The definition of Artificial Intelligence (AI) has been thrown around whilst it has risen to the top of the tech agenda over the past couple of years. Security professionals have determined AI to be a risk to businesses, and also an opportunity. But could it also be a way to better defend your network against attacks? For many years, AI and Machine Learning have gone hand in hand; with AI used to better determine defensive decisions and cut down on the human element in more basic functions.

Last week’s release of OpenAI’s GPT-4o, along with the story around Slack’s AI training policy, goes to show that AI innovation is happening at a rate where, for most companies, security simply can’t keep up.

Responsible AI Licenses (RAIL) are a class of licenses created with the intention of preventing harmful or unethical uses of artificial intelligence while also allowing for the free and open sharing of models between those who intend to use and improve them for authorized purposes. Anyone can make their own version of RAIL for their model, and in doing so can create more or less restrictions than those detailed in the template licenses.

AI is everywhere—but how can you be sure that your data isn’t being used to train the AI models that power your favorite SaaS apps like Slack? This topic reached a fever pitch on Hacker News last week, when a flurry of Slack users vented their frustrations about the messaging app’s obtuse privacy policy. The main issue?

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Since ChatGPT launched in November 2022, generative AI has emerged as one of the fastest-adopted technologies in the workplace ever. But, as seen in past paradigm shifts like cloud computing, the productivity benefits of this new technology are balanced with new risks.

Artificial intelligence (AI) has a long and storied history. Ancient Greeks, for example, told stories of Talos, an enormous automaton that stood guard over Crete’s shores. In the 17th century, Gottfried Leibniz, Thomas Hobbes, and René Descartes explored the possibility that all rational thought could be as systematic as algebra or geometry.

Phishing is not going away—it’s morphing and evolving. As an example, in late 2022 the overnight commodification of artificial intelligence (AI) changed the phishing threat landscape forever, lowering the barrier to entry for novice fraudsters who had previously had the motives, but lacked the means to launch sophisticated scams. ChatGPT and fraud-specific Generative AI tools (like the aptly named FraudGPT and WormGPT) granted malefactors the ability to supercharge and scale their operations.

Artificial Intelligence is becoming an integral part of our work. It helps us be more productive by automating tasks, analyzing, and boosting the speed of our work. Due to the desire to accelerate work and make it more efficient, SaaS providers integrate AI into their software. For example, Atlassian in 2023 announced Atlassian Intelligence, a combination of state-of-art OpenAI models and the power and data within the Atlassian platform.

Development teams of all sizes are embracing the excitement and possibility of using AI tools to build software. Coding assistants like Google Gemini and Github Copilot have the potential to accelerate development like never before, and developers are adopting these tools — whether or not leadership has officially approved them. As your team considers the best ways to adopt this new technology, this transition might feel like déjà vu.