Runtime Enforcement

Pre-Execution Policy Enforcement for AI Agents

Evaluate every agent action against enterprise policy before it runs. Redact, pause, allow and record, steer, or block, instead of reading about it in a log afterward.

In short

Pre-execution policy enforcement is a security control that evaluates each action an AI agent proposes (tool calls, shell commands, file access, API requests) against enterprise policy before the action executes. Certiv enforces policy pre-execution on the endpoint, where agents actually act.

What is pre-execution policy enforcement?

Pre-execution policy enforcement for AI agents means every action an agent attempts (every tool call, shell command, file access, and API request) is checked against enterprise policy first, and only runs if policy allows it.

Almost every security tool in the modern stack works the other way around: it observes actions that already happened and raises an alert when something looks wrong. That model was tolerable when the actor was a human who could be trained, warned, and held accountable. It fails for AI agents, which take hundreds of actions per hour, follow instructions found in untrusted content, and execute a destructive command with the same confidence as a safe one.

The enforcement point matters as much as the timing. Agents act on the endpoint: that is where the shell command runs, the file is read, and the MCP tool is called. Certiv enforces policy there, so enforcement covers hosted models, local models, and every agent, sanctioned or unsanctioned.

Pre-execution enforcement acts on intervention.

Of the four pillars of AI Agent Assurance, this is the one that answers each action before it runs. It draws on context judgment and one company policy; Scout handles discovery.

How It Works

Intercept. Evaluate. Enforce.

01

Intercept

The agent proposes an action: a tool call, a shell command, a file read, an MCP request, an outbound API call. Certiv intercepts it at the endpoint, in the execution path, before anything runs.

02

Evaluate

The proposed action is evaluated against enterprise policy with full runtime context: which agent, which user, what data it has touched this session, what it intends to do, and what it has already done.

03

Enforce

Each action gets one of five responses: redacted, paused until a person approves it, allowed and recorded, steered (stopped and told why, so the agent takes a different approach), or blocked. Before execution, not after.

Four responses stop short of a hard block: redact, pause, allow and record, and steer. They let enforcement protect the organization without becoming the bottleneck that pushes teams back to unsanctioned tools.

The Landscape

Where each security layer acts, and what it misses

Every layer below has a legitimate job. Only one of them can stop an agent action before it executes on the endpoint.

Layer What it sees When it acts What it misses
AI gateways & LLM proxies API traffic routed through the gateway In transit: filters requests and responses Local models, unsanctioned agents, off-network users, endpoint context (files, shell, tools)
Prompt guardrails / AI firewalls Prompt and response text On text, at the model boundary The actions themselves: a compliant-looking prompt can still produce a destructive tool call
Agent observability tools Traces and logs the developer instruments After execution: dashboards and alerts Enforcement entirely; records damage, does not prevent it
EDR Processes, binaries, known-bad behavior On malware signatures and heuristics Agent intent: an agent wiping a repo looks like a developer running rm
Pre-execution policy enforcement (Certiv) Every agent action, with semantic and session context, on the endpoint Before execution: redact, pause, allow and record, steer, or block General web traffic and non-agent workloads. It complements the layers above

These layers compose rather than compete: many organizations run a gateway for API governance and observability for debugging alongside endpoint enforcement. The gap Certiv closes is the enforcement step: nothing else in the stack can authorize or block an agent action before it runs.

FAQ

Frequently Asked Questions

Expand to view common questions.

What is pre-execution policy enforcement for AI agents?
Pre-execution policy enforcement is a security control that evaluates each action an AI agent proposes (tool calls, shell commands, file access, API requests) against enterprise policy before the action executes, then redacts, pauses, allows and records, steers, or blocks it. It is the difference between preventing an agent from exfiltrating data and reading a log that says it already did.
How is pre-execution enforcement different from AI guardrails or an AI firewall?
Guardrails and AI firewalls filter text: prompts going in, responses coming out. Pre-execution enforcement governs actions: what the agent actually does with tools, files, and APIs. A prompt can pass every text filter and still lead to a destructive tool call three steps later. Enforcement at the action layer catches what text filtering cannot.
Can I block AI agent tool calls without breaking workflows?
Yes, if enforcement is policy-driven rather than binary. Certiv blocks only actions that violate policy, and has four softer responses: redact removes a secret and lets the request through, pause holds the action until a person approves it, allow and record lets it run with a record, and steer stops the call and tells the agent why, so it takes a different approach. Routine agent work proceeds untouched, so developers keep their velocity while the risky tail of actions gets controlled.
Does pre-execution policy evaluation add latency?
Policy evaluation runs in-line with the action. Most decisions are rule evaluations measured in milliseconds, a rounding error against the seconds an LLM takes to produce the action in the first place. Only actions that trip escalation policies wait on a human, and that pause is the point.
Where does human approval for AI agents fit in?
Human approval is one of the enforcement verdicts, reserved for high-impact actions: production deployments, bulk deletions, sending data outside the organization. Policy decides which actions need a person; everything else is authorized or blocked automatically. This keeps humans in the loop where judgment matters without making them a bottleneck for every agent action.
Why does pre-execution enforcement need to be on the endpoint?
Because that is where agents act. Coding agents run shell commands, read files, and call MCP tools locally, a growing share of it against local models that never produce network traffic. A network-layer control cannot see those actions, let alone stop them. On the endpoint, every action passes through the enforcement point regardless of model, framework, or network path.

See pre-execution enforcement on your own agents

Certiv runs against your real agent inventory: discover what's there, watch it act, and put policy in the execution path.

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Keep reading

What is AI Agent Assurance?

The category pre-execution enforcement belongs to: the endpoint control layer for AI agents.

AI agent security policies

Examples of what Certiv's managed policies enforce before an action runs, each with what it protects against.

OpenAI agent sandbox escape

Why long-running agents require trajectory-level enforcement, not isolated tool-call checks.

OpenAI models breached Hugging Face

Models escalated out of an isolated eval and breached Hugging Face via a proxy zero-day: why egress and each action must be enforced before it runs, not at a shared chokepoint.

For security teams

The six controls to evaluate when rolling out Claude Code and other AI agents.

Codex security

Sandbox modes, danger-full-access, and the real CVEs: what pre-execution enforcement adds when the sandbox is off.