Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

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.

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.

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.

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.

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.

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.

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