As artificial intelligence (AI) advances, I am seeing a lot of discussion on LinkedIn and in the online media about the advantages it may bring for either the threat actors (“batten down the hatches, we are all doomed”) or the security defence teams (“it’s OK, relax, AI has you covered”).
It's time for you and your colleagues to become more skeptical about what you read. That's a takeaway from a series of experiments undertaken using GPT-3 AI text-generating interfaces to create malicious messages designed to spear-phish, scam, harrass, and spread fake news. Experts at WithSecure have described their investigations into just how easy it is to automate the creation of credible yet malicious content at incredible speed.
ChatGPT has been available to the public since November 30, 2022. Since then, it has made headlines – from being temporarily banned from Stack Overflow because, “while the answers ChatGPT produces have a high rate of being incorrect, they typically look like they might be good, and the answers are very easy to produce,” .
One of the primary challenges that our security analysts encounter is where and how to best use their time. Monitoring and reviewing the constant influx of data and alerts produced by our client’s networks whilst also finding the time to keep on top of trending and emerging threats is no mean feat, and not particularly conducive to a healthy work-life balance…
Artificial Intelligence (AI) is a trending topic for many industries now. A variety of organizations currently employ AI mechanisms to support their operational functions. Automated tasks, natural language processing, deep learning, and problem-solving; such AI characteristics have made business tasks much easier. The factor of security in AI is largely overlooked, and with the increasing number of cyber threats and attacks, AI security serves as a crucial element that should be paid attention to.
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.
