Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In the context of digital onboarding, demographic features such as ethnicity, age, gender, socioeconomic circumstances, and even camera/device quality might affect the software’s capacity to match one face to a database of faces i.e. AI Bias. The quality and resilience of the underlying database in various sorts of surveillance might feed bias in the AI models. Biometrics are used in modern face recognition software to map facial traits from an image or video.

Healthcare organizations routinely handle large amounts of sensitive data, making data privacy in healthcare a top priority. Protecting patient data is not just about compliance—it’s crucial for maintaining patient confidentiality and safety. Unauthorized access can be severely detrimental, leading to breaches that compromise medical records and erode trust. Over the years, the digital revolution in healthcare has greatly elevated patient care standards.

Artificial Intelligence’s ability to augment and support progress and development over the past few decades is inarguable. However, when does it become damaging, contradictory even? In our latest Beyond Data podcast AI’s Climate Jekyll & Hyde – friend and foe, Tessa Jones (our VP of Data Science, Research & Development) and Sophie Chase-Borthwick (our Data Ethics & Governance Lead) discuss exactly this with Joe Baguley, Vice President and Chief Technology Officer, EMEA, VMware.