Defining the Category

Runtime Assurance for AI Agents

The control layer that ensures AI agents behave safely and within enterprise policy while performing work on employee endpoints and enterprise systems.

In short

Runtime Assurance is the new security category for AI agents — the control layer that enforces what agents can do, access, and call at the moment of execution. It sits next to EDR and CASB in the modern enterprise security stack.

The Problem

Securing AI Agents Demands a New Paradigm

AI agents are effectively autonomous insiders with broad access. Existing security tools cannot govern them. Each action looks clean in isolation, but the chain of decisions and combinations creates a hidden threat. And no single layer can effectively see, understand, or control them.

Read CRM Query DB Send Email Firewall IAM DLP AI AGENT Unprecedented efficiency and risk LETHAL TRIFECTA
Existing Solutions Fall Short

The Agentic Control Gap

Today's security stack has a twofold gap when it comes to AI agents:

1. No semantic understanding. Existing tools see user identities, network packets, system calls, or API tokens, but none of them understand what an agent is reasoning about, why it’s making a decision, or what it intends to do next. Without that context, you can’t distinguish a legitimate workflow from a dangerous one.

2. No pre-execution enforcement. Even when something is detected, it's detected after the action has already been taken. Logs tell you what went wrong. Alerts notify you that damage occurred. But no layer exists to stop the action before it happens.

High
Runtime Control
Low
Blind Enforcement
The Gap Agent Centric Controls
Flying Blind
Passive Monitoring
Low High
Semantic Visibility
Four Pillars

The Architecture of Runtime Assurance

01

Complete Agent Context at the Endpoint

AI agents perform work locally: accessing files, interacting with development environments, running tools, calling APIs, and connecting to SaaS platforms. Most security tools see only partial signals.

  • Full visibility into tool execution and data access
  • Workflow step tracking and environmental context
  • Complete vantage point required to govern agents effectively
02

Pre-Execution Control

Traditional security tools respond after actions occur. By that point, damage may already be done. Certiv evaluates requests before an agent executes a tool, script, or high-risk operation.

  • Policy evaluation happens before execution, not after
  • Non-compliant actions are blocked, redirected, or escalated
  • Prevents harmful actions before they occur
03

Intent-Based Policy Enforcement

Traditional security relies on static rules: block this command, restrict this destination, deny this API call. AI agents don’t operate through isolated actions; they pursue goals and plans.

  • Policies evaluate why an agent acts, not just what it does
  • Allow: "Agents may analyze source code for refactoring"
  • Block: "Agents cannot deploy infrastructure without approval"
04

Runtime Flow Protection

Even well-configured agents can become dangerous through prompt injection, adversarial inputs, tool misuse, or behavioral drift. Over time, agents can effectively become insider threats.

  • Detects unexpected data access and suspicious tool usage
  • Identifies abnormal workflow sequences in real time
  • Ensures agents remain aligned throughout their execution lifecycle
Why the Endpoint

The Work Happens Here

Instead of centralized AI systems, organizations are deploying agent capabilities directly to employees. These tools run on developer machines and employee workstations, not in controlled server environments.

Coding Agents
AI Copilots
Workflow Assistants
Knowledge Agents

Without endpoint-level visibility and control, organizations cannot fully govern what these agents do. Runtime Assurance brings governance to where the work happens.

The Category in Three Lines
1

AI agents are autonomous software acting with enterprise privileges.

2

Existing security tools lack full visibility and control over agent behavior, especially on endpoints.

3

Runtime Assurance ensures agents operate safely by enforcing intent-aware policies before and during execution.

FAQ

Frequently Asked Questions

Expand to view common questions.

How is Runtime Assurance different from AI guardrails?
AI guardrails typically operate at the model or API layer, filtering prompts and responses as they pass through a gateway. Runtime Assurance operates at the endpoint where agents actually perform work. It sees the full context of agent behavior including tool execution, data access, and workflow steps, and enforces policies before dangerous actions execute. Guardrails filter text; Runtime Assurance governs behavior.
Why does the endpoint matter for AI governance?
AI agents increasingly run on employee workstations: coding agents, copilots, workflow assistants, and knowledge tools. The endpoint is where agents interact with files, execute tools, access data, and call APIs. Without visibility and control at this layer, organizations have blind spots in their AI governance. Network-level or cloud-level tools cannot see the full context of what agents do locally.
What does pre-execution control mean?
Pre-execution control means evaluating an agent's intended action against enterprise policy before it executes. If an agent attempts to run a script, access sensitive data, or call a restricted API, Certiv intercepts and evaluates the request first. Non-compliant actions can be blocked, redirected to a safe alternative, or escalated for human approval, preventing damage before it occurs rather than detecting it after the fact.
How does intent-based policy enforcement work?
Traditional security uses static rules: block this command, restrict this network destination. Agent behavior is more complex; agents pursue multi-step goals. Intent-based policies evaluate the purpose behind an action, not just the action itself. For example, a policy might allow an agent to "analyze source code for refactoring" while blocking it from "deploying infrastructure changes without approval." This aligns security with how agents actually work.
Is Runtime Assurance a replacement for existing security tools?
No. Runtime Assurance complements your existing security stack. EDR protects against malware. SIEM aggregates logs. CASB governs cloud access. Runtime Assurance adds the missing layer: governance of AI agent behavior at the endpoint. It integrates with your existing tools while providing the agent-specific visibility and control they were not designed to deliver.
Who needs Runtime Assurance for AI Agents?
Any organization where employees use AI agents in their daily work. This includes companies deploying coding assistants, AI copilots, workflow automation agents, or any AI tool that can access enterprise data, execute tools, or interact with systems. As AI agent adoption accelerates, the gap between agent capabilities and governance controls widens. Runtime Assurance closes that gap.
Learn More

See Runtime Assurance in Action

Discover how Certiv brings governance to where AI agents actually work.

Book a Demo See the Product

Keep reading

The Lethal Trifecta

The canonical example of an AI agent risk pattern Runtime Assurance was built to stop.

Our approach

Why existing security tools can't deliver runtime assurance, and how Certiv's architecture can.

See the product

Runtime assurance in practice: discover, understand, control, and protect every AI agent.