can serve as a second line of defense, providing a nuanced, manual assessment that complements AI’s capabilities.
This approach allows companies to maintain a frictionless experience for most users while intensifying scrutiny when the risk warrants it. Such a hybrid system not only helps mitigate potential oversights, but also builds user trust by ensuring that sensitive decisions involve human judgment alongside advanced AI.
Companies at the forefront of AI-powered verification must work closely with regulators to balance innovation with responsibility. AI plays a key role in creating seamless and secure verification experiences, but it also comes with risks, as bad actors can exploit the same technologies. The challenge is to stay ahead of these emerging threats while ensuring that solutions remain user-friendly, reliable, and compliant with industry regulations.
About the author
Mikkel Nielsen is Chief Product Officer at Verifymy.
Article Topics
age verification | biometrics | digital identity | dynamic verification | identity verification | user experience | VerifyMy
