Companies building AI products
Customers, investors and regulators increasingly ask for proof that AI features are built in a controlled, documented way.
/ AI GOVERNANCE
AI Governance
AI is already running across most organizations, often without anyone having signed it off. Qyntar's AI governance service brings order: we find out where AI is used, classify it by risk and build a governance framework that meets the EU AI Act, ISO/IEC 42001 and customer expectations without slowing innovation down.
[ 01 ] / WHAT IT IS
What is AI governance, and why isn't an AI policy enough?
In short
AI governance is the set of rules, roles, processes and controls that make sure an organization builds, buys and uses artificial intelligence responsibly, transparently and under control. Qyntar builds that system end to end, from inventory through risk management to monitoring, and prepares you for the EU AI Act, ISO/IEC 42001 and customer requirements. This is not legal advice.
AI governance is not a single document but a living management system. It settles who can approve an AI use case, what data it may touch, how risk is assessed, who answers for the outcome and how the system is watched once it is live. A policy on its own does not capture reality: employees use AI tools nobody approved (shadow AI), suppliers add AI features to their products, developers plug in third-party models, and in the meantime sensitive data, copyright and personal data are exposed.
The regulatory picture is moving quickly too. The EU AI Act is risk-based: some AI practices are banned, high-risk systems face strict requirements, certain systems carry transparency duties and general-purpose models have their own rules. The rules on prohibited practices and AI literacy have applied since February 2025, the general-purpose model rules since August 2025, and the transparency obligations apply from 2 August 2026. The 2026 AI omnibus amendment pushed the high-risk requirements back (to 2 December 2027 for stand-alone systems and 2 August 2028 for AI built into regulated products). Always check deadlines and scope against the legislation in force.
ISO/IEC 42001 is the international standard for an AI management system, and a governance framework can be built on it organizationally, while ISO/IEC 23894 and the NIST AI RMF support the risk management side. The service links closely to secure AI architecture and AI Red Teaming, which test whether the controls actually work. Official certification is issued by an accredited certification body.
Regulation and frameworks
- EU AI Act
- ISO/IEC 42001
- ISO/IEC 23894
- NIST AI RMF
- NIST AI RMF Generative AI Profile
- GDPR
- OWASP Top 10 for LLM Applications
- NIS2 / DORA (ICT risk)
[ 02 ] / WHO IT'S FOR
Who benefits from AI governance?
Any organization that builds, buys or uses AI and wants to know what is running, with what risk, and who is accountable for it.
Enterprises buying and using AI
Copilots, chatbots, decision-support models: from procurement to daily use, someone has to say what is allowed and who signs it off.
Financial services firms
Model risk, ICT risk management under DORA and the AI Act arrive together, for example for credit scoring or fraud detection models.
Healthcare and medical technology players
For medical devices containing AI, the AI Act and medical device regulation requirements have to be handled together.
Suppliers whose customers ask for AI assurance
Customer security and AI questionnaires keep getting more detailed. Structured governance makes answers faster and more credible.
Executives and compliance teams
People preparing for regulation who want to know what applies to them, what to do now and what can wait.
[ 03 ] / WHAT'S INCLUDED
What our AI governance service covers
We scale the scope to your size, how deeply you use AI and your regulatory exposure. The following elements are typical.
01
AI inventory and shadow AI discovery
We map where the organization uses AI: in-house systems, supplier features, SaaS tools and unapproved applications that employees use.
02
Risk classification
We sort each system by the AI Act categories (prohibited, high-risk, transparency obligations, minimal risk) and your own risk criteria, and establish your role as provider or deployer.
03
Governance framework and roles
Decision rights, an AI oversight body, named owners and escalation paths, so it is always clear who decides and who is accountable.
04
Policies and acceptable use
A practical AI policy, acceptable-use guidelines for staff, and rules for data handling and copyright.
05
Risk management and impact assessment
An AI risk assessment method and, where needed, impact assessments (such as a data protection impact assessment and a fundamental rights impact assessment), with treatment plans.
06
Procurement and third-party AI risk
An AI procurement checklist, supplier assessment, contractual security and data terms, and oversight of external models and services.
07
Lifecycle controls and monitoring
Data quality, model evaluation, change management, performance and bias monitoring, logging and AI incident handling from deployment through operation.
08
ISO/IEC 42001 readiness and awareness
A gap analysis against ISO/IEC 42001, evidence collection and awareness building for leaders and staff. For hands-on skills, see our cybersecurity training.
[ 04 ] / HOW IT WORKS
How an AI governance program is built
A pragmatic, risk-based approach: we deal first with what carries the greatest risk and the nearest deadline.
Starting point and scope
We learn your business goals, how AI is used, your regulatory exposure and the governance elements you already have.
AI inventory
Through interviews, procurement and network data and a look at development environments, we find the AI systems in use, shadow AI included.
Risk classification and gap analysis
We classify the systems and measure them against regulatory and standards requirements to see what is missing.
Building the framework
Together we design the governance structure, policies, approval workflows and risk method.
Rollout and training
We embed the processes in development, procurement and operations, and prepare leaders and staff.
Verification and sustainment
We set up an annual review cycle, metrics and, where needed, technical verification such as an LLM penetration test.
[ 05 ] / WHAT IT SOLVES
What AI governance solves
The service makes it clear where and how your organization uses AI, and removes the uncertainty around regulatory expectations. Leadership knows what it has approved, and developers and staff know exactly what the rules are, so AI adoption gets faster and safer, not slower.
It also gives customers, partners, auditors and regulators evidence: documented risk management, named owners and verifiable processes show that AI is run in a grown-up way.
- a full picture of where and which AI runs in the organization
- AI systems classified by risk and managed accordingly
- clear accountability and approval processes
- readiness for the EU AI Act and ISO/IEC 42001
- credible answers to customers' AI questions
What you receive
- an AI inventory and shadow AI report
- a risk classification with a regulatory map
- a governance framework, roles and approval workflows
- an AI policy and acceptable-use guideline
- a risk management method and templates
- a scheduled action plan and audit evidence
[ 06 ] / WHY QYNTAR
Why work with Qyntar on AI governance?
Because AI governance is a regulatory, executive and technical challenge all at once, and we know all three sides.
Real CISO experience
Executive experience in a regulated sector means we know how to turn requirements into decisions, budgets and daily operations.
Technical verification
We do not just write controls down. With an attacker's experience we test them too, for example through AI Red Teaming.
Proportionate by design
Our risk-based approach avoids an over-engineered, formal system and focuses on what actually matters in the way you use AI.
One view of the rules
We handle the AI Act, GDPR, NIS2 and DORA in a single control system, so the work is not done more than once.
[ 07 ] / FAQ
Frequently asked questions: AI Governance
What is AI governance?
The rules, roles, processes and controls that make sure an organization builds, buys and uses AI responsibly and under control. It runs from inventory through risk management to monitoring.
Does the EU AI Act apply to us?
If you develop, place on the market or use AI systems in the EU, probably yes, but how much depends on your role (provider or deployer) and each system's risk class. An AI inventory and classification settle this. Always check deadlines and details against the legislation in force. This is not legal advice.
What is ISO/IEC 42001, and will you certify us?
It is the international standard for AI management systems. Qyntar provides readiness work, gap analysis and pre-certification verification. Official certificates are issued by an accredited certification body.
What is shadow AI, and why is it a risk?
AI tools that employees use without approval. The risk is that confidential or personal data reaches an outside provider, copyright and compliance problems appear, and the organization does not even know its full exposure. An AI inventory and acceptable-use rules address this.
How does it relate to GDPR and NIS2?
AI systems often process personal data (GDPR) and form part of ICT risk management (NIS2, DORA). We handle them in one control system so requirements are not met several times over. See also NIS2 compliance.
Will a governance framework slow down AI adoption?
Not if it is designed well. Clear rules and fast, risk-tiered approval are exactly what let teams roll out AI solutions with confidence and without endless debate.
How long does an AI governance project take?
It depends on your size and how deeply you use AI. An AI inventory and gap analysis take a few weeks, and rolling out the full framework typically takes a few months. We give a firm plan after the initial assessment.
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[ + ] / CONTACT
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