Every major technology wave follows the same arc. A new capability emerges, business leaders push to adopt it to stay competitive, and security is immediately on the hook to make it safe enough to scale. 

AI agents present a unique version of this challenge. These agents are so easy to build, connect, and launch that citizen developers have already brought AI assistants like Clawdbot to scale before security can review, configure, or even inventory what’s running.  

From January 2025 to January 2026, we’ve observed the number of AI agents deployed in enterprise environments grow by more than 300x. And because users are not security experts, most agents are given broad access and excessive privileges. The average organization already has over 800 risky agents in operation, ones that don’t authenticate through SSO, appear in Active Directory, or stay confined to the network. 

The security leaders who thrive in this environment won’t be the ones who build the most restrictive pre-deployment policies. They’ll be the ones who rethink the model entirely, building programs designed to operate alongside machine identities, controlling machine behavior, and enforcing controls at machine speed. Most are eager to start that journey. They’re just beginning in the wrong place. 

Your AI Governance Plan Has a Blind Spot 

Security conversations around AI agents tend to focus on risks before agents go live: Prompt injection, data poisoning, and vulnerabilities in AI vendor infrastructure. Solving those problems matters. But they miss a more immediate reality: the agents are already running. 

And unlike traditional software, you cannot predict how they will behave. Agents are probabilistic, not deterministic. They interpret instructions, make inferences, and take actions that no configuration checklist can fully anticipate. This is where the blast radius becomes real. 

This is why we’re already seeing agents mass-delete inboxes and wipe source code from production environments. The users who built these agents weren’t being reckless. They were moving fast, the way the business wanted them to. But an agent a business can’t trust is an agent it can’t use at scale. And you cannot build that trust through configuration alone because by the time an agent misbehaves, it’s already too late. 

Why Runtime Security is the Missing Piece 

To understand why pre-deployment controls aren’t enough, consider how agent failures actually happen. 

Imagine a RevOps manager builds an agent to generate and send regular pipeline reports from Salesforce. For simplicity, he builds it in maker mode, granting the agent the same read/write privileges he holds. This lets him move fast and looks secure, since the agent is only accessing data he can access himself. 

After one report returns outdated information, he prompts the agent to delete the data. Misinterpreting the instruction, the agent determines the cleanest path is to delete the underlying records in the CRM rather than filter them from the report output. The enterprise had agreed that agents should never modify CRM data. But that policy was never encoded as a runtime guardrail. The user’s permissions became the agent’s permissions, and before the mistake could be caught, the deletions were already done. 

This is the nature of agentic risk. Static configurations are no longer sufficient when dynamic, autonomous agents are involved. An over-privileged agent doesn’t need to be compromised to cause damage. It just needs an ambiguous prompt and enough delegated authority to act on it. 

Runtime security solves this gap — and the good news is that it isn’t a new concept. Security teams already monitor user behavior and enforce guardrails in real time. A junior employee can’t open a sensitive file link unless it’s been shared with them. A user who fails too many login attempts is blocked until their identity is verified. Runtime security for agents works the same way: watching every action an agent takes and ensuring it stays policy-aligned, at machine speed, before damage is done. 

How CISOs Can Build a Runtime Security Program 

The default cannot be to block everything from the start. Security needs to be seen as AI-friendly. An accelerant for adoption, not a brake on it. A phased approach lets security teams begin monitoring AI agents at runtime, build evidence for where guardrails are needed, and make the business case for action before locking anything down. 

1. Build a single inventory of your AI agents. Adoption is accelerating faster than governance visibility, making inventory not a nice-to-have but a prerequisite for control. The challenge is the diversity of platforms users are building on. To centralize control, you need a single dashboard showing every agent that exists — regardless of where it was built — along with its owner, connected MCP servers, associated models, and connected applications. Crucially, this can’t be static, but a living map that evolves as new agents are added and new applications connected. This baseline is how you map real access and track every action agents take across your environment.
2. Define risk factors to instantly assess agent posture. Viewing an agent’s configuration alone can’t surface the risky actions it’s capable of taking. Scoring agents from low to critical based on defined risk factors helps prioritize runtime security and provides the evidence needed to have productive conversations with the teams building them. Key risk factors to watch: agents with embedded credentials that are publicly accessible; agents whose owner account has been disabled; and agents shared org-wide with access to data carrying sensitive classification tags.
3. Observe before you block. After defining runtime policies, watch how often agents would trigger them before enforcing anything. This gives you a business case — actions that are high-risk but rare can be shut down with minimal impact. Actions that are high-risk and frequent become the basis for a conversation with the agent’s owner: how do we preserve the productivity without carrying the risk? This approach turns security from a blocker into a collaborator.

Runtime Security Lets CISOs Deploy AI With Confidence 

There was a moment, not long ago, when it became undeniable. AI wasn’t going to stay an experiment. Once users experienced what it could do, there was no asking them to go back. The business wasn’t debating whether to adopt AI. It already had. 

And yet, beneath all that momentum, hesitation persists. Not about whether to use AI, but about whether to fully trust it. That hesitation is security’s opportunity. AI governance, done right, is a business differentiator. The faster security can establish runtime control, the faster they can unlock AI’s full potential for the entire organization.  

The companies that win in the AI decade will not be the ones that experiment the fastest. They will be the ones that scale responsibly by knowing what their agents are doing at every moment, understanding the dependencies, and being able to intervene before failures cascade. 

Security doesn’t have to be the reason AI stalls. With the right controls in place at runtime, it becomes the reason AI succeeds.