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AIRAS Cloud

Illustrative scenarios

How regulated organisations govern AI with AIRAS Cloud.

Representative deployment scenarios, modelled on the governance problems we see repeatedly in regulated sectors. They show how the platform is used and what it produces — they are not verified customer references.

Read these as scenarios, not as customer references

Each scenario below is illustrative. The organisations are composites, the figures are modelled to show the shape of a typical deployment, and the quotations are written to express the governance problem being solved — they are not statements made by named customers. Nothing here should be treated as verified customer evidence.

Where we can evidence something, we publish it with its status in the trust centre: verified in product, controlled document, in external assurance, or not claimed.

01Financial servicesIreland / EUNot claimed

From spreadsheet inventory to governed AI register in eight weeks.

Organisation: A mid-sized European retail bank with operations in Ireland, Germany and the Netherlands.

The challenge

  • More than forty AI use cases were tracked across different risk, procurement and IT teams.
  • There was no single record of which systems were high-risk under the EU AI Act, who owned them, or what evidence existed.
  • The compliance deadline of 2 August 2026 was approaching while internal legal review was still manual.

The approach

  • Deployed a single AIRAS Cloud tenant with segregated workspaces for legal, risk, compliance and IT review.
  • Imported existing policy documents and vendor contracts to seed the discovery pipeline.
  • Ran the deterministic qualification engine against every registered use case to produce a defensible classification.

47

AI systems registered in the governed inventory

12

High-risk systems pre-classified with evidence packs

8

Weeks from tenant provisioning to executive report

100%

Append-only audit trail for every classification decision

“For the first time we can show our regulator exactly how we reached a classification, who reviewed it, and on what evidence.”

— Illustrative Head of Model Risk, European retail bank perspective. Composite scenario, not a customer statement.
02HealthcareIrelandNot claimed

Clinical AI oversight with a clear chain of accountability.

Organisation: A private hospital group operating multiple sites across Ireland.

The challenge

  • Clinical decision-support tools, imaging algorithms and operational forecasting models were owned by different departments.
  • Patient-safety and data-protection teams needed a shared record that satisfied both clinical governance and EU AI Act expectations.
  • There was no consistent way to demonstrate human oversight for high-risk clinical AI.

The approach

  • Configured role-based access so clinical, legal, privacy and IT reviewers could each contribute without overreaching.
  • Linked every AI system to a named accountable owner, clinical workflow and evidence location.
  • Used the AIRAS oversight module to record review cadence, conditions of use and escalation paths.

23

Clinical and operational AI systems under governance

6

High-risk systems with documented human-oversight plans

3

Departments aligned on one governance record

0

Confidential data left the controlled environment

“The board wanted assurance that clinical AI was overseen, not just purchased. AIRAS gives us the record to prove it.”

— Illustrative Chief Clinical Information Officer, Irish hospital group perspective. Composite scenario, not a customer statement.
03Life sciencesIreland / USNot claimed

Global pharma pipeline governance from research to commercial.

Organisation: A biopharmaceutical company with R&D in Ireland and commercial operations in the United States and Europe.

The challenge

  • AI was used in compound screening, clinical-trial optimisation, pharmacovigilance signal detection and commercial analytics.
  • Different jurisdictions imposed different expectations, and the same system could be high-risk in one context and lower-risk in another.
  • The company needed a single source of truth that could be audited by both internal QA and external regulators.

The approach

  • Mapped each AI system to its jurisdiction, organisational role and intended use context.
  • Ran prohibited-practice and high-risk pre-classification screening against the EU AI Act baseline.
  • Created a material-change workflow so new indications, data sources or model versions triggered reassessment.

61

AI systems tracked across R&D, operations and commercial

4

Jurisdictional contexts modelled per system where needed

18

Assessments completed with reviewer segregation

1

Unified audit record spanning Ireland and US teams

“We needed governance that could travel with the product. AIRAS lets us show the same decision record to Irish, European and US stakeholders.”

— Illustrative VP Regulatory Affairs, biopharmaceutical company perspective. Composite scenario, not a customer statement.
04TechnologyIreland / RemoteNot claimed

SaaS vendor proves AI governance to enterprise buyers.

Organisation: An Irish-founded B2B software company embedding generative AI features into its platform.

The challenge

  • Enterprise procurement teams began asking for evidence of AI governance, risk assessment and EU AI Act readiness.
  • The company had no formal AI register and no documented process for classifying new features.
  • Sales cycles were lengthening because security and legal reviews could not be answered quickly.

The approach

  • Used AIRAS Cloud to register every AI-powered feature, model provider and data flow.
  • Produced buyer-facing evidence packs directly from the assessment and decision records.
  • Established a lightweight governance operating procedure for new feature releases.

19

AI features registered and assessed

4

Enterprise procurement reviews supported with evidence packs

50%

Reduction in security-questionnaire turnaround time

1

Public trust centre page backed by the governed record

“Buyers stopped asking whether a process existed and started asking to see the record. That is what moves an enterprise review forward.”

— Illustrative Chief Technology Officer, Irish B2B software company perspective. Composite scenario, not a customer statement.

Common outcomes

What these programmes share.

Defensible classification

Every high-risk determination is tied to a ruleset version, evidence item and named reviewer.

Deadline-ready workflow

Discovery, assessment, review and evidence are connected in one continuous process.

Executive visibility

Board and committee reports are generated from the same record the operational teams use.

Sales acceleration

Vendor and buyer-facing evidence packs shorten procurement and security reviews.

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