‍AI Governance describes all rules, roles, processes and controls with which companies use AI systems securely, legally and economically. It is therefore not just about technology, but about responsibility, risk, data protection, security and measurable benefits.
Many companies start with a pilot and only realize later that “a good result” in the test does not automatically mean that the company is stable, secure and auditable. This is exactly where AI governance comes in.
The added value is very concrete: less friction in implementation, faster decisions in projects and fewer surprises during data protection, security or subsequent audits.
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AI Governance: What it is and what it isn't
KI Governance is a management system for AI, comparable to what ISO standards do for information security or quality management. It defines guardrails without slowing down innovation.
AI governance is not synonymous with “we write a policy and that's it.” In practice, it also includes processes for procurement, approvals, testing, incident handling, changes and training.
The distinction is important: Data governance regulates data. IT governance governs IT. AI governance combines both and complements AI-specific topics such as model risks, hallucinations, bias, drift, prompting, output control and human supervision.
For managers, this means that AI governance is a management tool that brings risks and benefits into a common, decidable format.
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Which external regulations companies should be aware of
For companies in Austria and the EU, the EU AI Act is the central reference framework because it regulates AI on a risk-based basis and triggers obligations depending on the area of application. (EUR-Lex)
In addition, the GDPR remains relevant as soon as personal data is processed. In practice, AI governance is therefore almost always also data protection governance, including legal basis, transparency, purpose limitation and order processing.
International guidelines, such as the OECD AI Principles, which aim at trustworthy and human-centered AI, are helpful for orientation beyond pure legal requirements. (OECD)
And because AI always touches IT and security, established security management such as ISO/IEC 27001 is an important component, even if your AI project starts small. (ISO)
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Placing EU AI Act obligations in practice
The EU AI Act does not apply equally to all AI applications. Among other things, it distinguishes between prohibited practices, high-risk systems and systems subject to transparency, and it gradually rolls out obligations. (EUR-Lex)
For many companies, it is particularly relevant that the Act also addresses requirements for “AI literacy”, i.e. AI competence in the company. This is not just a training project, but part of governance, because employees must know how to interpret and verify AI results. (Artificial Intelligence Act EU)
Also important: According to EU requirements, rules for general purpose AI models apply from a fixed date, and the EU works with Codes of Practice as a bridge until standards take effect across the board. (Digital Strategy Europe)
For your AI governance setup, this means that you need a system that classifies use cases based on risk, derives obligations from them and verifiably documents these obligations.
AI Governance: ISO standards as a management system for AI
If you want to set up AI governance in a structured way, ISO standards provide a very practical foundation because they are intended as a management system.
ISO/IEC 42001 is a standard for an artificial intelligence management system and describes requirements for systematically establishing, operating and continuously improving AI. (ISO)
ISO/IEC 38507 is aimed explicitly at management bodies and describes governance implications when using AI in organizations. This is particularly valuable because it brings together the perspective of the board, management and supervision. (ISO)
ISO/IEC 23894 is relevant for AI risk management because it provides guidance on how organizations can manage AI specific risks. (ISO)
These standards do not replace legal advice, but they give you a robust framework to set up AI governance not ad hoc, but comprehensible and auditable.
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NIST AI RMF and OECD principles as guidelines
In addition to ISO, frameworks are used in many organizations to implement AI governance “hands on”.
The NIST AI Risk Management Framework is a voluntary framework that helps organizations identify, assess, and reduce risks across the AI life cycle. (NIST)
It is practical because it not only looks at technology, but also governance, measurability and organizational responsibility. This creates a common point of reference for IT, compliance and specialist areas.
The OECD AI Principles are not a checklist, but a good guideline for values, transparency, robustness and accountability. They help in particular if you want to formulate internal principles that apply regardless of the respective tool. (OECD)
A proven approach is: OECD principles for “What do we stand for”, NIST AI RMF for “How do we manage risks”, ISO/IEC 42001 for “How do we anchor this as a management system.”
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AI Governance: Which internal guidelines should companies implement
AI governance only becomes effective in everyday life when it is translated into concrete internal regulations. These components have proven effective in practice.
First, an AI policy that allows and prohibits, for example handling sensitive data, use of external tools, approval requirements and documentation requirements.
Second, an AI use case intake process, i.e. a standardized form plus decision-making body that evaluates new use cases: benefits, data, risk, affected groups of people, security requirements.
Thirdly, a model and tool register, i.e. an inventory: which models, which providers, which training data, which interfaces, which responsible persons, which versions.
Fourthly, a testing and evaluation standard: defined quality metrics, red teaming for critical systems, acceptance tests in the department, and a process for borderline cases.
Fifthly, a human oversight concept: when does a person have to check, how is escalated, how are incorrect decisions documented, and how is the team trained.
Sixthly, an incident and change process: What happens in the event of a data leak, incorrect output, security incident, model drift or provider change.
So that AI governance is not just paper, these rules should be visible in tooling and workflows, for example via approval steps in the ticket system or policies in the access system.
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Establish data protection and roles neatly
As soon as AI processes personal data, data protection is part of AI governance, not a downstream check. Data protection officers and security should therefore be involved early on.
It is helpful to clearly define internal roles: process owner in the department, product owner for the AI solution, IT for operation and integration, security for protective measures, data protection for legal bases and transparency.
External guidance is also useful: The European Data Protection Board publishes documents on the interface between AI models and data protection, which can help to assess data processing and risks. (EDPB)
In practice, a common stumbling block is the unclear data line: Which data is allowed into an external model? Is data stored? Who is a contract processor? AI governance must answer these questions in a binding manner.
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AI Governance: Think about security and cyber resilience
AI solutions are software systems. This means that classic security requirements continue to apply, plus AI-specific risks such as prompt injection, data outflow via outputs or manipulation of input data.
An established ISMS in accordance with ISO/IEC 27001 helps to address these topics in a structured way because it pours risk, controls and continuous improvement into processes. (ISO)
At EU level, ENISA regularly provides situation views and recommendations on cybersecurity in the Union, which are also relevant for AI projects because AI is increasingly supporting critical business processes. (ENISA)
For AI Governance, this means that security requirements belong to the architecture decision, the provider evaluation and the operating model, not just in acceptance.
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Don't overlook industry regulations and supervision
Depending on the sector, additional requirements apply. Insurance companies, for example, are subject to horizontal regulation as well as sector-specific rules, and supervisory authorities issue additional expectations for governance and risk management. (Eiopa)
Such documents are helpful in AI governance because they specify which controls supervisors expect, for example in model validation, governance structures or traceability.
Even if you're not regulated, it's worth taking a look: Industry guidelines often show which minimum standards become “state of practice.”
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AI Governance: A Pragmatic Roadmap in 5 Steps
AI governance doesn't have to start with a major project. A pragmatic structure is often achieved in five steps.
Step one: Define scope. Which AI categories do you use, generative AI, classic ML models, or both?
Step two: Define roles and committees. Who decides use cases, who is responsible for operations, who reviews risks?
Step three: Create core rules. AI policy, use case intake, tool register, testing standard, incident process.
Step four: Pilot and learn. Start with two to three use cases, measure quality and effort, and iteratively improve the regulations.
Step five: Scale. Only when standards take effect is it worthwhile to roll out wider, because then every new use case creates less friction.
A good sign of effective AI governance is that new projects start faster but are better documented at the same time.
AI Governance: Common Questions from Companies
What is AI governance in one sentence?
AI governance is the system of rules, roles and controls that ensures that AI is used in a company responsibly, legally compliant and economically.
Which regulations are most important for AI governance?
For many EU companies, the EU AI Act and GDPR are the most important legal points of reference. ISO/IEC 42001 is very helpful as a management system and ISO/IEC 38507 as a governance guide. NIST AI RMF and ISO/IEC 23894 are good building blocks for risk management. (EUR-Lex)
What does AI literacy mean in the context of AI governance?
AI literacy describes AI competence in companies so that employees can correctly classify, review and use AI results responsibly. The EU AI Act addresses this issue explicitly, making training and guidelines part of governance. (Artificial Intelligence Act EU)
Does every company have to implement ISO/IEC 42001?
Not necessarily. ISO/IEC 42001 is particularly useful if you use AI broadly, need to be auditable, or want to transfer governance into a formal management system. Many companies start pragmatically with internal policies and later orient themselves to ISO/IEC 42001. (ISO)
How do I prevent AI governance from slowing down innovation?
By managing on a risk-based basis: simple processes for low-risk use cases, stricter controls for high-risk. This allows you to get started quickly without neglecting governance.
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AI Governance: Your Next Step in Practice
AI governance is good when it makes decisions easier: What can be tested, what needs approval, which data is allowed, and how do we measure success and risk?
If you want to start now, take a specific use case, define data and KPIs, and therefore build your first governance building blocks. This is how AI governance is created based on real work, not as a theory project.
The KI Company helps companies set up AI governance pragmatically and at the same time design use cases in such a way that they can be operated productively and responsibly.
Which AI application currently causes you the most questions about responsibility, data, or risk? This is usually the best starting point.
