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CISSP 17.4 - Automating Incident Response (Part 1 of 2)
This episode of the ISC2 Certified Information Systems Security Professional (CISSP) exam prep series explores automating incident response from Domain 7, showing how automation and learning systems lighten the response workload. It covers the repetitive verify-and-respond work that wears analysts down and how machines can handle the routine flood while people focus where judgment matters most.
What this episode covers
- The problem automation solves — the slow, repetitive verify-and-respond work that is tedious and error-prone.
- Security orchestration, automation, and response — SOAR technologies that handle known incidents automatically.
- The upfront work — documenting every incident and its correct response before the tools can act on their own.
- Playbooks — the written plans defining how to verify an incident and respond, doubling as a manual backup.
- Runbooks — those playbooks implemented in an automated tool that carries out the conditional steps itself.
- Machine learning versus AI — ML starts with rules and improves, while AI starts with nothing and derives its own.
- Behavior-based detection — both approaches hunting anomalies from feedback, with model drift needing retraining.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What problem does response automation solve?
Traditionally an administrator handles every warning by hand, verifying the alert is real and then responding, often repeating the exact same rote steps a hundred times. Consider a flood attack on a screened subnet: tools raise the alert, an administrator confirms it, manually adjusts a server setting to weather the attack, and later reverses that change. That work is slow, tedious, and prone to human error, and automation targets exactly this kind of repetitive, well-understood task so people are freed for the judgment calls that truly need a human.
What is security orchestration, automation, and response?
Often shortened to SOAR, it is a group of technologies that let organizations respond to some incidents automatically. Instead of an analyst working every alert by hand, the system verifies and reacts on its own, following rules you defined in advance. The hard part is not the technology but the upfront work of documenting every known incident and its correct response, then configuring the tools to act. Do that well, and the machine handles the routine flood while your people focus on the unusual.
How do playbooks and runbooks differ?
These are the two halves of SOAR. A playbook is the written document, defining how to verify a given incident and laying out the response steps in plain language a person can follow. A runbook takes that playbook and implements it in an automated tool so the system carries out those conditional steps on its own. The playbook is the plan and the runbook is the plan put into motion, and the playbook doubles as a manual backup, so if the automation fails your team can still follow the written steps by hand.
What separates machine learning from artificial intelligence?
People often use the terms interchangeably, but they are not the same, and machine learning is actually a part of the broader field of artificial intelligence. A machine learning system starts with a set of rules or a baseline, then improves through experience, getting better as it gathers more data. An artificial intelligence system starts with nothing, no rules at all, and progressively learns them from feedback, then writes its own strategies as it goes, much like one player handed a rulebook versus another who must deduce the rules from winning or losing.
How do machine learning and AI apply to detection?
Behavior-based detection shows both at work. A machine learning system takes the administrator’s baseline of normal activity as its starting rules, watches for anomalies, and learns from feedback about which alerts were real, refining the baseline over time. An artificial intelligence system starts with no baseline, builds one purely from the traffic it observes, and hunts for anomalies as it learns. One caution: as the environment shifts, a learned model can drift out of step with reality, so continuous monitoring and retraining are needed to keep its judgments valid.
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Reference: This article is based on concepts discussed in CISSP 17.4 - Automating Incident Response (Part 1 of 2).