Artificial Intelligence and Cybersecurity

In this respect, AI enables us to do more in less time; an enormous benefit to cybersecurity teams struggling with talent shortages.

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.

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.

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

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.

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

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

The nearly 100-page paper, titled “Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations,” provides a comprehensive overview of the cybersecurity and privacy risks that come with the rapid development of both predictive and generative AI tools over the last few years.

To realize the importance of prioritizing cybersecurity, it’s helpful to understand the cyber risks to AI safety.

Generative AI tools help bad actors easily craft sophisticated attacks. CISOs are investing in AI-native cybersecurity solutions to fight back.

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

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.

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

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.

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.

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

A recent study from the National Institute of Standards and Technology (NIST) and their collaborators has exposed the vulnerability of AI and machine learning systems to deliberate manipulation, commonly known as "poisoning."

LLMs are uniquely positioned to take on the challenge of predicting potential intrusion attempt patterns across endpoints using collected attack data.