Ai Incident Response: Llm & Agent Failures In Production
Published 8/2026
Created by Dr. Amar Massoud
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate |
Genre: eLearning |
Language: English |
Duration: 41 Lectures ( 3h 59m ) |
Size: 3.5 GB
Detect, contain and recover from prompt injection, tool abuse, agent loops and hallucination incidents in production.
What you'll learn
⚡ Instrument an LLM or agent stack so incidents are visible - prompts, completions, tool calls, retrievals and cost
⚡ Triage an AI incident in the first five minutes and score its severity on blast radius, autonomy, data class and reversibility
⚡ Contain a misbehaving agent without making things worse - kill, throttle, revoke, roll back, isolate
⚡ Execute a named playbook for each of the eleven common LLM and agent failure modes
⚡ Preserve evidence and reconstruct root cause on a system that will not reproduce on demand
⚡ Recover safely - purge poisoned state, restore progressively, and gate re-enablement on evals
⚡ Run a blameless, model-aware post-incident review and stand up an AI incident response programme
Requirements
❗ Comfortable with Python and the command line
❗ Basic understanding of LLM APIs and tool/function calling
❗ Prior security or SRE incident experience helps, but is not required
❗ No attack-development experience needed - every lab incident is handed to you in progress
❗ A machine that can run a small local model (labs use Ollama - no API key, no cost)
Description
This course contains the use of artificial intelligence.
It is four in the morning. Your autonomous agent has just sent fourteen hundred customers a letter nobody approved. Uptime is fine. Latency is fine. The error rate has been zero all night. Every dashboard you own is green - and that is exactly the problem.
This is an operations course. Not governance, not red teaming, not architecture. The alarm has already fired, and you are the one holding the pager. You will learn to detect, triage, contain, preserve evidence, investigate and recover from failures in deployed LLM and agent systems - organised by failure mode, never by framework.
Everything is taught against
Meridian Health, a fictional insurer running an LLM claims assistant and an autonomous operations agent with tool access to a claims database, outbound email and an internal MCP server. Every incident you respond to happens to Meridian first.
What makes this course different
✨ Built on the Coalition for Secure AI (CoSAI)
AI Incident Response Framework V1.0 - the first genuinely authoritative reference in this field - and made operational rather than summarised.
✨ Eleven named playbooks, each following the same five-part spine: signals, containment, evidence, eradication, recovery gate.
✨ Real cases, including the Canadian tribunal decision that rejected "the chatbot is a separate legal entity" defence, and the documented 2026 runaway-cost incidents.
✨ Cost-as-an-incident is covered properly - under-developed even in the frameworks, and the best-documented failure mode of 2026.
You will leave with five artefacts you can put into production: an AI incident severity matrix, containment procedures for your own stack, your own incident playbook, an evidence checklist with chain of custody, and a model-aware post-incident review template. The capstone assembles all five with a gap statement and a 90-day roadmap.
Nine hands-on labs run entirely on your own machine against a local model - no API keys, no cloud spend. You will instrument an agent, score live incidents, run a timed containment drill, reconstruct an incident from raw evidence, and rebuild an eval gate that refuses to open until the fix is proven.
No attack-development experience is needed. Every incident in the labs is handed to you already in progress. You are the responder, never the attacker.
Who this course is for
⭐ SOC analysts and incident responders whose organisation just put LLM or agent systems into production
⭐ AI platform, MLOps and SRE engineers who own the agent stack and are the de-facto responders
⭐ Security leads and AppSec engineers accountable for AI systems they did not build
⭐ DFIR practitioners moving into AI incidents who want to know what to actually seize
⭐ GRC and risk professionals who need to understand what an operational AI response involves
Homepage
Код:
https://www.udemy.com/course/ai-incident-response-llm-agent-failures-in-production
Ai Incident Response Llm & Agent Failures In Production
