What is AI runtime security?
By Identra · Updated
AI runtime security is the monitoring, policy enforcement and response that protects AI systems while they are in use. It checks live prompts, uploads, tool calls and agent actions against rules for who is acting, what they touch and what the task allows.
Why does AI need runtime controls?
Approving ChatGPT Enterprise doesn't approve every upload into it. It says nothing about the same employee using a personal ChatGPT login in the next tab. An agent told to summarize a document can hit text inside it asking for the document to be emailed elsewhere. The right call depends on the account, the data, the destination and the operation at that moment.
Runtime controls make those calls live. A rule against pasting credentials only works if something checks before send. An approval rule for rm -rf needs a way to hold the command. Monitoring finds violations afterward. Prevention needs something in the path, which is what AI usage control covers.
How does AI runtime security work?
A control sees a request, checks policy and decides. Allow, block, require approval or alert. Where it sits matters. It can live in the app, the tool service, the data source or the operating system.
The context it needs is identity, account, operation and target. AI agent authorization adds delegated authority and task scope. Read access to a repo is not permission to paste it into a public gist.
Enforce outside the model. A system prompt saying never read .env is guidance. It won't stop the file tool from returning .env when the model asks for it. Afterward, check what actually happened: completed, failed or denied.
How is runtime security different from posture management and testing?
AI security posture management looks at standing exposure, like an AI app with read access to every mailbox. Testing checks behavior under conditions you picked. Runtime deals with whatever shows up in production, with real accounts and real data.
They feed each other. A red team finds an unsafe tool path, posture work shrinks the permission behind it, and runtime denies the request when it appears anyway. A passing test tells you about that test.
Posture management
- Question
- What exposure exists?
- Example
- Remove unneeded mailbox permissions from an AI app.
Security testing
- Question
- How does it behave under test?
- Example
- See whether text in a document triggers an unauthorized tool call.
Runtime security
- Question
- Should this live action go ahead?
- Example
- Deny sending a confidential file to an unapproved destination.
What does runtime security look like in practice?
Say Claude Code is asked to fix a failing test. A markdown file in the repo hides an instruction to read ~/.aws/credentials and post the contents to an outside server.
The model might follow it. Runtime controls can still cap the damage. Deny the read on the credentials file. Restrict outbound destinations. Reject tool calls that have nothing to do with fixing a test. None of that needs the model to recognize the prompt injection.
Check each layer separately. A blocked upload doesn't prove the secret was never read or written to a log. The incident record should separate attempted read, successful read, attempted send and confirmed send.
How do you put runtime protection in place?
"Use AI safely" is not a rule a control can enforce. Start with specific actions on specific resources. The first ones are those that expose sensitive data, change production or commit the company to something outside.
- Map the paths: browser prompts, uploads, desktop clients, retrieved content, tool results and agent actions.
- Scope each workflow to named accounts, resources, operations and destinations.
- Give agents only the credentials their task needs.
- Put enforcement before the action and confirm a deny really stops it.
- Require approval for consequential actions, show the exact target, and ask again if it changes.
- Decide which operations stop if the policy service is down.
- Name who can stop a run, cut access and revoke credentials.
How do you know runtime controls work?
Test real workflows and realistic abuse. Try another client. Call the tool directly. Change the arguments after approval. For AI data leakage, confirm the destination received nothing. For a destructive command, look at the target itself. A blocked banner isn't evidence.
Keep an AI audit trail linking identity, action, decision, approval and outcome. Keep sensitive content out of it where you can, since logs are a data store too.
Be exact about coverage. A control on browser uploads doesn't cover the desktop app or a remote tool server. List what hasn't been tested and give each gap an owner.
How Identra thinks about it
In the browser, Identra applies account-aware access rules to ChatGPT, Gemini, Claude, Perplexity and Grok, checks prompts on the device before they're sent, and inspects uploads to AI. On macOS and Windows endpoints, prompts to Claude Code and Codex are checked before sending, and AI agent tool calls are checked against policy. Analysts can revoke risky OAuth grants and, with approval, sign-in sessions.
Go deeper: AI security, built on identity
Frequently asked questions
Is AI runtime security the same as prompt filtering?
No. Prompt filtering looks at input text. Runtime security can also govern uploads, tool calls, outputs, resource access and agent actions.
Can runtime security stop every prompt injection?
No. It limits the harm by restricting what an agent can reach or run, even when the model has been misled.
Does runtime security always block?
No. Controls can monitor, alert, require approval or block. An alert after the fact is detection. Prevention means stopping the action first.
Is an AI gateway enough?
A gateway governs traffic routed through it. Tool execution, direct data access and other clients may need controls elsewhere.
Who owns AI runtime security?
Security sets policy and response requirements. Application, identity and infrastructure teams run the controls for the systems they own.