Operational AI governance and risk assurance
Established 2021 · Live and fully operational
Govern AI with clarity, control and evidence.
One governed path from AI intake to risk assessment, control, review, decision, monitoring and audit-ready evidence. Built for regulated organisations that must show their working.
AI governance portfolio
Northfield Group · All departments · Ruleset v2.4
Total AI use cases
64
Registered across 9 departments
Awaiting review
8
3 approaching due date
High risk
6
All with active conditions
Evidence completeness
91%
Across approved records
Risk distribution
- Low21
- Medium29
- High14
Reviews due this month
12
Periodic review, condition expiry and vendor change checks.
Governance queue
- Customer Service CopilotMediumPrivacy reviewAwaiting evidence
- Predictive Maintenance ModelHighQuality and SecurityIn review
- Supplier Document AssistantLowGovernance leadApproved with conditions
- Clinical Operations SummariserHighQuality, Privacy, LegalRestricted
Lifecycle
- Registered
- Assessed
- Reviewed
- Decision
- Monitoring
Illustrative interface – synthetic data
Enterprise delivery experience
AIRAS Cloud is founded on experience delivering complex technology, governance and transformation programmes across regulated and enterprise environments.
- Aer LingusAviation
- AvalonAviation leasing
- AIBBanking
- Bank of IrelandBanking
- IBM / KyndrylTechnology services
- EirGridCritical infrastructure
- Virgin MediaTelecoms
- Irish GovernmentPublic sector
The position
Most organisations can list their AI ambitions.Far fewer can evidence their AI decisions.
Policy documents describe intent. Governance fails at the operational layer — where assessing, reviewing, evidencing and monitoring actually happen. AIRAS Cloud is that layer.
One register
Every AI system, model, agent and embedded vendor feature in a single authoritative inventory, with named owners and lifecycle stage.
One rulebook
A deterministic risk engine applies versioned, human-approved criteria, mandatory floors and prohibited-use checks. AI does not approve AI.
One control set
Applicable controls derive from assessed context, with owners, due dates, hashed evidence and visible completeness.
One evidence trail
Versioned decisions, segregation of duties and append-only history that exports as an inspection-ready pack.
The platform
Assessment you can defend. Decisions you can prove.
Two of the mechanisms at the heart of AIRAS Cloud: a deterministic risk engine with a readable explanation trace, and an append-only decision and audit record.
Risk dimensions
Ruleset v2.4 · approved- Impact on people4 / 5
- Data sensitivity4 / 5
- Autonomy of action3 / 5
- Regulatory exposure5 / 5
- Operational criticality3 / 5
- Vendor dependency2 / 5
Calculated outcome
Base calculation returned Medium. A regulatory floor for GxP-impacting use raised the band to High.
Explanation trace
- Special category data declared in intake → sensitivity floor applied
- GxP-impacting process confirmed → Quality review mandatory
- Operational write-back enabled → Security review mandatory
- No prohibited-use pattern matched
Routed to Quality, Cybersecurity and Privacy. Segregation of duties enforced: the submitting owner cannot approve this record.
Rules are versioned and human-approved. AI does not approve AI.
Illustrative interface – synthetic data
Every dimension scored under approved, versioned rules — with floors, prohibited-use checks and the reason the outcome was reached.
Reviewer decisions
- Quality12 Mar
H. Okonkwo · Approved with conditions
- Cybersecurity12 Mar
L. Fereday · Approved
- Privacy / DPO13 Mar
A. Marchetti · Approved with conditions
- Executive sponsor14 Mar
S. Nairn · Final approval
Conditions: quarterly output sampling, documented human review of all customer- facing outputs, and reassessment on model or vendor change.
Audit history
14 Mar 09:41 — Decision recorded: Approved with conditions (v3)
13 Mar 16:02 — Condition added: quarterly output sampling
13 Mar 11:20 — Evidence attached: DPIA-0431 v2
12 Mar 08:55 — Risk recalculated under ruleset v2.4
Append-only. Entries cannot be edited or deleted.
Export
Illustrative interface – synthetic data
Reviewer decisions, conditions and rationale versioned against the record, with an exportable assessment and decision pack.
How it works
Eight governed steps, from first idea to inspection-ready evidence.
- 01
Discover and Register
Create a reliable inventory of AI use cases, systems, models, agents, vendor features and owners.
- 02
Define Context
Capture intended use, excluded use, users, decisions, value, lifecycle and accountability.
- 03
Map Data and Integrations
Record inputs, outputs, data classifications, providers, systems, transfers, access and write-back.
- 04
Assess Risk
Apply transparent, versioned risk rules, mandatory floors and prohibited-use checks.
- 05
Apply Controls
Generate relevant control and evidence requirements for the assessed context.
- 06
Review and Decide
Route Quality, Privacy, Security, Legal, Risk and other reviewers with clear segregation of duties.
- 07
Monitor and Reassess
Control changes, incidents, conditions, periodic reviews, restrictions and retirement.
- 08
Export the Evidence
Generate traceable assessment and decision packs with versions, mappings and audit history.
Alpha programme
Tested in the field, in Ireland, by the people accountable.
Between 2024 and 2025 AIRAS Cloud was tested end to end by risk, compliance, quality, security and audit leaders across regulated Irish organisations. Their feedback shaped the operating model now in production.
- HealthcareMarch 2025
We put nine clinical decision-support tools through the guided intake. The screening picked up two that had never been recorded anywhere and correctly floored a triage model to high risk on human-oversight grounds. The DPIA linkage meant our data protection officer worked from the same record as the clinical governance committee, which had never happened before.
Dr. Niamh Ó Braonáin
Head of Clinical Digital Governance · Voluntary teaching hospital group, Dublin
Tested: Clinical AI intake and DPIA linkage
- Financial servicesApril 2025
The point that convinced our audit committee was that the engine is deterministic and versioned. We re-ran an assessment from six weeks earlier against ruleset 1.0 and got the identical score and explanation trace. Segregation of duties is enforced rather than requested, so the approver genuinely cannot be the assessor.
Cormac Whelan
Director of Risk and Compliance · Irish retail bank, Dublin
Tested: Deterministic scoring and reviewer segregation
- Life sciencesMay 2025
We tested it against a live Annex 11 readiness review. The evidence pack exported with hashed artefacts, control owners, due dates and the full decision history in one document. Our auditor's usual three-week evidence hunt became a single afternoon of review.
Aoife Ní Chatháin
Quality Systems Lead · Contract pharmaceutical manufacturer, Cork
Tested: GxP evidence packs and Annex 11 alignment
- InsuranceMay 2025
Embedded vendor AI was our blind spot. Registering third-party features as first-class entries surfaced eleven capabilities switched on inside tools we already owned. Three needed contractual action. Without the register we would not have known they existed.
Seán Mac Giolla Phádraig
Chief Information Security Officer · Insurance group, Dublin
Tested: Vendor and embedded AI discovery
- Public sectorJune 2025
The prohibited-use checks stopped a proposed profiling use case at intake, with a written rationale we could hand straight to elected members. Being able to show the decision, the version of the rules applied and who approved it is what makes this defensible in a public setting.
Máire Donnelly
Data Protection Officer · Local authority, Galway
Tested: Prohibited-use screening and public accountability
- MedTechJune 2025
We ran two autonomous agents through oversight for six weeks. Scoped permissions, escalation thresholds and the moderation log gave our engineering leads something they had never had: a factual record of what the agent was allowed to do and what it actually did.
Declan Fitzgerald
Head of Data and AI · Medical technology manufacturer, Limerick
Tested: Agent oversight and runtime moderation
- ManufacturingJuly 2025
As an auditor my first instinct is to try to break the trail. The history is append-only, decisions are versioned and nothing can be quietly edited after approval. I tested it deliberately and could not manufacture a gap. That is a rare thing to be able to write in a report.
Sinéad Kavanagh
Group Internal Audit Manager · Food and agribusiness group, Kilkenny
Tested: Audit trail integrity
- TelecomsJuly 2025
Configuration governance was the surprise. Ruleset changes go through controlled versioning, so our risk function can evolve criteria without invalidating historic assessments. Integration into our existing identity and ticketing estate took days, not a quarter.
Ruairí Ó Donnchadha
Enterprise Architect · Telecommunications operator, Dublin
Tested: Multi-tenant configuration and integration
- Life sciencesAugust 2025
We used it to build our EU AI Act inventory position. Classification, obligations and control coverage sit against each system rather than in a spreadsheet a single person maintains. It turned a mapping exercise into an operating routine.
Orla Brennan
Head of Regulatory Affairs · Clinical research organisation, Dublin
Tested: EU AI Act readiness mapping
- Financial servicesSeptember 2025
Our reviewers are not data scientists. The adaptive intake asks only what the context needs and the explanation trace is written in plain language, so a board committee can read the rationale without a translator. Adoption was the fastest I have seen for a governance tool.
Pádraig Lynch
Director of Technology · Credit union services body, Cork
Tested: Adoption by non-technical reviewers
- Professional servicesOctober 2025
Most tools stop at approval. Periodic reassessment triggers and the incident register meant a model that drifted after a data change was picked up, reassessed and re-approved with the change captured against the original decision. That closed loop is the whole point.
Caoimhe Ní Mhurchú
Head of Responsible AI · Professional services firm, Dublin
Tested: Periodic reassessment and incident handling
- LogisticsNovember 2025
The executive view gave me one honest number: how many AI use cases we run, how many are governed and where the gaps sit. Assessment turnaround dropped from roughly five weeks to eight days across the alpha, and the board stopped asking for a status deck because they could see it.
Fergal Byrne
Chief Operating Officer · Logistics and supply chain group, Waterford
Tested: Value realisation and executive reporting
Alpha participants are named with permission. Organisation names are withheld under the confidentiality terms of the alpha programme.
Where it is used
Regulated environments, treated as first-class.
Screening logic, review routes and evidence expectations reflect the obligations of the sector, not a generic risk template.
- EU AI Act
- NIST AI RMF
- ISO/IEC 42001
- GDPR / DPIA
- EU GMP Annex 11
- SOC 2 / ISO 27001
- OWASP GenAI guidance
Framework references describe design alignment and evidence support. They do not represent certification, legal advice or a guarantee of compliance.
- Pharma, Biotech and MedtechQuality, validation and inspection readiness shape how AI can be adopted in regulated manufacturing, laboratory and clinical support environments.
- Financial Services and InsuranceModel inventories, customer impact and third-party oversight require consistent risk ownership and decision history.
- HealthcareClinical workflow, sensitive data and human oversight obligations demand careful assessment and traceable accountability.
- Critical InfrastructureOperational technology, write-back access and resilience obligations change the risk profile of AI-enabled capability.
Register your interest in AIRAS Cloud
Tell us about your organisation, your governance obligations and the next step that would be most useful. A named member of the team will respond directly.
No pricing commitment. No confidential information required.