Podcast/Agentic AI/ Prompt Injection: 4 Practical Defenses for Your AI Agents

Agentic AI

Episode 32 · Season 1

Prompt Injection: 4 Practical Defenses for Your AI Agents

February 27, 202622:00

Agentic AI security is now the difference between “we shipped” and “we leaked.” In this episode, we break down the real security risks AI agents introduce, and the practical ways to mitigate them.

What you’ll learn:

  • How prompt injection happens (and why LLMs struggle to separate trusted vs. untrusted instructions)
  • How data exfiltration can occur even without a malicious prompt (often from system/design errors)
  • How to reduce tool abuse risk by scoping tools, validating calls, logging, and using approvals for sensitive actions

Who it’s for: CTOs, CIOs, architects, engineers, and business leaders implementing or governing AI agents.

Guest + credibility: Greg is joined by Gavin Franklin and Tim to translate agent security into practical patterns teams can apply immediately.

CTA: Share this episode with someone responsible for AI rollout security. (And follow the show for more Agentic AI implementation guidance.)

Recommended Links

Episode Artifact

Gavin Franklin on LinkedIn

Tim Harting on LinkedIn

Cirrius Solutions

Cirrius Blog

Greg Banks on LinkedIn

Jason Fowler Music

For questions or feedback, please contact us at CirriusTalk@CirriusSolutions.com

We cover

  • Overview of AI Security Risks
  • Deep Dive into Prompt Injection
  • Understanding Data Exfiltration
  • Exploring Tool Abuse
  • Mitigation Strategies for AI Security Risks

Chapters

  • 0:00Introduction to AI Security Concerns
  • 2:51Overview of AI Security Risks
  • 6:25Deep Dive into Prompt Injection
  • 10:39Understanding Data Exfiltration
  • 13:23Exploring Tool Abuse
  • 15:16Mitigation Strategies for AI Security Risks
  • 19:04Conclusion and Final Thoughts

Resources and downloads

The artifacts Greg’s team built for this episode, free to download.

Prompt Injection: 4 Practical Defenses for Your AI Agents

A walkthrough of three security risks that AI agents reintroduce: prompt injection, data exfiltration, and tool abuse. Each risk is traced through a worked email-agent example showing how it happens and what it costs, followed by the defenses that address them, including keeping secrets out of the model, human approval on sensitive actions, narrowly scoped sub-agents, and least privilege on every tool call.