Data protection and data security
Posted: Sun Feb 02, 2025 5:09 am
challenges and solutions
Although there are already some solutions and options on the market, you should also think about your individual challenges if you want to launch an in-house ChatGPT.
Challenge 1:
Using GPT models carries the risk of exposing sensitive company data. This is especially relevant in industries with strict data protection regulations. Despite the benefits, privacy is an important consideration. ChatGPT uses the input data and interactions to train their models, which carries risks to sensitive company data.
Solution : Use of cloud services such as Microsoft Azure , which are specifically designed for the European market and guarantee compliance with the GDPR. In addition, data encryption should be ensured both during transmission and storage. These are precisely the solutions we use at Spekto .
Challenge 2: Integration into existing systems
Many companies already have complex IT infrastructures honduras number dataset into which new technologies such as GPT must be integrated. You also have to think about which systems you can and want to connect to the GPT technology via which interfaces.
Solution : Collaboration with AI experts who have experience in integrating AI solutions into existing systems. This can be done by adapting interfaces or developing customized middleware. To start with, however, it is helpful to think about your own data and evaluate which systems should actually communicate with the company's own ChatGPT.
Challenge 3: Need for expertise and training
Depending on how extensive such an in-house AI solution becomes, employees may not be familiar with the functions and possibilities, which can lead to suboptimal use and low adoption.
Solution : Provide the most relevant training programs that cover both the technical aspects of GPT and its practical application in the corporate context. It can be helpful to train managers and key employees as multipliers who pass on their knowledge internally (keyword AI champions)
By addressing these challenges through targeted solutions, companies can successfully master the introduction of their own CompanyGPT and exploit its full potential.
Although there are already some solutions and options on the market, you should also think about your individual challenges if you want to launch an in-house ChatGPT.
Challenge 1:
Using GPT models carries the risk of exposing sensitive company data. This is especially relevant in industries with strict data protection regulations. Despite the benefits, privacy is an important consideration. ChatGPT uses the input data and interactions to train their models, which carries risks to sensitive company data.
Solution : Use of cloud services such as Microsoft Azure , which are specifically designed for the European market and guarantee compliance with the GDPR. In addition, data encryption should be ensured both during transmission and storage. These are precisely the solutions we use at Spekto .
Challenge 2: Integration into existing systems
Many companies already have complex IT infrastructures honduras number dataset into which new technologies such as GPT must be integrated. You also have to think about which systems you can and want to connect to the GPT technology via which interfaces.
Solution : Collaboration with AI experts who have experience in integrating AI solutions into existing systems. This can be done by adapting interfaces or developing customized middleware. To start with, however, it is helpful to think about your own data and evaluate which systems should actually communicate with the company's own ChatGPT.
Challenge 3: Need for expertise and training
Depending on how extensive such an in-house AI solution becomes, employees may not be familiar with the functions and possibilities, which can lead to suboptimal use and low adoption.
Solution : Provide the most relevant training programs that cover both the technical aspects of GPT and its practical application in the corporate context. It can be helpful to train managers and key employees as multipliers who pass on their knowledge internally (keyword AI champions)
By addressing these challenges through targeted solutions, companies can successfully master the introduction of their own CompanyGPT and exploit its full potential.