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AI in Cybersecurity: Striking the Balance Between Innovation and Trust

October marks Cybersecurity Awareness Month. The theme “Secure Our World” invites businesses (and individuals) to reflect on how digital risks are evolving and what it takes to stay ahead. It is an open invitation toward resilience in the face of attackers enhancing their playbooks by leveraging cutting-edge technologies and tactics.

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Code, Comply, Repeat: Why Human Oversight is Essential in AI-Assisted Code Development

From GitHub Copilot to ChatGPT plugins and IDE-integrated assistants, AI fundamentally changes how developers write and ship code. What once required hours of manual configuration and debugging can now be scaffolded in seconds by a smart prompt. But while productivity is skyrocketing, the rise of AI-assisted development introduces new challenges in code security, quality, and accountability.

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Is there an (easy) way of detecting bias and fairness in Computer Vision AI systems?

Computer Vision AI systems are much more complicated than AI systems using tabular datasets (typically extracted from databases) as the features they employ to make a decision (millions of pixels) are orders of magnitude more than what tabular datasets usually entail (tens to hundreds of database columns).

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From Compliance to Culture: Rethinking Cybersecurity in the Age of AI

We are living in a paradox. Artificial intelligence is often hailed as the great enabler of digital transformation. However, it is also proving to be a potent weapon in the hands of cyber attackers. Yet on the defensive side, organizations often struggle to extract similar value. Why is it that AI empowers offense more effectively than defense?

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