For a long time, governance in the context of AI was seen as a necessary evil. Today, however, the picture is different: responsible AI is increasingly becoming a strategic success factor. Companies that
AI models are interchangeable, but their effects are not. Whether a system poses a low or high risk does not depend on the technology, but on the intended use.
Many risks do not arise in the cloud, but in the inventory. ITAsMa shows where systems are running, what data they process and when they were last checked.
From chatbots to scoring: every AI in a company needs measurable responsibility, clear evidence and a robust process. Those who take a structured approach now will protect data, brand value and
At the latest when companies implement several parallel AI projects, a common basis for evaluation is essential. Otherwise, different specialist areas, models and application types can hardly be compared with each other. A central
Model maps make AI comprehensible. They document the purpose, data origin, versions, performance metrics and limits of AI models. Those who use them consistently and integrate them into workflows gain auditability and
While the use of AI in companies is increasing rapidly, the development of structures for responsible AI and its management is lagging behind. Several recent studies show
Whether for texts, code or translations: AI tools have long since arrived in everyday working life. However, many AI tools are used without the departments responsible for IT, data protection
The GDPR and the AI Regulation not only oblige companies to comply with regulatory requirements, but also to prove their compliance. This proof is provided via various documentation obligations.
The EU Data Act brings a decisive innovation for the European data market: providers of cloud and data processing services are obliged to actively inform their customers when switching providers.