In this episode, Justin Hutchens discusses the potential misuse of generative AI for social engineering and hacking. They cover AI's ability to learn human emotions and manipulate people for compromising security. The conversation also touches on the role of identity in threat monitoring and the challenges and opportunities AI presents for organizations in defending against evolving threats.
AI can manipulate individuals through social engineering, posing security risks.
Generative AI models enhance attacks' sophistication and speed, challenging traditional defense strategies.
Deep dives
Evolution of AI in Social Engineering
Artificial intelligence has evolved drastically, from basic chatbots to complex systems capable of automating social engineering. By leveraging AI, attackers can scale social exploitation, creating automated processes to manipulate and exploit individuals. The ability to fully automate social interactions and exploit vulnerabilities poses a significant threat in compromising accounts and personal information.
Capabilities of Generative AI in Threats
Generative AI models, like GPT-3 and chat GPT, pose new challenges in threat detection and defense strategies. These AI systems can autonomously execute hacking campaigns, targeting systems with script kitty level attacks. Enhancements in AI technology accelerate the sophistication and speed of attacks, making it increasingly difficult to rely on traditional signs of attacks.
Adversarial AI in Cybersecurity
Adversarial AI refers to threat actors leveraging AI capabilities maliciously. The adoption of AI in social engineering and technical hacking enhances attackers' capabilities. Large language models, like Microsoft Copilot, offer attackers avenues to exploit existing systems and conduct targeted attacks with minimal detection risks.
Challenges for Organizations in AI Defense
Organizations face challenges in understanding and defending against evolving AI threats. Guardrail frameworks, such as NIST's AI risk management framework, MITRE's Atlas framework, and Google's SAFE framework, aid in implementing effective defense strategies. Balancing the risks from external threats and internal system inconsistencies is crucial in mitigating AI-related security vulnerabilities.
In the 50th episode of the Trust Issues podcast, host David Puner interviews Justin Hutchens, an innovation principal at Trace3 and co-host of the Cyber Cognition podcast (along with CyberArk’s resident Technical Evangelist, White Hat Hacker and Transhuman Len Noe). They discuss the emergence and potential misuse of generative AI, especially natural language processing, for social engineering and adversarial hacking. Hutchens shares his insights on how AI can learn, reason – and even infer human emotions – and how it can be used to manipulate people into disclosing information or performing actions that compromise their security. They also talk about the role of identity in threat monitoring and detection, and the challenges and opportunities AI presents organizations in defending against evolving threats and how we can harness its power for the greater good. Tune in to learn more about the fascinating and ever-changing landscape of adversarial AI and identity security.
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