We at the Data Protection Institute follow what is happening in our field as closely as possible. In this post, we identify three trends that we have noticed in the privacy landscape recently and explain how you should deal with them as a DPO.<\/p>\n
Face recognition per se is, of course, hardly a trend. However, the gradual shift towards the widespread use of face recognition for all kinds of consumer applications is indeed a trend. At European level, efforts are still being made in the AI Act<\/a> to ban the use of face recognition in the public domain (such as police forces) but this is by no means the intention for consumers. Whether checking in for the Eurostar<\/a>, attending festivals<\/a> or buying goods in shops<\/a>, you will come across face recognition!<\/p>\n This is not necessarily a bad thing. The technology does involve risks, but it also brings huge advantages in terms of convenience. As a privacy professional, you might be horrified by the use of face recognition anywhere, but if consumers like it, the choice is still theirs. And therein lies the crux of the matter: this must be a transparent and informed choice.<\/p>\n Always make sure that face recognition applications are offered as a fair choice (suggesting an alternative without face recognition and without disadvantages) and that users are well informed in advance. Provide stringent measures to protect templates of faces (or derivatives) against external access and restrict internal access as much as possible.<\/p>\n <\/p>\n \u201cWhat an open door, DPI!” <\/em>we hear you rant at your screen. AI is the buzzword in almost every sector linked to the knowledge economy. However, what is mainly meant<\/a> by AI these days is the subset within AI known as “machine learning”. And the keyword here is learning<\/strong>. Models that form the basis of ChatGPT, for example, learn from huge amounts of data and many aspects of these data are therefore crucial for the technology to be successful.<\/p>\n Pay attention to the basics when you come up against AI\/machine learning:<\/p>\n There are also areas of concern when using AI applications. For example, when using an existing AI application, check how it deals with the (personal) data used as input. The application itself reusing the input data may pose risks (data leaks).<\/p>\n <\/p>\n Within security and privacy, we often focus on threats that come from outside or on internal “errors”, such as hackers, phishing, ransomware, or e-mails sent by accident. Slowly, however, more attention is being paid to the principle of the “insider threat”, in other words an individual within the organisation deliberately trying to gain access to (personal) data of the organisation for their own benefit. Recent examples come from the police<\/a>, hospitals<\/a> or even a dental hygienist<\/a>. Some aspects worth considering:<\/p>\n ***<\/p>\nArtificial Intelligence<\/strong><\/h4>\n
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Insider Threat<\/strong><\/h4>\n
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