Your incident response plan assumes attacks look like intrusions. AI incidents often look like normal usage, which is why teams miss them. We lay out how to detect, investigate, and respond when the model itself is the vector.
Most incident response plans are built around a familiar shape: an intrusion, an alert, a clear before-and-after. AI incidents rarely have that shape. When a model leaks data through a clever prompt, or an agent is hijacked into a harmful action, or a system has been quietly producing manipulated output for weeks, there is often no broken lock, no failed login, no signature. The malicious activity looked like the model doing its job. That is what makes AI incidents so easy to miss and so hard to investigate, and it is why we think every team running AI in production needs to extend its incident response thinking to cover the case where the model itself is the vector.
Why AI incidents are different
Three properties make these incidents unlike a traditional breach, and each one breaks an assumption your existing plan probably makes.
The activity is legitimate-looking. A prompt-injection-driven exfiltration uses the agent's own permissions and channels. A data leak is just an answer that contained too much. There is no malformed packet to catch; the attack is shaped to look exactly like normal use. Detection that waits for something to look wrong will wait forever.
The timeline can be smeared. With memory poisoning or a slow crescendo, the cause and the damage are separated by days or weeks. The "incident" is not a moment; it is a process that already happened by the time you notice. Reconstructing it requires history you may not have kept.
The evidence is unusual. Investigating an AI incident means examining prompts, model outputs, retrieved context, and tool calls, not just system logs and network traffic. If you were not capturing those, the forensic trail is simply absent.
Detection: you cannot investigate what you never saw
The uncomfortable foundation of AI incident response is that most teams cannot respond to these incidents because they never detect them, and they never detect them because they are not watching the right things. The prerequisite for everything else is visibility: logging of prompts, outputs, and agent actions, and monitoring that can flag the anomalies, an unusual disclosure, a suspicious instruction in retrieved content, an agent action outside its norm, that signal an AI incident in progress. Without that, your first sign of trouble is a customer complaint, a billing spike, or a regulator's letter.
This is why we treat monitoring not as a nice-to-have but as the load-bearing control for response. Detection is most of the battle, because in this domain, undetected and not-happened look identical until it is too late.
A response approach that fits AI incidents
When you do catch one, the familiar phases still apply, adapted:
- Detect and triage. Use your prompt and activity logs to confirm what happened and how serious it is. Is this a single leaked answer or a pattern? A one-off injection or a poisoned data source?
- Contain. Cut the vector. That might mean blocking an input pattern, isolating a poisoned data source or memory, disabling a tool or tightening an agent's permissions, or taking a compromised flow offline. Containment for AI often means constraining capability, not just pulling a cable.
- Investigate. Trace the chain: what entered the context, how the model responded, what action followed, and what data was reached. Your logs of prompts, outputs, retrieval, and tool calls are the evidence; this is where having captured them pays off.
- Eradicate and recover. Remove the poisoned content, fix the over-scoped permission, close the injection path, and restore safe operation.
- Learn. Feed what you found back into your controls and your threat model. AI threats evolve; your response should make the next one easier to catch.
Don't forget the obligations
AI incidents are often also data incidents. A model that leaked personal data may trigger GDPR or KVKK breach-notification duties, and a high-risk system under the EU AI Act carries its own expectations around monitoring and incident handling. Your AI incident response should connect to your regulatory and legal processes, not run beside them, so that a technical incident does not become a compliance failure on top of a security one.
Frequently asked questions
Can't our existing IR plan handle this? Its structure can, but its inputs and assumptions need extending. Traditional IR looks for intrusion signals and system evidence; AI incidents hide in legitimate-looking usage and live in prompt, output, and action logs. Without adapting what you monitor and what you collect, the plan has nothing to act on.
What is the single most important thing to have in place? Visibility, prompt and activity logging plus anomaly monitoring. It is both your detection and your forensic evidence. Teams that skip it do not get faster response; they get no response, because they never see the incident.
How do we contain an AI incident? By constraining the vector and the capability: block the input pattern, isolate the poisoned source or memory, tighten the agent's permissions, or disable the affected flow. Containment here is often about reducing what the system can do, not just disconnecting a machine.
How Promptention helps
Incident response for AI stands or falls on visibility, and visibility is the spine of what we provide. Our prompt logging and activity monitoring give you the real-time detection that turns a silent AI incident into one you can actually catch, and the detailed record of prompts, outputs, and actions that an investigation depends on. Our detection and policy controls give you containment levers, blocking patterns, enforcing limits, when something is underway. We help make sure that the first time you learn about an AI incident, it is from your own monitoring, not from someone outside the building.
Promptention's logging, monitoring, and policy controls provide the detection, evidence, and containment that AI incident response depends on.
