Mythos & Fable Can Weaponize Cyberattacks. This n8n System Fights Back

n8n ·2026-06-16 ·1 min read

Summary written by us from the video's transcript. The video, and everything in it, is n8n's work.

Learn how to integrate AI into cybersecurity incident response using an n8n workflow that combines historical tickets, playbooks, and threat intelligence while keeping the AI constrained.

Takeaways

Vector embeddings let the workflow quickly surface past incidents that share indicators such as IP addresses or domains.
Structured output parsers keep the AI’s responses predictable and safe for security operations.
Playbooks act as a second knowledge source, guiding the AI with human‑crafted response steps.
Threat‑intelligence lookup, even a simple web search, adds up‑to‑date context for novel attacks like zero‑days.
The whole pipeline is model‑agnostic; you can swap in more powerful LLMs (e.g., Mythos) as they become available.

Why add AI to SecOps?

The speaker explains that attackers are already automating with AI, so defenses need similar automation. Adding AI can speed up triage of incident tickets but must be carefully controlled because security data is sensitive.

Data foundation for the workflow

Three key data sources power the system: a library of test incidents, reference playbooks for common attacks, and a collection of resolved incidents to provide context. These are stored in a Supabase vector database using embeddings for similarity search.

Workflow architecture

A webhook receives an incident JSON payload and branches into three parallel steps. The first looks up similar historical tickets via vector similarity, the second consults relevant playbooks, and the third performs external threat‑intelligence lookup (currently a Google search). Structured output parsers enforce strict response formats at each step.

Using embeddings for historic context

All past incidents are vectorized; when a new ticket arrives the workflow finds the most similar ones, extracts common root causes (e.g., look‑alike domains) and recommended remediation steps. The demo showed three strong matches being returned for a phishing test case.

Playbooks and threat intel

Playbooks provide step‑by‑step guidance for frequent attack types such as phishing. Publicly available or vendor‑supplied playbooks can be imported. Threat‑intel is the lowest‑priority branch but becomes critical for zero‑day exploits like those discovered by Anthropic’s Mythos model.

Practical considerations

The system is model‑agnostic; any LLM (including Mythos) can be plugged in. Success depends on clean, well‑organized data—SOPs, incident logs, and playbooks must be maintained. As AI‑driven attacks increase, the volume of tickets may grow tenfold, making automated triage essential.