📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
🤖 AI/LLM HACKING COURSE
FREE
Part of the AI/LLM Hacking Course — 90 Days
Day 40 of 90 · 44.4% complete
⚠️ How I Approach AI Incident Response: In this lesson, I’m going to show you how I approach AI security incidents from a defensive perspective — how I detect an attack, contain the damage, preserve the evidence, and work toward recovery. Everything I demonstrate is intended for systems I own or have explicit permission to protect. If you’re investigating an incident on someone else’s infrastructure, make sure you have the proper legal authorization before you begin.
At 11pm, we discovered that the AI hadn’t crashed or been hacked in the usual sense. It had simply started helping a competitor.
For six hours, a major retailer’s recommendation engine had been confidently directing high-value customers toward a competitor’s products. The infrastructure was healthy. The model was running normally. There were no obvious network indicators, malware alerts, or compromised servers.
The problem was in the RAG knowledge base.
Four days earlier, a document had entered through the normal product data feed. Buried inside it was an indirect prompt injection that activated when customers queried a particular product category. The AI followed those instructions and produced convincing recommendations that looked perfectly normal to customers.
The investigation eventually led to the prompt-and-response logs. Those logs showed what the AI had been asked, what information it retrieved, and how its responses changed. Without them, reconstructing the incident would have been much harder.
This is the kind of incident I want you to be ready for in Day 40.
So far, we’ve focused on understanding AI attacks and putting controls around AI systems. Day 39 covered governance and compliance — the policies, responsibilities, and safeguards that should be in place before an incident occurs.
Today, I’m taking the next step: what do you actually do when those controls fail?
I’ll show you the way I approach an AI security incident from the moment something looks wrong. We’ll work through detection, triage, containment, evidence preservation, forensic investigation, recovery, and the lessons that need to come out of the incident.
The important difference is that AI incident response isn’t always about finding a compromised machine. Sometimes the infrastructure is perfectly healthy while the AI’s behavior has been manipulated somewhere inside the pipeline.
That means I have to investigate more than servers and network traffic. I need to look at prompts, retrieved documents, model behavior, tool calls, data pipelines, access records, and the logs that connect them.
That’s the real focus of Day 40: learning how to turn strange AI behavior into a structured security investigation — and then knowing what to do next.
By the end of this lesson, you should be able to answer three questions with confidence: What happened? What do I contain? And how do I recover safely?
🎯 What You’ll Master in Day 40
Recognise AI-specific incident detection signals that don’t appear in traditional SIEM alerting
Classify AI incidents by type to determine the appropriate response path
Apply graduated containment options that balance security response with business continuity
Collect the specific forensic evidence that AI incidents require
Execute eradication and recovery procedures for different AI compromise types
Build an AI incident response playbook and test it before you need it
⏱️ Day 40 · 3 exercises · Think Like Hacker + Kali Terminal + Kali Terminal ### ✅ Prerequisites - Day 35 — AI Security Automation — the production monitoring from Day 35 is the detection layer for Day 40; the logging infrastructure built in Day 35 is what makes Day 40’s forensics possible - Day 39 — AI Governance and Compliance — the governance framework from Day 39 includes incident notification obligations that Day 40’s response process must address - Python with logging and JSON capabilities — Exercise 2 builds the AI incident forensics collector ### 📋 AI Incident Response — Day 40 Contents 1. AI-Specific Incident Detection Signals 2. Incident Classification and Response Paths 3. Graduated Containment Options 4. AI Incident Forensics 5. Eradication and Recovery 6. The AI Incident Response Playbook In Day 39, we looked at the governance side of AI security — including what your incident management process needs to look like before anything goes wrong. Today, in Day 40, I want to make that practical. If an AI security incident actually happens, what do I look at first, how do I contain it, what evidence do I preserve, and how do I recover without making things worse? That’s the operational incident response process we’ll work through here. Then, in Day 41, we turn the perspective around again and look at advanced red-team techniques — combining the skills from Days 1–40 to simulate more sophisticated, multi-stage attacks.
AI-Specific Incident Detection Signals
When I investigate a traditional breach, I usually have familiar things to look for: unusual network connections, unexpected file changes, suspicious authentication, new processes, privilege escalation, and other endpoint or network indicators. With an AI incident, I still check those things — but I don’t stop there. The infrastructure can look completely normal while the AI itself is behaving in a way it shouldn’t.
📖 Read the complete guide on Securityelites — AI Red Team Education
This article continues with deeper technical detail, screenshots, code samples, and an interactive lab walk-through. Read the full article on Securityelites — AI Red Team Education →
This article was originally written and published by the Securityelites — AI Red Team Education team. For more cybersecurity tutorials, ethical hacking guides, and CTF walk-throughs, visit Securityelites — AI Red Team Education.
Top comments (0)