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.

Rego, the policy language of the Open Policy Agent (OPA), is known for its flexibility and power in policy enforcement across various systems. Its declarative syntax and data-centric approach make it versatile for application authorization, infrastructure as code (IaC) authorization, and network policies. To fully appreciate OPA/Rego’s capabilities, it’s helpful to compare it with other policy languages and frameworks like AWS’s Cedar and Google’s Zanzibar.

Large Language Models (LLMs) have become indispensable tools across various sectors, reshaping how we interact with data and driving innovation in sensitive domains. Their profound impact extends to areas such as healthcare, finance, and legal frameworks, where the handling of sensitive information demands heightened security measures.

In the oft-obscure world of Architecture, Engineering, and Construction (AEC), the structures we see reaching for the skyline are not just feats of design and engineering but archives of data, each rivet and beam a data point in a colossal network of information. Yet, with these digital monoliths comes an invisible vulnerability – data control, a challenge that’s upending the AEC industry.

Google Cloud Platform (GCP) is one of the world’s most widely used cloud services. At the heart of this system lies roles, which act as predefined sets of permissions that grant users specific access levels amidst the complexity of credentials, identities, and resources in the cloud environment.

Analysis of malicious large language model (LLM) offerings on the dark web uncovers wide variation in service quality, methodology and value – with some being downright scams. We’ve seen the use of this technology grow to the point where an expansion of the cybercrime economy occurred to include GenAI-based services like FraudGPT and PoisonGPT, with many others joining their ranks.

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.