Glossary
/ Data security

Data security

Data security refers to the protection of data from unauthorized access, use, disclosure, destruction, modification, or loss. This is crucial in today's world, where data is considered one of the most valuable resources. Companies, organizations, and individuals rely on various security measures and protocols to protect their data from threats and security breaches.

In relation to artificial intelligence (AI), data security gains additional importance. AI systems often process, analyze, and store large amounts of sensitive and personal data. In doing so, they can become both a security solution and a potential security risk:

  1. AI as a security tool: AI can be used to detect anomalies and predict security breaches. Machine learning can recognize patterns in network traffic and identify unusual activities that may indicate a possible attack.
  2. Security risks through AI: On the other hand, AI models can become targets of attacks. For example, there are "adversarial attacks" where input data is intentionally manipulated to deceive AI models.

Some key aspects of data security in the context of AI are:

  • Data encryption: Encrypting data, both during transmission and at rest, is crucial to ensure that it is unreadable to unauthorized persons.
  • Access control: Only authorized users should have access to AI models and the data they process. This requires robust authentication and authorization protocols.
  • Data minimization: When processing and storing data, the principle of minimization should be applied, i.e., only absolutely necessary data should be collected and retained.
  • Audit and monitoring: There should be constant monitoring of systems to quickly detect and respond to any unusual activities or security breaches.
  • Robust training data: AI models are only as good as the data they are trained on. It is crucial to ensure that the training data is of high quality and does not contain embedded biases or inaccuracies.
  • Transparency and explainability: Particularly with AI systems, it is important to ensure transparency about how data is processed and used. This promotes trust and allows users to make informed decisions about using such systems.

For platforms like MAIA, which provide data-driven knowledge management, data security is of utmost importance. Customers must be able to trust that their data is secure and used only for intended purposes. This requires not only the use of security technologies but also an ethical and responsible approach to data processing and storage.