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Data protection and data security in AI applications: Why they are essential and how to determine the right level of security
In an era where data is the new oil, protecting sensitive information through AI solutions such as those from Futurised is becoming increasingly critical. This article highlights the importance of data protection and security in AI applications, provides a checklist for determining the security level of AI projects, and underlines that compliance with these practices is not only a legal obligation, but also a key factor for corporate success.
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Low-code tools are going mainstream
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Multilingual NLP will grow
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Combining supervised and unsupervised machine learning methods
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Automating customer service: Tagging tickets and new era of chatbots
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Detecting fake news and cyber-bullying
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In a world where artificial intelligence (AI) is penetrating more and more business and everyday processes, protecting sensitive data is becoming increasingly important. Companies like Futurised are committed to ensuring that AI solutions are not only powerful but also secure and trustworthy. In this article, we explain why data protection and data security are so critical in AI applications and provide you with a practical checklist for determining the required security level for your AI project.
Why are data protection and data security so important in AI?
Create trust: In an era in which data is considered the new oil, trust is the foundation on which business relationships are built. Consumers and business partners must be able to trust that their data is secure. This is particularly important because AI systems can often deeply invade privacy.
Regulatory requirements: With stricter data protection laws, such as the GDPR, companies must ensure that their AI solutions comply with these regulations. Compliance with these laws not only protects consumers, but also strengthens legal compliance and the company's image.
Protection against data misuse: AI systems are particularly vulnerable to manipulation. A robust security system prevents data from being manipulated, which could lead to incorrect decisions. Ensuring data integrity is therefore a critical aspect of any AI implementation.
Checklist for determining the security level for your AI project:
Type of data:
Personal data: Does your project contain personal data? These require the highest level of security.
Company data: Is confidential business data involved?
Publicly available data: Is publicly available data processed?
Purpose of data:
Decision-making: Is the data used to support decision-making processes?
Automation: Is AI used to automate processes?
Risk assessment:
Data breaches: What is the risk of a data breach?
Consequences of data leaks: How serious would the consequences of a data leak be?
Technical and organizational measures:
Data backup: How often is data backed up?
Access controls: Who has access to the data and systems?
Encryption techniques: Is data encrypted during transmission and storage?
Review and compliance:
Regular audits: Are regular safety audits carried out?
Compliance checks: Is compliance with data protection standards regularly checked?
EU AI Act: Consider the upcoming EU AI Act, which sets new regulatory requirements for AI systems, to ensure that your AI projects meet the latest legal standards.
Conclusion:
Data protection and data security are not only legal obligations, but also critical components for the success of AI projects. Companies that are able to implement robust security measures are positioning themselves as trustworthy partners in the digital landscape. At Futurised, we are committed to providing leading artificial intelligence solutions that are both innovative and secure. Our expertise ranges from generative artificial intelligence technologies to advanced AI chatbots, which ensure that your data is protected and creates maximum value from your AI investments.
