While businesses shouldn’t shy away from using generative AI in operations, it’s important to implement it with security top of mind.

GenAI is evolving rapidly, and codifying internal governance allows your security teams to stay two steps ahead, making the use of GenAI consistent across the business, and identifying risk ahead of time.

Recent technological advances in hardware-based cybersecurity enable an “oversight system” to monitor and guarantee the integrity of incoming data and the confidentiality of outgoing data.

Meanwhile, 36% were either neutral or disagreed that AI would play an important role in improving their cybersecurity.

Two surveys by tech vendors reveal a significant slowdown in AI adoption as business and IT leaders consider the legal and practical ramifications.

Five ways generative AI can help with the cybersecurity skills shortage — from lowering the bar to entry to improving collaboration.

Threat actors are starting to use artificial intelligence, but too many executives aren't prepared for the risks.

Generative AI could be the holy grail of DevSecOps, from writing secure code and documentation to creating tests. But it could be a major point of failure if not used correctly.

Defensive AI emerged as a critical AI use, with 58% of respondents to a survey saying their organization is investing in AI to stop AI-based attacks.

For AI to reach its full potential across enterprises, it requires an entirely new tech stack, one with security designed to start with software supply chains and model development.

The survey of global CEOs from PwC brings to light what global executives aim to achieve with generative AI in the near and long term.

A study revealed most organizations faced evolved cyberattacks, adding to the time and cost needed for remediation.

Zero-trust security runs more effectively with AI and ML. This allows IT teams and organizations to protect their networks properly.

Allowing AI-related hype and FOMO to hijack decisions around cybersecurity could be a high price to pay for many firms.

Continuous learning and awareness of the latest threats and best practices are key to maintaining a secure financial environment.

As AI becomes integral to systems brought into the enterprise ecosystem it is increasingly critical for security teams to know where it is and reduce its risks.

a16z General Partner Zane Lackey and Partner Joel de la Garza recently sat down with us to discuss the state of security in 2024. They addressed the top concern for CISOs: the impact of generative AI on enterprise security. Here’s what they had to say about the key considerations for technology adoption and strategie

The only way to fight against AI is with AI – but you must master the basics first

In this episode of the SecurityANGLE, host Shelly Kramer, managing director and principal analyst at theCUBE Research, is joined by analyst, engineer and theCUBE Collective community member Jo Peterson for a conversation about the top four generative artificial intelligence cyber risks that keep CISOs up at night.

AI can improve threat-hunting efficiencies with improved automation and accuracy, according to a study.

Google Security Engineering and The Carnegie Mellon University Software Engineering Institute in collaboration with OpenAI have sorted through the hype - and done some serious thinking and formal research on developing "better approaches for evaluating LLM cybersecurity" and AI-powered patching: the future of automated vulnerability fixes. This is some great formative framing of the challenges ahead as we collectively sort out the implications of the convergence of generative AI and future cyber capabilities (offensive and defensive).

Large language models may boost the capabilities of novice hackers but are of little use to threat actors past their salad days, concludes a British governmental evaluation

A proactive, coordinated effort can reduce the chances that manipulations will impact model performance and protect algorithmic integrity.

Generative AI has taken the world by storm but there’s still some confusion over its applicability in a cyber security context

Prompt injection, prompt extraction, new phishing schemes, and poisoned models are the most likely risks organizations face when using large language models.