Security News
A consensus on regulatory AI frameworks seems distant. The imperative for secure and responsible AI deployment cannot be overstated.
Code components available from public repositories can contain hidden backdoors or data exfiltrators, and pre-built models and datasets can be poisoned to cause apps to behave unexpectedly inappropriately. Backdoored or malware-spiked libraries and models, if incorporated into shipped software, could leave users of those apps open to attack as well.
In this Help Net Security video, Tracy Reinhold, CSO at Everbridge, discusses why AI technology must be embraced while also exploring some guardrails that must be in place to protect organizations...
Google's Gemini large language model (LLM) is susceptible to security threats that could cause it to divulge system prompts, generate harmful content, and carry out indirect injection attacks. The...
As cybersecurity professionals, we consider the security risks and implications it presents for businesses, and we must do our best to remain in control and set clear boundaries and limitations on what the technology can do. When we think about the security implications of interactive AI, we must first consider the concerns that have previously been raised around generative AI models and LLMs. These range from ethical concerns to political and ideological biases, uncensored models, and offline functionality.
97% of technology leaders find traditional AIOps models are unable to tackle the data overload, according to Dynatrace. 88% of organizations say the complexity of their technology stack has increased in the past 12 months, and 51% say it will continue to increase.
"At the most basic level, AI has given malicious attackers superpowers," Mackenzie Jackson, developer and security advocate at GitGuardian, told the audience last week at Bsides Zagreb. These superpowers are most evident in the growing impact of fishing, smishing and vishing attacks since the introduction of ChatGPT in November 2022.
What role do AI and automation play in cloud communications cybersecurity, and how can these technologies be leveraged to improve security posture? AI and automation are transforming cloud communications cybersecurity by enhancing threat detection, response times and the overall efficacy and efficiency of security operations.
The U.S. Department of Justice has announced the unsealing of an indictment against Linwei Ding, 38, a former software engineer at Google, suspected of stealing Google AI trade secrets for Chinese companies. The allegedly stolen trade secrets involve crucial technology underpinning Google's advanced supercomputing data centers, which are essential for training and hosting large AI models capable of processing nuanced language and generating intelligent responses.
To benefit society as a whole we also need strong public AI as a counterbalance to corporate AI, as well as stronger democratic institutions to govern all of AI. One model for doing this is an AI Public Option, meaning AI systems such as foundational large-language models designed to further the public interest. They would provide a mechanism for public input and oversight on the critical ethical questions facing AI development, such as whether and how to incorporate copyrighted works in model training, how to distribute access to private users when demand could outstrip cloud computing capacity, and how to license access for sensitive applications ranging from policing to medical use.