Artificial Intelligence and Cybersecurity

AI is ushering in a multitude of opportunities for businesses, but keeping AI on track demands significant work.

Hype and FOMO around generative AI are hijacking crucial discussions around cybersecurity in many firms between CISOs and senior execs

AI is not a new concept for cybersecurity, but one of the key takeaways from the 2024 RSA Conference is that AI is inseparable from the future of cybersecurity.

Generative AI is not just teaching cyber bad guys new tricks — it's also making it easier for anyone to become a bad guy, said Cybersecurity and Infrastructure Security Agency chief Jen Easterly.

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.

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.

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.

GenAI can help by providing new tools for accurately analyzing cyber risks, streamlining process efficiencies, and helping insurers improve the security of buyers.

With the ability to craft convincing fabrications and process large amounts of data autonomously, malicious actors can (and in many cases already have) infiltrate once-impenetrable systems.

Despite the best efforts of generative AI developers to secure technologies with rigorous guardrails, attackers continue to find creative ways to circumvent these protections.

Experts at the RSA Conference urged cyber professionals to lead the way in securing AI systems today and pave the way for AI to solve huge societal challenges

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

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

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

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.

There's growing interest in generative AI Isolation and comparable technologies to keep confidential data out of ChatGPT, Bard and other gen AI sites