The podcast explores the potential cyber threats of AI and the need for aggressive measures to combat them. It discusses the use of AI in cyber attacks, securing neural networks, using robots to combat telemarketers, and utilizing generative AI to counteract malicious activities in cybersecurity.
The potential of AI-driven disinformation poses significant challenges and researchers are actively exploring ways to detect and counter such threats.
Collaboration between psychologists, engineers, and various professionals is crucial in understanding the social nature of AI and developing effective mitigations.
Deep dives
The Malicious Uses of Generative AI by Disinformation Groups
In this podcast episode, the focus is on the malicious uses of generative AI by disinformation groups. These groups utilize large language models, such as Chet-GPT and Bard, to create various forms of disinformation. Examples include using AI-generated images of political figures in social media posts, creating AI-powered news anchors for fake news outlets, and producing deep-faked videos to spread false narratives. The potential for AI-driven disinformation poses significant challenges, and researchers are actively exploring ways to detect and counter such threats.
Addressing Cyber Threats Posed by Generative AI
Researchers and cybersecurity professionals are working towards preventing and defending against the cyber threats posed by generative AI. Given that AI is fundamentally a social technology, the solutions to combat these threats should consider the social aspects involved. Collaboration between psychologists, engineers, and various professionals is crucial in understanding the social nature of AI and developing effective mitigations. While technical solutions like running prevention and detection software are important, new approaches such as parameter pruning and model compression are being explored to detect and stop AI malware from spreading.
AI in Cybersecurity: Leveraging Good AI Against Bad AI
AI is not only used by malicious actors, but also by cybersecurity defenders to analyze and combat cyber threats. Chat GPT and codecs programs, for example, are being used to aid in threat research, malware analysis, and analyzing blockchain smart contracts. While AI has provided considerable advancements on the defensive side, the potential of generative AI-driven attacks suggests the need for more robust measures. Researchers are investigating methods like social engineering active defense (SEED), where the power of generative AI and social engineering is used to counteract the efforts of cyber attackers. This ongoing battle between good AI and bad AI highlights the evolving nature of cybersecurity and the need for constant vigilance.
Much of the cybersecurity software in use today utilizes AI, especially things like spam filters and network traffic monitors. But will all those tools be enough to stop the proliferation of malware that will come from generative AI-driven cyber attacks? The potential of AI to disrupt cyberspace is far greater than any solutions we’ve come up with thus far, which is why some researchers are looking beyond the traditional answers, towards more aggressive measures.