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Regulation · Product · Vendor & risk decisions

Age Assurance Across Global Markets

How do you meet divergent regulatory obligations across markets without shipping a hostile product experience?

Company
SoundCloud
Period
2024–2026
Disciplines
Regulatory, Product, Privacy, Risk
Chapters
8
Multi-market
Divergent regulatory regimes
1 model
Instead of per-market bolt-ons
01The situation

Regulators across several jurisdictions introduced age assurance obligations on different timelines with different definitions of adequate. Each one, read alone, implied a different implementation. Read together, they implied an architecture.

02The actual problem

The request was

Get us compliant in these markets by the deadlines.

Discovery showed

Compliance per market would have produced a stack of one-off integrations, each collecting more personal data than necessary and each degrading signup in a different way.

The durable answer was a single assurance model with market-configurable strictness, meeting the strictest requirement structurally while minimizing data collected and friction imposed everywhere else.

03What I learned
  • Compared obligations across markets to find the shared structural requirements underneath different wordings.
  • Evaluated assurance vendors on accuracy, data minimization, accessibility, and regional coverage.
  • Modeled signup friction and drop-off against each assurance method.
04How I framed it

Exhibit

Assurance Method Decision Matrix

 CertaintyData collectedFrictionAccepted where
01Self-declarationLowMinimalNoneLowest-risk contexts
02Behavioral / inference signalsModerateExisting onlyNoneSupplementary
03Third-party estimationHighTransient biometricLowSeveral markets
04Document verificationHighestSensitive IDHighStrictest regimes

Escalate strictness only when the market or the risk demands it. Never collect a tier more than needed.

What you're looking atMethods scored against certainty, data collected, user friction, and market acceptance. The right answer differs per market, the framework does not.
05What we built
  • 01A single age-assurance model with market-configurable strictness.
  • 02A vendor and method decision framework balancing certainty, data minimization, and friction.
  • 03Implementation sequencing tied to regulatory deadlines by market.
06My role
Product
Owned the assurance experience and its strictness model.
Regulatory
Translated obligations across markets into product requirements.
Vendor & risk
Led evaluation and the data-minimization position.
07The outcome
  • One architecture serving multiple regimes instead of per-market bolt-ons.
  • Data collection held to the minimum each market genuinely required.
  • Regulatory deadlines met without a punitive signup experience.
08What I'd do differently

Regulation reads like a constraint and behaves like a product brief. Treating it that way earlier in the process would have gotten design involved sooner, and design is what kept the strictest path from feeling like a punishment.

Contact

Let's get into it.

Ambiguous problem, AI adoption that stalled, an operating model that stopped scaling, a support function that should be a product. That is the conversation I want.