AI agents don’t behave like standard LLMs. Their risk surface spans prompt → retrieval → reasoning → action & tool calling, creating attack paths that slip past static filters, model-level tuning, and legacy governance frameworks.

Summary:
This webinar explains how enterprise AI agents are driving a shift to AI-native security, where runtime-first, model-driven defenses are required to manage expanding attack surfaces, tool-using workflows, and prompt-injection-style threats across multimodal systems.

Key points:

• AI agents introduce a new security paradigm: from static application security (SDLC, shift-left) to runtime AI security and continuous monitoring.
• The agent attack surface expansion includes tool use (APIs, MCP servers, databases, payments), enabling real-world impact beyond traditional input/output risks.
• Core threats in AI security include prompt injection, jailbreaks, indirect prompt manipulation, and contextual attacks embedded in retrieval pipelines.
• AI-native security architectures use AI models for detection, red-teaming, and guardrails to enforce real-time policy enforcement at inference time.
• Multimodal AI security risks (text, image, code, audio) introduce cross-modal injection and data exfiltration vectors requiring unified defenses.
• Effective protection requires low-latency guardrails, SLMs (small language models), and in-line inspection rather than large, slow model calls.
• Enterprise AI adoption is accelerating across industries, making AI security by design (day-zero security integration) critical for safe deployment.