Securing agentic AI systems

Practical insights on attack surfaces, threats, and defense architectures

AI agents that plan, use tools, persist memory, and coordinate with other agents are reaching production systems faster than most security teams can evaluate their risks. Their security model differs fundamentally from standalone LLMs, introducing new attack surfaces across tools, memory, planning loops, and agent-to-agent interaction. I’ve been writing about these emerging threats and the defense patterns needed to secure agentic systems as the ecosystem evolves.

If you need practical support, I offer agentic AI security consulting to help trace attack paths and design defense architectures tailored to your environment.