Summary:
This webinar explores how LLMs and agentic AI systems can be systematically jailbroken through automated, scalable attacks, and how enterprises can respond with layered, continuously evolving defense strategies.

Key points:

• Jailbreaking LLMs is often a search problem, enabling highly effective automated attack methods like Tree of Attacks (TAP) and adversarial reasoning.
• Modern attacks increasingly use multi-turn and decomposition strategies that hide malicious intent across benign-looking queries.
• Automated red teaming can outperform human attackers in scale, cost, and consistency.
• Enterprise risk is best understood through business impact (cost, data exposure) rather than raw attack success rate.
• Traditional cybersecurity is insufficient due to AI systems being dynamic and context-dependent, requiring continuous monitoring.
• Effective defense relies on a “Swiss cheese” model: stacking alignment, prompt defenses, reasoning filters, guardrails, and monitoring.
• Agentic systems expand risk from bad outputs to harmful actions, increasing the stakes significantly.
• Enterprises should focus on safe tool access, employee education, and endpoint monitoring to reduce exposure.