Global AI Assurance · Platform ecosystems
Platform and Algorithmic Assurance
The assurance kernel is subject-agnostic on purpose. A platform that ranks, recommends, moderates, targets and generates is running the same objects a bank runs: models, agents, data, authority, actions, jurisdictions, controls and evidence.
So the architecture extends to algorithmic and AI-driven platforms without a separate product, and the jurisdiction engine resolves the rules per user population rather than per company address.
Published as reference architecture. Control weights, grading rubrics and the full control set are set per engagement.
The assurance object does not change
Subjects the kernel serves
Eleven assurance domains
Each one is a question a supervisor, a court or a parliamentary committee has already asked a platform.
Algorithm assurance
What determines what a user sees, and can that determination be described without reference to the model weights?
Evidence · Versioned ranking objectives, change log, and the review that approved each change.
Recommendation assurance
How does ranking behave for the populations most exposed to it?
Evidence · Segment-level outcome testing with dispositions, dated, plus the remediation raised.
Content moderation assurance
Which decisions are made by a model, which by a person, and which by a model a person never sees?
Evidence · Decision records with actor type, policy invoked, appeal route and reversal rate.
Generative AI safety
What happens when a user interacts directly with a generative system on the platform?
Evidence · Pre-release evaluation coverage, refusal logs, and incident records tied to versions.
Child and minor safety
Which additional safeguards apply, and are they enforced rather than declared?
Evidence · Age-assurance control state, restricted-feature enforcement records, and escalation logs.
Privacy assurance
What data feeds the models, on what basis, and for how long?
Evidence · Data lineage from source to model, lawful basis, retention, and transfer register.
Advertising and targeting assurance
Are targeting and optimisation systems operating within consumer and sector rules?
Evidence · Excluded-attribute enforcement, financial-promotion checks, and per-market rule sets.
Platform integrity
Can the platform show that manipulation, bots, coordinated activity and synthetic content are detected and acted on?
Evidence · Detection coverage, action rates, and the record of what was left in place and why.
Automated decision transparency
Can a significant automated decision be explained to the person it affected?
Evidence · Explanation artefacts held against the decision record, not generated after a complaint.
Cross-border governance
Which rules apply to which users, and does enforcement follow the user rather than the company address?
Evidence · Per-jurisdiction overlay applied at the user population level, with evidence residency named.
Evidence
Can the platform prove its controls operated during the period under review?
Evidence · Continuous control records in the evidence vault, with freshness and completeness stated.
One platform, many regimes
A global platform does not need one global rule. It needs a global policy, jurisdictional overlays, and evidence that both operated.
EU users
US users
UK users
Canadian users
Indian users
Japanese users
Position
Cabier does not moderate content and does not rank it. It assures that the systems doing so are governed, tested, attributable and evidenced, and that the applicable rules are resolved per user population rather than per company headquarters.
One platform, many user jurisdictions, one intersection to resolve.
See how the populations resolve