FPF Releases Updated Infographic on Age Assurance Technologies, Emerging Standards, and Risk Management
The Future of Privacy Forum is releasing an updated version of its Age Assurance: Technologies and Tradeoffs infographic, reflecting how rapidly the technical and policy landscape has evolved over the past year. As lawmakers, platforms, and regulators increasingly converge on age assurance as a governance tool, the updated infographic sharpens the focus on proportionality, privacy risk, and real-world deployment challenges.
What’s New
The updated infographic introduces several key changes that reflect the current state of age assurance technology and policy:
A Fourth Category: Inference. The original infographic outlined three approaches to age assurance: declaration, estimation, and verification. This update adds a fourth category—inference—which draws reasonable conclusions about a user’s age range based on behavioral signals, account characteristics, or financial transactions. For example, an email address linked to workplace applications, a mortgage lender, or a 401(k) provider, combined with login patterns during business hours, may infer that a user is an adult.
Relatedly, the updated version intentionally downplays age declaration as a standalone solution. While declaration remains useful for low-risk contexts and as an entry point in layered systems, experience and enforcement history continue to show that it is easily bypassed and insufficient where legal or safety obligations attach to age thresholds. The infographic now situates declaration primarily as an initial step within a waterfall or layered approach, rather than as a meaningful assurance mechanism on its own.
The update also highlights several new and emerging potential risks associated with modern age assurance systems. If not addressed properly, these could include loss of anonymity through linkage, increased breach impact from improper secured retained assurance data, secondary data use of assurance data, and circumvention risks such as presentation attacks or shared-device misuse.
In parallel, the infographic expands its coverage of risk management tools that can mitigate these concerns when age assurance is warranted. These include tokenization and zero-knowledge proofs to limit data disclosure, on-device processing and immediate deletion of source data, separation of processing across third parties, user-binding through passkeys or liveness detection, and emerging standards such as ISO/IEC 27566 and IEEE 2089.1. The emphasis is not on eliminating risk—which is rarely possible—but on aligning technical controls with the specific harms a service is attempting to address.
As with prior versions, the updated infographic reinforces a core message: there is no one-size-fits-all age assurance solution. Effective approaches are risk-based, use-case-specific, and privacy-preserving by design, balancing assurance goals against the rights and expectations of users. By clarifying the role of inference, contextualizing declaration, and surfacing both new risks and mitigation strategies, this update aims to support more informed decision-making across policy, product, and engineering teams.
Emerging Age Assurance Concepts. The field has advanced considerably, and the updated infographic now includes a dedicated section on emerging technologies that address Age Signals and Age Tokens, User-Binding, Zero Knowledge Proofs (ZKP), Double-Blind Models and One-Time vs. Reusable Credential.
Updated Risks and Risk Management Approaches. The infographic now presents a more comprehensive view of the risks and challenges associated with age assurance—including excessive data collection and retention, secondary data use, lack of interoperability, false positives and negatives, data breaches, and user acceptance challenges. Correspondingly, the risk management section highlights both established and emerging mitigations: on-device processing, tokenization and zero knowledge proofs, anti-circumvention measures (such as Presentation Attack Detection), standards (ISO/IEC 27566-1, IEEE 2089.1), and certification and auditing.
Practical Example: The updated infographic includes a detailed use case following “Miles,” a 16-year-old accessing an online gaming service. The scenario illustrates how multiple age assurance methods can work together in a layered “waterfall” approach—starting with low-assurance age declaration for basic access, escalating to facial age estimation for age-restricted features, and offering authoritative inference or parental consent as inclusive fallbacks when estimation results are inconclusive and formal id is not available . The example also demonstrates token binding with passkeys, ensuring that even if Miles shares his phone with a younger friend, the age credential cannot be accessed without the correct PIN, pattern, or biometric.
