AI Governance
Responsible, traceable AI - oriented to European standards for data protection and trustworthy AI. From the architecture, not as a downstream check.
Stance
AI governance is first a leadership question, not a technical one. And leadership doesn't respond with bans - a ban is the admission that you've stopped steering. Leadership sets the direction: clear guardrails, enablement and training, plus vetted solutions people are actually allowed to use.
Where leadership stays silent, a vacuum forms. Then every department finds its own way, every team picks its own tool - and business-critical data flows, uncontrolled, into third-party services. Not out of ill will, but because no one offered the better path. Once it has leaked, no one gets it back.
Good governance fills that vacuum - not with bans, but with offerings. It states clearly what's allowed, makes the safe path the easy path, and gives people the means and the knowledge to use AI well.
What we mean by it
Governance isn't a document in a drawer, it's the interplay of five building blocks. Only together do they create control instead of standstill - and a tailwind instead of friction.
Why governance
Your people already use AI - the only question is whether it's under control or in a private browser tab. A blanket ban just moves the risk to where no one is looking.
And that's exactly where it gets dangerous: without clear rules, confidential information - contracts, design data, customer data - ends up in third-party AI services, uncontrolled, somewhere in the world. Once it has leaked, no one gets it back.
Good governance doesn't put on the brakes, it enables. It gives clear answers instead of uncertainty: sensible rules and limits - and, above all, vetted AI and vetted, preferably local language models firmly in the foreground. So your teams work with a tailwind, not in the shadows.
No model without a review. Developers don't wire just any LLM into new or existing tools because it happens to be convenient. Every model that goes into products and processes passes an honest assessment first:
How secure is the model itself - data leakage, attack surface, supply chain?
Where does the model make things up - and how do we catch that in operation?
What distortions does it bring, and are they tolerable for the use case?
Where does the model come from, what's inside it, whom do we trust and why?
Only once a model passes these questions does it earn trust - and a place in your landscape. Where it comes to the security of the models themselves, this meshes closely with our Cybersecurity.
The real bottleneck
Most organisations already have guidelines, the first governance structures, perhaps a policy document. The sticking point isn't the paper - it's the step into reality: which AI is actually in use, on what data, with what permissions? That's where control is decided.
Questions that often go unanswered today:
Which AI systems are actually in use?
What data is processed, which models are used?
Which APIs and tools have access?
How are agentic-AI systems controlled?
How are risks assessed and approved?
How is it monitored and traced in operation?
Our approach
AI interprets results but doesn't plan its own chains of calls. People decide.
Access via existing identities and permissions - no one goes beyond their own rights.
Who, when, what, on what basis - completely traceable, centrally logged.
AI on European infrastructure. Your data stays in Europe.
Services
No tool, no licence model - solid methodology. We assess real AI landscapes, define controls and anchor steering where it works: in architecture, operations and the organisation. Governance, enterprise architecture and steering complex landscapes have always been our craft - AI governance is the logical continuation, not a new label.
Quick clarity on maturity, usage and risks - a solid starting point.
Where governance, architecture and security converge - our differentiator.
Governance as part of the operating model, platform and lifecycle - at group level.
In the works: governance automation (controls, monitoring, evidence collection), AI red teaming & advanced AI security, and managed services.
Orientation
We design AI governance along the relevant European frameworks - by design, not after the fact.
ISO/IEC 42001 is the backbone here: the international standard for AI management systems. It mainly describes the organisational layer - management system, roles, processes, documentation, risk management. Technical operationalisation - architecture, data flows, APIs, agentic AI, security - begins where the standard ends. That's exactly the bridge we build.
Certified to ISO 9001. We don't orient ourselves by ISO/IEC 27001 and ISO/IEC 42001 on paper alone: we already work to these standards - processes, training and responsibilities are in place, and an AI management system (AIMS) is in operation. The formal certifications are in preparation.*
*An attestation confirms a management system already in practice - the work to that standard begins before the certificate, not with it. Security and governance have been our baseline from the start, not an afterthought.
The remaining framework references (EU AI Act, EU Data Act) serve to position the solution design - not as proof already achieved.
End-to-end
Governance target picture and guardrails, architecture and implementation, identity and access concepts, gapless auditability in operation - all from one source.
Clarity in governance, too. Not just in the data.
Who's behind it
Our AI governance is owned by Yavuz Yıldız - Founder and Managing Director of Xient. For him, responsible AI is a matter for the top: not a ban, but a clear framework that makes AI accountable and secure - carried by architecture, processes and evidence that hold up in operation.
Yavuz Yıldız · Founder & Managing Director
In practice
The three fit together - depending on where you stand. Answer the question that matches you.
"Which language models can we even trust?"
A continuously maintained catalogue of vetted language models with a Trust Score. You pick the right model - and apply your own security policies on top.
Ideal when you want to adopt AI but first need clarity on which models you can trust.
Xient Trusted LLM →"How do we keep our data entirely in-house?"
The chosen model runs locally on your own hardware - advised by us, tuned to your purpose and securely operated. As much local as possible, as little cloud as necessary.
Ideal when data sovereignty comes first and as little as possible should leave the house.
Xient Local LLM →"How do we use our SAP knowledge safely via AI?"
Governed AI on your vetted SAP data - in natural language inside Microsoft Teams, read-only first, curated and fully auditable.
Ideal when you want to make existing SAP knowledge accessible without giving up control.
Xient Bridge →Where governance meets confidentiality and protection, the path continues to Cybersecurity.
We'll show you how governance, security and impact work together - on your topics.
Book a slot with Oliver Schwenteck directly.