AI Governance
Set the policies and controls that govern AI, and scale with the right guardrails
From ungoverned use of AI to the safety of proper guardrails for your teams
We help you to:
Map where AI is already used across your organisation
Set policies and standards your teams can actually follow
Control model access, permissions, and how data is handled
Put guardrails, audit trails, and monitoring into production
Embed governance into delivery and keep it current
AI Governance Services
AI Governance Readiness Assessment
A structured review of how AI is used across your teams, tools, and data today, showing what needs to be prioritised.
Policy & Standards Development
Clear, usable policies covering acceptable use, data handling, model selection, and workflow integration to ensure consistent scaling.
Responsible AI Frameworks
Ethical AI practice made operational: bias mitigation, red-teaming, content restrictions, and transparent reporting.
Security & Access Governance
Identity, access, and permissions configured so teams can use models safely across internal systems, customer data, and third-party tools.
Risk, Controls & Compliance
Practical controls against data leakage, misuse, bias, and regulatory exposure, aligned to the assurance requirements of your sector.
Operational Oversight & Guardrails
Model choices, prompt standards, audit trails, fallbacks, and monitoring that keep teams moving quickly.
Regulatory Readiness
Your policies, documentation, access controls, and incident workflows mapped to recognised AI governance standards.
Governance Implementation & Enablement
We configure IAM and logging, apply model usage controls, embed governance into your SDLC, and train your teams.
Governance has to scale as fast as adoption does.
Our ATOM framework makes sure it can.
ATOM
Zartis AI Trust Operating Model
Our proprietary AI governance framework, built to extend your organisation's existing controls to Claude at enterprise scale.
ATOM is built towards ISO/IEC 42001, mapped to whatever governance model your organisation already runs, and applied wave by wave rather than signed off once at the end.
Get access to a framework that keeps every wave accountable, controlled and evidenced as adoption scales past a single pilot.
What you get:
- Named accountable owners across board, risk and delivery
- Compliance review mapped to ISO/IEC 42001
- Full audit trail: policy, risk register, system inventory, impact assessments
- Continuous change management stream as usage spreads
Use cases
In action
What good governance makes possible across your teams
FAQs: AI Governance
What is included in AI Governance?
AI Governance covers the structures, controls, and practices your organisation needs to use AI safely and responsibly. That includes usage policies, guardrails, acceptable-use standards, model access controls, and the processes teams follow when building with or using AI. We design the framework around how your business actually operates: your sector, your risk profile, your data sensitivity, and the types of AI your teams touch. It spans people, processes, and technology, covering who can use what, how data is handled, which models are approved, and how usage is monitored over time.
Is this only relevant if we are in a regulated industry?
No, although regulated sectors tend to feel the urgency sooner. Governance matters for any organisation using AI beyond isolated experiments. Once several teams are using LLMs, assistants, or automated workflows, someone has to be able to answer what is running, against which data, under whose approval. Unregulated companies still face data leakage, unvetted model usage, biased outputs, and unmanaged agent behaviour in production.
How is this different from AI Alignment?
AI Alignment answers what you should do with AI: where to invest, which use cases matter, and who owns each decision. AI Governance answers how that gets done safely: the policies, controls, and guardrails that sit around the work. Most organisations need both, and they are usually sequenced, with alignment setting direction and governance making it operable. If you are unsure which you need first, a scoping call will normally settle it in one conversation.
Do you align with recognised AI governance standards?
Yes. We work to the established AI governance principles and control frameworks that are becoming the global reference point, and we help you interpret what they mean for your systems rather than leaving you with the standard itself. That covers aligning your usage policies, access controls, documentation, model oversight, and incident workflows to a structure an auditor will recognise. The aim is that audit-readiness is a by-product of how you already work, not a project you run later under pressure.
Will governance slow our teams down?
Not when it is built properly. Poor governance slows delivery, because every new use case becomes an open question. Good governance removes the ambiguity: teams know the approved tools, the security rules, and the safe workflows, so they move faster with fewer mistakes. It also prevents the most expensive kind of rework, which is an AI initiative that has to be redesigned or withdrawn after a risk surfaces late.
Can you implement the controls, or do you only define them?
Both, and most clients need both. We implement guardrails in your infrastructure, apply IAM policies for AI tools, configure logging and audit trails, integrate model usage controls, and embed governance into your SDLC or cloud workflows. We also support rollout through documentation, training, and adoption workshops. Governance only works once it is operationalised, which is why the definition and the implementation sit in the same engagement.
Could our existing security and compliance teams do this in-house?
Often partly, and where that is true we say so. The gap is usually not compliance capability, it is knowing how AI systems fail: non-deterministic outputs, prompt injection, context leakage between tools, and agent behaviour that no existing control framework anticipates. We bring that engineering perspective and work alongside your internal subject matter experts rather than replacing them. In practice the strongest outcome comes from upskilling your team where necessary and supporting them with our delivery experience of AI in production.
What happens once the engagement ends?
You keep the framework, the documentation, and the controls, and your teams own them. We build governance to be run internally, which means naming owners, writing standards in language your teams use, and training the people responsible for applying them. Where useful, we stay involved through periodic reviews as regulation changes or as new AI capability enters your stack. The measure of success is that governance still works after we leave.