Compliant AI for regulated businesses

Viking designs, implements and governs AI solutions that respect your regulatory obligations, so boards and regulators can rely on the outcomes as much as your teams do.

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AI that fits existing controls

Financial services, insurance, healthcare and gaming ready

Viking’s approach is tuned to the realities of regulated operations, where directors carry accountability and regulators expect robust governance over every model in production.

Financial Services

Support for Financial Supervision regulations, including explainable models, strong oversight and minimised concentration risk on single AI providers.

Healthcare

Protection of sensitive health and patient data, strict access control and auditable use of generative AI for triage, documentation and support tasks.

Gaming

Controls to prevent leakage of player data, manage AML and safer-gambling workflows, and evidence compliance during licence reviews.

Board-Level Comfort

Clear documentation, risk assessments and governance workflows designed to help directors fulfil their duties when AI is embedded in critical processes.

Governance first, models second

An architecture built for regulation

Viking combines its KONI platform with IBM watsonx to deliver AI that is controlled, monitored and explainable across the full lifecycle - from data to decisions.

Controlled Infrastructure

AI workflows are deployed on Viking’s open-source platform and IBM watsonx in environments aligned to your data residency, networking and security requirements.

Data Protection & Privacy

Solutions are designed to keep sensitive data within governed stores, using least-privilege access, encryption and configurable retention, reducing GDPR and confidentiality risk.

Model Governance

IBM watsonx.governance provides model inventories, policy enforcement, risk scoring and monitoring to align with frameworks such as EU AI Act, ISO 42001 and SR 11‑7.

Maximum Auditability

Logging of prompts, outputs, data sources and model versions enables robust audit trails for internal assurance, incident investigations and supervisory reviews.

Powerful, but rarely governed

The problem with “off-the-shelf” AI tools

Public AI tools are not designed for regulated businesses: they blur data boundaries, lack governance, and make it hard to prove compliance when regulators start asking detailed questions.

Exposure & Residency

Consumer AI tools often send prompts and content to external clouds, with unclear data retention, training and residency rules that conflict with GDPR and supervision frameworks.

Opaque Models

Many services provide no meaningful explanation of how outputs are created, making it difficult to meet model risk guidelines and AI transparency requirements.

Shadow AI

Staff can adopt tools without approval, creating unmanaged channels for sensitive customer and patient data, breaching internal policies and often the law.

No Regulatory Alignment

Generic AI does not align with frameworks such as EU AI Act, ISO 42001 or local supervisory guidance, leaving compliance teams to retrofit controls themselves.

From assessment to assurance

Working with your risk and compliance teams

Every AI engagement is structured around your existing policies, risk appetite and regulatory obligations, with compliance built in from scoping to ongoing monitoring.

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Regulatory & Policy Review

Map relevant regulations, internal policies and data classifications to candidate AI use cases, highlighting where AI is not yet appropriate.

Architecture & Control Design

Define where data lives, which models are used, what guardrails apply, and how monitoring and reporting will work.

Controlled Pilot & Testing

Run proof-of-value in a constrained environment with pre-agreed success metrics, risk controls and rollback options.

Production Rollout with Governance

Integrate with identity, logging and change control processes; hand over documentation and dashboards for ongoing oversight.

Closing the gap between use and oversight

How AI is adopted vs what regulators expect

Most organisations start using AI in an ad‑hoc way, but regulators assess it as critical infrastructure - this view makes the differences visible and shows where Viking’s approach closes the gaps.

Business Area

Usual AI adoption

What regulators expect

What Viking AI does

Strategy & Ownership

Individual teams experiment with public AI tools without a clear strategy, risk appetite or accountable owner.

A documented AI strategy, and clearly assigned responsibility for AI outcomes at senior management and board level.

Work with stakeholders to define an AI roadmap and ownership model, positioning AI as governed infrastructure rather than ad‑hoc experimentation.

Tool Selection

Convenience-led: staff choose whatever AI tools are easiest to access, often via consumer accounts and freemium tiers.

Formal due diligence, procurement and third‑party risk assessment covering security, privacy, resilience and regulatory obligations.

Standardised on KONI platform and IBM watsonx, with vendor and architecture review so approved components are reused across use cases.

Data Handling & Residency

Sensitive data is pasted into external chatbots with unclear storage, training and geographic processing locations.

Data classification, residency and transfer rules applied up front, with controls to prevent unauthorised upload of regulated or personal data.

Data stays in governed stores, enforces residency and access rules, and can be deployed on‑prem or in selected regions to meet sector regulations.

Governance & Policies

Little or no AI usage policy; “shadow AI” grows faster than the ability to monitor or manage it.

Clear AI policies defining approved tools, prohibited uses and escalation paths, embedded into compliance and IT security.

Helps operationalise AI usage policies, then implements technical guardrails on the KONI or watsonx to enforce them.

Model Risk & Explainability

Black‑box models are used because they work, with limited documentation of training data, limitations or failure modes.

Model inventories, risk classification and testing, with documented limitations and evidence that outputs are appropriate for the use case.

Register models, capture lineage and limitations, and design solutions that provide traceable sources and rationales for outputs.

Monitoring & Audit

Minimal to no logging of prompts, outputs or changes; difficult to reconstruct what happened when something goes wrong.

Comprehensive logging, versioning and monitoring to evidence proper use, investigate incidents and demonstrate control to regulators.

Configure log queries, responses, data sources and configuration changes, providing auditable trails for internal use or regulators.

Regulatory Alignment

Controls are retrofitted after pilots, often only when internal audit or regulators start asking questions.

Proactive mapping of AI systems to regimes such as GDPR, EU/UK AI rules and sector guidance.

Starts each project with a regulatory lens, using IBM and Viking accelerators to map obligations to controls in the reference architecture.

Change & Lifecycle Management

New models or prompts are deployed informally, outside change management, with no structured review of downstream impacts.

AI changes follow established change control, with pre‑deployment assessment, sign‑off, post‑launch review and periodic re‑validation.

Integrate AI changes into IT and risk processes, with recorded test plans, approvals and re-certification cycles for workflows and models.

Ready to transform your business with secure AI?

Book an intro call to see how Viking AI can help your organisation unlock the power of AI while staying fully compliant.

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