Episode 68

When AI Becomes the Attack Surface, Securing LLMs, Agents & the Enterprise

In this episode of the AI Loves Data Podcast, we sit down with Ritika Verma, a cybersecurity and AI security professional whose work spans enterprise security, adversarial AI, threat modeling, AI risk, and the security of increasingly autonomous AI systems.

Ritika brings eight years of cybersecurity experience together with hands-on work in LLM security, GenAI applications, AI red teaming, RAG security, and agentic AI risk. Today, her work at the Institute for Security and Technology focuses on emerging AI security risks and translating technical findings into practical approaches to risk management and governance.

We explore what changes when AI isn’t simply another technology organizations need to protect but becomes a new attack surface capable of accessing data, using tools, interacting with systems, and taking action.

Key Highlights:

• How prompt injection, jailbreaks, adversarial attacks, and other emerging vulnerabilities challenge traditional cybersecurity approaches.
• What changes when AI systems gain access to tools, data, applications, and the ability to act autonomously.
• Where vulnerabilities can emerge across retrieval, proprietary data, model interactions, and enterprise GenAI pipelines.
• Why testing AI systems requires new offensive security techniques and what organizations should actually be testing.
• How AI can also help defenders automate vulnerability management, compliance, detection, and remediation.
• Understanding how increasingly autonomous AI systems introduce new questions around monitoring, containment, governance, and human oversight.

🎧 Tune in to Episode 68 to hear Ritika Verma’s perspective on securing the next generation of AI and what enterprises need to understand as LLMs and agents become an increasingly important part of the cybersecurity landscape.

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