Three Questions Every CISO Should Ask Today
Can you identify every AI agent running in your environment right now? Not just the applications with "AI" in the name or LLM calls, but every autonomous workflow, every agent composing other agents across your infrastructure.
Do you know what data those agents are accessing and where it's going? Can you trace the lineage of information as it flows through agent chains, gets transformed by models, and gets sent to external services?
Can you enforce policy on agentic behavior in real time? Not just block access to tools, but govern how agents use those tools based on runtime context, intent, and business impact.
If the answer to any of these is "no," you're operating with a critical blind spot. Here's why this matters—and why your existing security stack can't answer these questions.
The Problem: 30 Years of Security Assumptions Just Broke
For three decades, enterprise IT security operated on a foundational assumption: every application has a user, a device, and a session that can be secured.
That assumption is now obsolete.
AI agents and agentic applications fundamentally don't behave like traditional applications:
- Agents don't log in — they are triggered automatically
- Agents don't persist — they exist ephemerally
- Agents compose recursively — creating sub-agents with no visibility trail
Yet every micro-decision these agents make carries real business impact:
- Sensitive data accessed and moved
- Code executed in production
- Contracts auto-updated
- Compliance boundaries crossed
This is the new enterprise control crisis: AI workloads moving faster than our ability to see, govern, or secure them.
Enterprise security has adapted before. We moved from physical control of desktops to identity-based control of SaaS access. Each shift required rethinking the control plane.
The Desktop Era gave us Group Policy Objects and virtual app delivery. Control meant owning the infrastructure where apps ran. This worked when applications and users were predictable, stateful, and long-lived.
The SaaS Era gave us Zero Trust Network Access and identity as the new perimeter. When apps moved to the cloud, we secured the access path instead of the infrastructure. This worked because SaaS apps were still user-bound—a human signed in, a session started, a policy applied.
But here's what's different now: AI agents have no persistent identity, no stable sessions, and no predictable behavior patterns.
Why Your Security Stack is Blind to Agent Behavior
1. Identity Platforms Can't Track Ephemeral Agents
Identity and Access Management systems were architected around persistent principals. A user account exists in your directory. A service account has static credentials.
But AI agents spawn dynamically and create sub-agents recursively. When an agent spawns another agent to handle a subtask, no traditional IAM system registers that as a new identity event. You granted access once—to the parent agent—but now you have inherited permissions cascading through chains you never explicitly authorized.
The core problem: IAM assumes stable identities. Agents have fluid, recursive ones.
2. Zero Trust Tools Miss Agent-to-Agent Communication
Zero Trust platforms secure user-to-application traffic. They inspect web sessions, enforce device posture checks, and apply conditional access policies.
But agents don't communicate through web browsers. They orchestrate tool calls, exchange data through APIs, and invoke models directly. This agent-to-agent, agent-to-tool communication happens outside the ZTNA control plane entirely.
The core problem: Zero Trust secures the network perimeter. Agents operate above it.
3. AppSec Tools Can't Detect Behavior Drift
Data Loss Prevention and application security tools monitor static data flows and user actions. They look for sensitive data crossing boundaries or users doing unauthorized things.
But agentic behavior drift is different. It's when an AI agent starts acting outside its intended scope—accessing data it shouldn't need, chaining tools in unexpected combinations, or making decisions beyond its designed purpose.
This isn't a policy violation you can define ahead of time. It's emergent behavior that only becomes visible at runtime.
The core problem: Traditional security operates at configuration time. Agent behavior needs runtime governance.
What AI Agent Security Actually Requires
The fundamental insight is this: you can't secure what you can't see, and you can't see what doesn't persist long enough to audit.
Agent security requires three new capabilities:
1. Discovery: Know What's Running
- Automatic detection of every agent, model call, and tool invocation
- Visibility into agent composition graphs (which agents spawn which sub-agents)
- Runtime inventory that updates continuously, not periodically
2. Insight: Understand What It's Doing
- Real-time tracking of data flows across agent chains
- Behavioral profiling to detect drift from intended scope
- Context-aware logging that captures not just what happened, but why
3. Governance: Enforce Policy on Actions, Not Just Access
- Per-action policies based on runtime context
- Dynamic enforcement that adapts to agent behavior
- Audit trails that trace decisions through recursive agent chains
This is fundamentally different from traditional security. It's not about authenticating before allowing access—it's about observing behavior and enforcing policy at the moment of action.
What This Means for Enterprise Security Teams
If you're running AI agents in production—whether that's autonomous workflows, LLM-powered tools, or agent-orchestrated processes—you have a visibility gap. Traditional monitoring shows you containers and API calls. But it doesn't show you:
- Which agent made which decision and why
- What data that agent accessed and where it went
- Whether that agent's behavior matches its intended scope
- How many sub-agents it spawned and what permissions they inherited
The gap between what your security stack sees and what your AI workloads are doing is growing every day. The longer you operate without agent-specific visibility and governance, the larger your exposure becomes.