S8.08 - Cloud_deployment

S8.08 ? Cloud deployment

flowchart LR
    A[Cloud context: scalable managed services
but partial access to internals and logs] --> B[RAIDT
Run-level evidence framework]
    B --> C[[Cloud deployment
Evidential conditions for each run]]
    H[Healthcare, finance, public services,
enterprise copilots, managed APIs] --> C
    C --> D[Evidence pack completeness]
    C --> E[Score profile quality]
    C --> F[Reviewer reconstruction]
    D --> G[Reviewability, contestability,
audit readiness, organisational learning]
    E --> G
    F --> G

? Star S8 - Implementation and Operations

Star context: Shows how RAIDT can be adopted manually, semi-automatically or through orchestration, and how deployment choices shape what evidence can be captured, reviewed and governed in real organisational practice.

Academic picture

Definition / background

Cloud deployment refers to the use of cloud-hosted infrastructure, managed APIs, software platforms, or enterprise AI services to execute generative AI runs. In a narrow technical sense, it concerns where the model and associated services are hosted. In RAIDT, however, cloud deployment has a more specific governance meaning: it defines the evidential environment within which a run occurs and therefore affects what can be captured, inspected, reproduced, and challenged afterwards.

This matters because RAIDT treats the run as the unit of governance. A run is not only a model output; it is a situated event involving a task, prompt, configuration, inputs, outputs, timing, operator role, and organisational context. When such a run takes place in a cloud environment, the organisation may gain scalability, resilience, and managed tooling, but it may lose direct visibility into internal model changes, hidden service updates, infrastructure logs, or provider-side controls. Cloud deployment therefore sits at the boundary between operational convenience and evidential sufficiency.

The concept differs from related terms such as local deployment, hosting choice, or software-as-a-service adoption. Those terms often focus on cost, speed, or information security. RAIDT includes those concerns, but its distinctive question is whether the deployment setting supports credible run-level evidence. If a cloud platform exposes prompt histories, model version identifiers, timestamps, retrieval traces, moderation events, user roles, and output logs, RAIDT can work effectively. If it does not, the organisation must compensate through wrappers, process controls, or explicit acknowledgement of evidence gaps.

For that reason, cloud deployment belongs centrally within RAIDT. It affects the completeness of the evidence pack, the defensibility of the five-pillar score profile, and the organisation's ability to move from principle-level claims about responsible AI to operational governance based on inspectable traces.

Why this concept matters

Cloud deployment matters because many organisations will adopt GenAI first through managed cloud services rather than through fully local or self-hosted systems. If governance frameworks ignore that reality, they become aspirational but impractical. RAIDT instead asks a harder and more useful question: what evidence can still be generated and reviewed when the system is deployed in a cloud environment with partial organisational control?

This concept helps avoid a common confusion between technical access and governance sufficiency. An organisation may have a secure contract with a cloud provider and still be unable to reconstruct a controversial run. Equally, a cloud deployment may be governable if the organisation deliberately captures the right metadata, retains the relevant prompts and outputs, and documents the limits of what the provider exposes. The issue is therefore not whether cloud deployment is good or bad in the abstract, but whether the deployment supports accountable review.

Without this concept, organisations risk overstating what they know about model behaviour, underestimating evidential blind spots, and treating provider assurances as substitutes for run-level records. RAIDT uses cloud deployment to make those limits explicit and manageable.

Key idea: Cloud deployment matters in RAIDT because hosting choices directly shape the quality, completeness, and reviewability of run-level evidence.

What this item enables

It enables organisations to translate a cloud architecture decision into a governance and evidence decision.
It enables reviewers to judge whether a cloud-based run can be reconstructed with sufficient detail for challenge, audit, or learning.
It enables evidence packs to record provider, platform, model version, access layer, region, logging scope, and retention constraints.
It enables more realistic scoring across the five RAIDT pillars by distinguishing between visible evidence and provider-side unknowns.
It enables compensating controls such as wrappers, metadata templates, reviewer forms, and orchestration logs when cloud platforms expose limited internals.
It enables clearer comparison between cloud and local deployment in terms of evidence sufficiency rather than ideology.

Practical example / likely audience question

Audience question

If a generative AI system is deployed through a cloud platform that hides many internals, can RAIDT still provide meaningful governance?

Answer

The concern behind this question is that cloud deployment may appear to make serious governance impossible because the organisation does not control the full stack. The direct answer is yes: RAIDT can still provide meaningful governance, but the strength of that governance depends on the quality of the evidence the organisation can capture at run level and on how clearly it documents what remains opaque.

A practical example is an organisation using a managed cloud API to generate draft policy summaries. The organisation may not have access to model weights, provider-side routing logic, or all backend moderation events. However, it can still record the prompt, user role, task type, model identifier, timestamp, configuration settings, retrieved documents, output, post-run review decision, and any escalation triggered by the run. That evidence is sufficient to support reviewability and organisational learning, even if it does not provide total technical transparency.

RAIDT handles this issue better than a generic AI governance approach because it does not rely on broad assurances such as ?the vendor is reputable? or ?the system is secure by design?. Instead, it asks what can be evidenced for this run, what cannot be evidenced, and how those limits affect the resulting score profile and governance confidence.

Practical example in RAIDT terms

A hospital uses a cloud-hosted large language model through an enterprise platform to draft discharge summaries from clinician notes. The GenAI use case is operationally useful because it saves administrative time and improves consistency, but the run-level governance issue is that the hospital cannot inspect provider-side model updates or low-level inference traces.

For a specific run, RAIDT would require evidence such as the prompt template used, the clinical notes supplied as input, the staff role initiating the run, the model and service version exposed by the platform, the time and date, the output draft, any warning flags raised by the interface, the clinician's edits, and the final approval decision. Additional evidence would include the hospital's retention policy, region or tenancy constraints, and whether the platform logs retrieval or tool-use steps.

The most affected RAIDT pillars are Auditability, Dependability, and Traceability. Auditability depends on whether the run can be reconstructed. Dependability depends on whether the cloud service behaves consistently enough for safe operational use. Traceability depends on whether the organisation can link the output back to the exact run conditions and governance decisions. Responsibility and Interpretability are also affected, but often through documented oversight and explanation practices rather than direct access to model internals.

In governance readiness terms, cloud deployment does not block adoption, but it requires the hospital to be precise about evidential limits and to add compensating controls where the platform is opaque. That is exactly the kind of operational realism RAIDT is designed to support.

Detailed link to RAIDT

Cloud deployment links to RAIDT in four ways.

First, it links to RAIDT's core idea that GenAI governance should be based on evidence about specific runs rather than general claims about systems or vendors.
Second, it links to the run because the cloud environment shapes which aspects of a run are visible, recordable, and reconstructable.
Third, it links to the evidence pack and the score profile because deployment conditions affect evidence completeness, scoring confidence, and the interpretation of missing traces.
Fourth, it links to reviewability, contestability, audit readiness, and organisational learning by making platform constraints explicit instead of leaving them hidden inside procurement or infrastructure decisions.

Cloud deployment ? Run-level evidence capture ? Evidence pack completeness ? RAIDT score profile quality ? Governance readiness

In this way, cloud deployment is not peripheral implementation detail. It is part of the chain through which RAIDT operationalises responsible governance.

Link to the five RAIDT pillars

Responsibility

Cloud deployment affects responsibility because organisations remain accountable for runs even when key services are provided externally. Responsibility therefore depends on clear role allocation between the organisation, the operator, and the cloud provider.