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AI and DATA Protection

Rukshan B


AI and DATA Protection Artificial Intelligence is taking over many industries by facilitating personalized experience and managing & providing information. One of the main fear factors among the public against the adaptation of AI is the privacy concern related to data usage of individuals without their consent for various purposes.

It’s being only one side of the coin, there are many advantages that businesses can be benefitted from integrating AI. One such area of benefit is the automation of user support and the level of automation of user support can be varied from zero to full based on the organization’s requirement. Accordingly, based on the level of usage of AI in user support for automation, it can be either reactive support or proactive support and Autonomous support.

1. Reactive Support – Artificial Intelligence will only react to commands from users to support them, this can be through various input activities.

2. Proactive Support – Artificial Intelligence proactively take actions without explicit user request. This can be either.

  • AI services notify users what to pay attention on via notifications or alerts.
  • AI services make personalized recommendations such as fitness and health applications.

3. Autonomous Support – Artificial Intelligence will make decisions and take actions on behalf of users without confirmation.

However, we can protect our data ourselves.There are few levels that you can take your data protection initiative. At Management Level, System Level and AI Level. While management level and system level assist to protect data from unauthorized entities, AI can provide different DATA protection approaches where the AI itself will protect your data. Depend on the level of protection provides, there are 4 approaches for data protection.

  • Data-modifying Approaches – Modify or sanitize user data so that it cannot be linked to specific individuals which leads to an inherent conflict between both privacy and effectiveness goals.
  • Data-encrypting Approaches – This comprises protection approaches that work with encrypted user data, ensuring integrity and confidentiality when sharing data.
  • Data-minimizing Approaches – Aim to increase efficiency by minimizing the amount of personal data required.
  • Data-confining Approaches – An approach that does not require sharing personal data outside the user’s territory.

These 4 approaches ensure data integrity and confidentiality related to AI. The personalization of AI is one such way that protects your data handled by AI.

Accordingly, it identifies the advantage of using AI over the general fear of data security. However, due to the broad nature of AI, it cannot be concluded that it is fully secured and there are still some arguments that are to be further researched and addressed.

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