Runtime Governance 101: The Critical Role of Runtime Governance in Securing Agentic AI

As AI systems evolve from reactive models into agentic systems capable of autonomous decision-making, security requirements are fundamentally changing. Traditional controls were not designed for systems that can reason, adapt, and act independently in real time.

In a recent FastChat, Rampart-AI CEO Lee Krause broke down why runtime governance is becoming essential for securing agentic AI... and why existing approaches fall short.

What Is Runtime Governance?

Runtime governance focuses on controlling and validating what an AI agent does while it is operating, not just how it is designed.

As Krause explains:

“Governance really is looking at the standpoint of controlling from an enterprise standpoint, how agentic systems are used, determining their goals and objectives, tools that they can work with, foundation models they work with, and data that they work with.”

But runtime governance goes a step further.

“Runtime governance is ensuring that it does what’s expected.”

This means continuously verifying that an AI agent’s actions align with its intended purpose — even as it encounters new data, tools, or situations.

The Building Blocks of Runtime Governance

According to Krause, runtime governance spans multiple layers of control and visibility, including:

“The identity of the system, guardrails that protect the foundation model… controlling the interface to other tools, ensuring tools are called appropriately, controlling the decision-making, [and] transparency ... being able to understand how decisions are made and why.”

Equally important is knowing when autonomy should pause:

“Finally… when to stop, when you’ve gone too far, you’re outside of your safe zone, and when to ask humans for approval.”

These controls transform AI from an opaque system into one that is observable, auditable, and governable in real time.

Why Traditional Security Tools Fall Short

Legacy security approaches like EDR and MCP were not built for autonomous reasoning systems.

“EDR is looking at the endpoints, what’s happening on your system, whereas runtime governance is looking at the agentic system itself and how it’s operating.”

While MCP defines what tools and data an agent can access, it does not ensure those tools are used safely:

“What runtime governance is doing is ensuring that you’re using the right one at the right time and ensuring that you’re not being pushed off course.”

In other words, access control alone isn’t enough when systems can decide how and when to act.

Defining the “Safe Zone” for Autonomous AI

Agentic AI introduces flexibility and uncertainty.

“Traditional systems pretty much do what they’re told to do in the sequence they’re told to do it. In agentic systems, you’re giving them flexibility to make decisions themselves.”

This is where runtime governance becomes critical.

“It’s important to understand what is the safe zone of freedom you’re going to allow it to operate in.”

Static policies break down when behavior is dynamic:

“Static policies work when you know everything that’s going to happen. It’s when things are going to change that static policies fall short.”

Runtime governance evaluates every action against goals, objectives, and constraints as the system operates.

Balancing Trust and Control in Real Time

One of the hardest challenges in agentic AI is maintaining trust without limiting innovation.

“It really falls back to this whole concept of what’s inside and outside of the safe zone.”

When an agent’s actions align closely with its goals, autonomy is safe. When it starts pushing boundaries:

“That’s where there’s a need for the system either to ask humans to approve the actions… or to run internally and prove to itself that it’s safe.”

This dynamic decision-making framework enables responsible autonomy instead of blind trust.

Why Runtime Governance Is Now Non-Negotiable

AI systems today continuously ingest data from the outside world — data that appears trustworthy but may not be.

“You’re taking in information from the outside world that you perceive to be trustworthy and you need to be able to prove that it’s trustworthy at runtime.”

That’s the fundamental shift:

“Ensuring that prompts, data, language models, or MCP tools make sense for the goals and objectives of what you’re trying to do.”

Without runtime governance, agentic systems operate unchecked. With it, organizations gain resilience, visibility, and confidence in autonomous AI.

Learn more about Rampart AI’s runtime governance solution and how it keeps agentic systems secure, resilient, and trustworthy at runtime.