Cabier AI · Umbrella category

    Global AI Assurance

    Governance asks who owns AI. Assurance asks whether the institution can prove its policies operate. As systems retain memory, use tools, reach data and take actions, the supervisory question moves from what was approved before deployment to what the estate is doing right now, and who is able to stop it.

    Cabier is model-agnostic by design. The institution chooses the models. Cabier assures the estate that results, including the parts of it that were never procured as AI.

    Published as reference architecture. Control weights, grading rubrics and the full control set are set per engagement.

    Nine disciplines under one umbrella

    Each discipline exists because a different person asks the question. The board, the regulator, the CISO, the auditor and the operator are not asking the same thing about the same system.

    01

    AI Governance

    Who owns AI, and under what policy?

    Named accountability per system, an approved policy set and a board record of the risk that was accepted.

    AI Assurance OS AI Model Inventory GPAI Registry
    02

    AI Assurance

    Can the institution prove those policies operate?

    Evidence produced continuously by the control itself, not assembled when a supervisor asks.

    AI Supervisory Readiness Independent Evaluation AI Runtime Assurance
    03

    AI Control

    Can the institution constrain what AI is able to do?

    Scoped authority per agent, enforced at the point of action rather than described in a policy document.

    AI Control Plane Agent Permission Fabric AI Controls Library
    04

    AI Security

    Can it detect and contain an AI-enabled attack?

    Prompt injection, jailbreak, privilege escalation and exfiltration treated as cyber events with owners.

    Agent Security Framework Agentic Control Testing
    05

    AI Resilience

    Can it survive model, provider or agent failure?

    Fallback models, exit tests and impact tolerance measured with the AI dependency as the failure mode.

    AI Supply Chain Graph AI Systemic Risk
    06

    AI Regulatory Intelligence

    Which rules actually apply, and from when?

    Obligation-level mapping from authority to control to evidence, with effective dates and supervisory expectation.

    Cross-Jurisdiction AI Assurance AI Regulatory Harmonisation
    07

    AI Sovereignty

    Where do the model, the data and the evidence operate?

    Residency and compellability resolved per workload before deployment, not discovered during an examination.

    Sovereign AI Governance OS Sovereignty Decision Engine
    08

    AI Supply Chain

    Which third-party models, agents and tools are embedded?

    The estate the institution did not choose but already owns through a vendor, mapped to the dependency beneath it.

    AI Supply Chain Graph AI Estate Discovery
    09

    AI Agent Assurance

    What can autonomous systems actually do today?

    Authority envelope per agent, an out-of-envelope refusal on record and a named human owner for each.

    Agent Identity Registry Agent Workforce Agent Transaction Assurance

    The architecture

    Models and agents sit below. The assurance layer sits above them, and everything it produces resolves into one institutional position.

    Global model ecosystem

    OpenAIAnthropicGoogleMicrosoftMetaxAIMistralDeepSeekQwenKimiSovereign / nationalPrivate / internalOpen-weightOn-premiseEmbedded vendor

    Ecosystems observed in institutional estates. Naming is not endorsement, and no comparative benchmark is published.

    Cabier assurance layers

    AI Governance
    AI Assurance
    AI Control
    AI Security
    AI Resilience
    AI Regulatory Intelligence
    AI Sovereignty
    AI Supply Chain
    AI Agent Assurance

    Assurance Kernel

    Seven Trust Gates evaluate every governed invocation.

    Trust Graph

    One typed map of obligations, controls, systems, models, agents and evidence.

    Decision Fabric

    Where an intervention is decided, recorded and attributed.

    Evidence Vault

    The artefact a supervisor, auditor or board reads.

    AI Trust Score

    Per-system position across fourteen dimensions.

    ORS

    The institutional resilience position the board is accountable for.

    Enterprise

    Board, CRO, CISO, CCO, CIO and internal audit read the same position from the same evidence.

    Government

    Ministries, agencies, supervisors and auditors read it under their own residency conditions.

    One kernel, composed per institution

    Global horizontal kernel, plus a jurisdiction pack, plus an industry pack, plus the enterprise AI estate. Different obligations, different evidence, the same kernel underneath.

    JurisdictionIndustryPackWhat changes
    CanadaBankingOSFI E-23 and B-10 with federal privacy overlayModel risk rating drives validation depth, and third-party AI is examined at the same time.
    United StatesInsuranceNAIC model bulletin with state-level conduct overlayConduct and unfair-discrimination testing sit ahead of model documentation.
    United KingdomLegalSRA and professional-privilege constraints with SMCR-style accountabilityPrivilege and confidentiality bound the reach of the agent before any performance question.
    SingaporeGovernmentSovereign deployment with public-sector data classificationResidency and compellability decide the architecture, not the procurement.

    As intelligence becomes more capable and more autonomous, assurance becomes infrastructure. That is the layer Cabier operates.

    Start with estate discovery

    Flagship analysis

    Governance became an operating control

    The full argument: why the approval gate stopped working, what a running assurance loop does instead, and what the board should be told rather than scored.

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