.png)
Age assurance has become a mainstream requirement. More platforms are being asked, or rather, required, to verify their users’ age before granting access to certain content or features.
But growing consumer awareness and increasing privacy concerns are becoming important considerations in this conversation, too. Now platforms must go beyond asking whether they should verify age, and instead focus on how they will verify age securely, privately, accurately, and at scale.
In this article, I examine why leading platforms are going beyond the regulatory minimum and making on-device age estimation an option for users who want it.
Regulations across four continents now demand that digital platforms, especially those operating in sensitive markets, verify the age of their users:
Each of these frameworks converges on a singular expectation: platforms must take meaningful steps to prevent minors from accessing age-restricted content. And they must start taking those steps as soon as possible because age assurance requirements are evolving rapidly.
Age verification tools have become absolutely necessary to meet compliance outcomes. But forward-thinking companies aren't rolling out age verification and stopping there. They're asking a harder question: does our age assurance approach unnecessarily alienate end users?
Three out of four consumers say they simply won’t engage with organizations they don’t trust with their data, and 63% of consumers have expressed serious concern over biometric data collection. Asking users to hand over more biometric information or sensitive data in order to verify their age risks creating a further trust divide. Organizations must approach age assurance cautiously.
No regulation today specifically requires on-device processing. The law defines the obligation, yes, but it doesn't define how it gets fulfilled. That gap is where platform reputation is built or lost. Offering users a more private option for age assurance helps to signal respect for their preferences.
Cisco’s 2026 Data and Privacy Benchmark Study found that 95% of organizations worldwide have experienced increased consumer loyalty and trust as a result of strong investment into privacy-related initiatives.
As regulations surrounding age assurance develop further and users become more familiar with the privacy-enhancing techniques available, these decisions will become a significant competitive advantage.
Not all age assurance methods carry the same user experience. Document verification requires uploading an ID. Database checks require matching against personal records. Facial age estimation works differently: it requires nothing from the user except a selfie. It requires no document, personal records, or account lookup.
That simplicity makes it inherently more inclusive. Facial age estimation works across demographics and markets, regardless of whether a user has government-issued ID, making it the most accessible first layer a platform can offer.
Based on Incode's data across regulated markets, facial age estimation is chosen 8 out of 10 times as the first age assurance method. It is the most common entry point users have into age assurance today.
That reach is exactly what makes user choice within this method so important. If most users are experiencing age assurance through facial estimation first, platforms have a real opportunity to offer options that match different user comfort levels. Some users are perfectly comfortable with standard flows. Others want the assurance that their face stays only on their device and goes no further.
Offering on-device facial age estimation as an option recognizes that users are different, their expectations are different, their expectations are different, and the platforms that respect that will earn trust much more quickly.
And organizations can go one step further with on-device age estimation.
In a standard facial age estimation flow, a user's image is captured, sent to a remote server, analyzed, and a result is returned. The image, or data derived from it, travels outside the device. With Incode’s On-Device Age Estimation, the model runs locally on the user's own hardware. The face is never transmitted to any server and remains technically inaccessible to Incode or anyone else.
Critically, Incode’s on-device processing does not mean a trade-off in accuracy. Advances in on-device machine learning mean that local models can match the performance of server-side processing, delivering the same reliable result without biometric data leaving the device.
For a meaningful share of users, particularly those who've experienced a data breach, those who operate in privacy-forward contexts, or those who simply read the fine print, the difference between on-device age estimation and server-side estimation matters a great deal.
At Incode, privacy-preserving architecture is our foundation. Across our platform, we are actively investing in solutions where biometric data is structurally protected by design, not by policy.
On-device age estimation is becoming a priority within that broader commitment for exactly the reasons outlined above. It is the method most users encounter first, and giving platforms the ability to offer it as a privacy-preserving option is one of the most impactful things we can do for end users today.
As Ricardo Amper, Founder & CEO of Incode, put it in his Privacy Manifesto: "We did not build it this way because the law required it. We built it this way because we saw no other responsible option."
Our On-Device Age Estimation solution gives users who want the highest level of privacy the option to verify their age without their face ever leaving their device.
Ready to give your users that option? Learn more about Incode On-Device Age Estimation.