When the agents walk in, your security model walks out

1. From static threat models to dynamic behavior monitoring
Traditional threat models expect attackers to follow known patterns including lateral movement, privilege escalation, payload delivery. But agents don't follow known anything. They can improvise.

Security teams need to start monitoring emergent behavior. That means building telemetry around what agents do, how they think, and when they deviate from intended paths. Semantic. Observability. Nuff said.

2. From perimeter controls to runtime policy enforcement
Firewalls and gateway-level protections don't help when the LLM agent is already inside calling tools, accessing files, or submitting API requests autonomously.

Security must move closer to runtime, enforcing task-scoped permissions, environment isolation, and intent validation in real time. Think of it as policy-as-inference: what an agent is allowed to do must be checked as it decides to do it. This is the collapse of data and control planes, and security has to be involved.

3. From logging events to capturing context
You can't secure what you can't understand, and with agents, understanding requires more than logs. You need prompt chains, tool call metadata, memory snapshots, and execution context all logged and traceable. Context is the new perimeter.

Why did the agent book five meetings and send an email to a vendor at 2 AM? You won't know unless you can replay its decision tree. This isn't observability. It's agent forensics.

4. From code reviews to behavioral testing
An agent's logic isn't in code, it's in the combination of weights, prompts, tools, and context. That makes static review useless.

What's needed is sandboxed behavioral QA: simulate edge cases, adversarial inputs, and permission boundaries. Run agents like they're junior engineers in training, not deterministic code modules.

Red-teaming needs to evolve from "penetrate the system" to "manipulate the agent"-repeatedly, and with a keen eye for failure cascades.

5. From user identity to agent identity and scope
Access control today is mostly user-centric: who are you, and what roles do you have? That won't work for agents. You now need to assign identity, privilege scope, and task boundaries to AI actors, along with automatic expiration (think TTL), isolation from shared memory, and persistent audit trails.

In short: zero trust now applies to non-human actors. And that trust must be earned every time they invoke a tool or touch a resource.