Check out all the on-demand sessions from the Intelligent Security Summit here. While others were shopping and decorating for the holidays, VentureBeat readers didn't check out for Christmas cheer this week. Rather, they were consuming coverage in two keys -- as reflected in our Top 5 stories of the week -- AI and security. Sharon Goldman's coverage of ChatGPT and generative AI captured the two top spots among the list of most-read stories. Goldman talked to Forrester Research's Rowan Curran about how and why ChatGPT is having an iPhone moment.

Britannica describes artificial intelligence as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The trouble in this definition is the term intelligent. How do you define intelligence? How do you measure intelligence? You can say something is intelligent if it can do descriptive, diagnostic, predictive, and prescriptive analytics at the same time or within a short timeframe.

Organizations are deluged with billions of security events every day, far too many for human analysts to cope with. But security analysts have a powerful ally in their corner: machine learning is tipping the advantage toward defenders. Machine learning (ML) is changing the approach of organizations to threat detection and how they adapt and adopt cybersecurity processes. The idea is not just to identify and prevent threats, but to mitigate them as well.

Organizations are deluged with billions of security events every day, far too many for human analysts to cope with. But security analysts have a powerful ally in their corner: machine learning is tipping the advantage toward defenders. Machine learning (ML) is changing the approach of organizations to threat detection and how they adapt and adopt cybersecurity processes. The idea is not just to identify and prevent threats, but to mitigate them as well. ML has the power to comprehend threats in real time, to understand the infrastructure of a company and its network design and attack vectors, and to protect and defend it with human talent and machine power. The algorithm--the machine--is capable of the unthinkable when it comes to data mining, data crunching, and data correlation, since it does what it does best tirelessly, without complaint, and without having a bad day.

Both AI and cybersecurity are nearly omnipresent in our daily lives, and the intersection of the two is of increasing importance as our world becomes more connected, more "intelligent," and more reliant on online or automated systems. AI technology can impact existing problems in cybersecurity, national security, physical safety, and even media consumption. The threats are sometimes more sophisticated than ever -- but often not. As attack and defense systems evolve, the need for human expertise becomes more imperative -- not less. And some of the seemingly most onerous threats, like deepfakes and the increasing presence of AI-powered cameras, have practical and political solutions.

Once a quarter, VentureBeat publishes a special issue to take an in-depth look at trends of great importance. This week, we launched issue two, examining AI and security. Across a spectrum of stories, the VentureBeat editorial team took a close look at some of the most important ways AI and security are colliding today. It's a shift with high costs for individuals, businesses, cities, and critical infrastructure targets -- data breaches alone are expected to cost more than $5 trillion by 2024 -- and high stakes. Throughout the stories, you may find a theme that AI does not appear to be used much in cyberattacks today.

VentureBeat's second special issue is nigh. Following our first special issue, Power in AI, this next one focuses on AI and security. Each special issue is a package of articles that explores a central topic from a variety of angles, from voices in industry, academia, and our newsroom. Whether we're aware of it or not, both AI and cybersecurity are nearly omnipresent in our daily lives at this point, and together they're of increasing importance as our world becomes more connected, more "intelligent," and more reliant on online or automated systems. Yet both can seem intractably technical, even for tech-savvy people.

The stakes are higher than ever now for AI and security. Following Sepideh Seifzadeh's keynote on managing the AI lifecycle, Dawn Song of the University of California's BAIR Lab took the stage to discuss an important but often overlooked component of the AI lifecycle: Security, specifically with deep learning, and how the stakes are becoming higher as AI becomes more intelligent. We often hear about various security concerns in the news, seemingly every day in regards to a hack affecting our personal information. Though, now both the hackers themselves and AI systems are becoming significantly more sophisticated, and data scientists are tasked with finding new ways to mitigate, prevent, and remedy these advancements. "Our current framework is insufficient for protecting data rights and privacy," she said.

Machine learning allows us to get down to what is essential for detection content,

Machine learning allows us to get down to what is essential for detection content,