AI intervention is the runtime control function that approves, modifies, or blocks an AI agent's action at the moment of the action, rather than allowing it and detecting the problem afterward. It is the third core control function of an AI control plane, after discovery, which establishes what exists, and observation, which captures what those systems do.
- Intervention acts at action time, while detection reports once the action is done
- Requires inline placement, because an alert cannot stop a tool call already fired
- Policy has to be authored in language the accountable operator can read and audit
- Five enforcement actions: alert, block, mask, steer, and ask
Why is AI intervention important?
Intervention matters because detection produces alerts while intervention produces outcomes. An alert thirty seconds after a destructive tool call is not a control – it is forensics with better timing. Few vendors in AI security define intervention as a distinct control function, which is precisely why it warrants one.
The distinction only became consequential when agents started acting rather than answering. A model that returns text can be reviewed before anyone acts on it. An agent that writes to a production database, moves funds, or commits code has already changed the world by the time a log entry appears. CrowdStrike documented malicious prompts injected into legitimate AI tools at more than 90 organizations in 2025, used to generate commands that stole credentials and cryptocurrency.
There is a second reason, and it concerns adoption rather than defense. Security teams asked to approve broad agent deployment without the ability to stop an action tend to refuse, or narrow the scope until the deployment stops being useful.
What is AI intervention?
AI intervention is the enforcement step between an agent's decision and its execution. When an agent attempts an action – calling an external API, reading a sensitive record, escalating its own permissions, running a command – the intervention layer evaluates that attempt against policy and returns a decision before anything executes.
What separates real intervention from a nominal version is placement first of all. Anything observing from the side can only describe what already happened, so the layer has to sit in the request path. Policy then has to be readable, so the rules can be authored and reviewed by the people accountable for them rather than only by the platform team. And the available actions have to reach past block, because a control that can only refuse forces a choice between security and usefulness.
Five enforcement actions are available, and the range is what distinguishes a mature layer. Alert records the action and lets it proceed. Block stops it outright. Mask returns a result with sensitive elements obscured so the agent can keep reasoning. Steer sends the action to a safer target, or adjusts it until it satisfies policy – narrowing a query, stripping a field, reducing a scope. Ask routes the decision to a human when the action crosses a threshold warranting judgment.
Types of AI intervention
Intervention divides along two useful lines: what triggers it, and what it does once triggered.
By trigger, policy-based intervention fires on a defined rule – this agent may not call that tool, this data class may not cross that boundary. Anomaly-based intervention fires on deviation from an established behavioral baseline, which catches the case nobody wrote a rule for. Threshold-based intervention fires on consequence rather than content, holding anything irreversible or high-value for review regardless of which agent asked.
By response, blocking stops the action outright and suits a narrow set of clearly prohibited operations. Steering keeps the task moving inside policy, and it is the right default for the large middle ground where an action is neither clearly safe nor clearly forbidden. Escalation hands the decision to a person, which is where human review belongs: on consequential and ambiguous actions rather than on everything.
The design question is how much traffic lands in that middle ground. Teams that implement only blocking find out it is most of it.
AI intervention & Onyx
Intervention is one of the three differentiators of the Onyx Secure AI Control Plane.
Steering redirects or modifies an action at action time rather than only blocking it, so an agent's task completes within policy instead of failing at the boundary. Just-in-Time access evaluates context at the request boundary rather than at role assignment, so an agent holds the permissions a specific action requires and nothing beyond it. The AI-native action surface was designed for agent actions specifically rather than adapted from human-user controls, which is what lets policy address tool calls and reasoning steps directly, against the agent identity that made them.
Enforcement runs inline across SaaS, cloud, endpoint, and code, with every decision recorded. Runtime and prompt injection defense depends on it.
