Introduction

Security incidents and outages are no longer just about servers, firewalls, and misconfigured APIs. GenAI-driven threats, model abuse, prompt injection, data leakage through LLMs, and automated attacks are reshaping what “incident response” even means. Traditional frameworks—built around well-known malware families or predictable network intrusions—often struggle to keep up.

Yet in the middle of a complex, multi-team, multi-tool outage, one surprisingly low-tech artifact can bring order to chaos: a single, physical paper notebook.

Think of it as a railcar log—a paper record that travels with the incident from phase to phase, team to team, like a railcar coupled to different locomotives along a route. While observability tools, ticket systems, SIEMs, and chat platforms come and go throughout the lifecycle of an outage, the railcar log remains a consistent, human-readable source of truth.

This post explores how a simple analog notebook, when combined with strong runbooks and integrated workflows, can dramatically improve the way teams manage modern, GenAI-era incidents.

Why Traditional Incident Frameworks Are Struggling

Most established incident response frameworks grew up in a world where:

• Systems were mostly on-prem or in a small number of clouds.
• Threats followed familiar patterns: phishing → credential theft → lateral movement.
• Logs and alerts were relatively siloed, and AI played a minimal role.

GenAI changes that:

• New incident types: prompt injection, data exfiltration via model outputs, model poisoning, misuse of AI agents, and jailbreaks.
• Faster propagation: automated agents can make wrong or malicious decisions at machine speed.
• Messy boundaries: incidents can cross product, security, ML, data governance, and legal domains.

Traditional frameworks often don’t describe:

• How to treat an LLM as an asset with its own threat model.
• How to triage incidents caused by AI (e.g., misclassification, hallucination) vs. incidents against AI (e.g., prompt injection, data theft).
• How to coordinate across the many tools and teams involved in AI-centric systems.

What they still do offer is structure: phases like detection, triage, containment, eradication, and recovery. The challenge is tying all of these moving parts together in a way humans can actually follow when the pressure is on.

That’s where a single, physical railcar log can help.

The Railcar Log: A Single Analog Source of Truth

A railcar log is a paper notebook dedicated to incidents. Each incident gets its own segment with:

• A unique incident ID.
• Start date and detection context.
• Key contacts and owners.
• Timeline of major decisions, actions, and hypotheses.
• References to relevant tickets, dashboards, and tools.

The goal is not to replace digital systems, but to provide a stable, cross-phase narrative that isn’t fragmented across Jira, Slack, your SIEM, and a dozen dashboards.

Why Analog Still Works (Especially When Things Get Weird)

1. Tool-agnostic continuity
During a critical outage, teams might:

• Switch from one monitoring tool to another.
• Migrate tickets across systems.
• Escalate from a local team to a global SRE or security group.

The railcar log stays put. It carries the incident’s memory across these transitions. When a new team joins the call, they don’t need to trace 500 Slack messages; they can skim the railcar log and get the curated story so far.

2. Cognitive simplicity under stress
In high-pressure situations, humans benefit from:

• A single place to write the next fact.
• A single place to read the last decision.

The physical act of writing forces the scribe to summarize and clarify. This naturally surfaces contradictions and missing information that may be buried in digital noise.

3. Resilience when systems fail
If identity systems break, VPNs fail, dashboards become unavailable, or your GenAI assistant is down, the paper notebook still works. It’s an always-on fallback.

Runbooks: Turning Ad-Hoc Firefighting into Repeatable Practice

A railcar log on its own is just a narrative. To make incidents manageable and repeatable, you need runbooks.

Runbooks are documented, step-by-step procedures that specify:

• When to trigger a response (e.g., “If GPT-powered agent touches production config without change ticket…”).
• Who should be involved (owners, approvers, SMEs).
• What to check (logs, dashboards, controls).
• How to make decisions (criteria for escalation, containment, or recovery).

For GenAI-related incidents, you might have runbooks like:

• “Prompt Injection Response for Internal LLM Tools”
• “LLM Data Leakage Suspected: Containment and Forensics”
• “Model Misbehavior in Production: Rollback and Safeguard Review”

These turn panic into process. Anyone on the on-call rotation can pick up the runbook, follow the steps, and log their actions into the railcar notebook.

Branching Logic and Templates: Standardized Yet Adaptive

Not every incident follows the same path. That’s where branching logic and templates come in.

Branching Logic

Runbooks should include decision points:

• If the model output exposed personally identifiable information (PII):
  • Notify privacy and legal.
  • Trigger PII incident sub-runbook.
• If the incident appears to involve external attackers:
  • Engage security IR team.
  • Preserve relevant logs and artifacts.
• If only internal test data is impacted:
  • Contain and proceed to engineering-focused remediation.

Branching logic recognizes that the same initial alert—say, “unexpected LLM output”—can lead to very different obligations and response paths.

Templates

Templates systematize what needs to be captured every time:

• Incident summary (in one or two sentences).
• Suspected scope and blast radius.
• Systems and models involved.
• Hypotheses and tests run.
• Decisions, with timestamps and owners.

Your railcar log should mirror these templates by:

• Having a standard first-page layout per incident.
• Using the same headings for every incident.
• Leaving space for branching sections (e.g., Legal, Comms, Security Forensics).

This keeps analog and digital worlds aligned: the information your tooling expects is the information your scribe is collecting in the notebook.

Keeping Runbooks Aligned with Evolving GenAI Systems

GenAI systems and threats evolve faster than most documentation. A runbook written six months ago might not reflect:

• New models or vendors you’ve adopted.
• Additional guardrails, red-teaming procedures, or logging pipelines.
• Fresh attack patterns observed in the wild.

To keep your runbooks—and thus your railcar log patterns—relevant:

1. Schedule regular reviews (e.g., quarterly) focused specifically on AI-related runbooks.
2. Use post-incident reviews to capture gaps:
   • What steps were missing or unclear?
   • Which decisions were made ad hoc and should now be codified?
3. Incorporate threat intelligence about AI and LLM-specific attacks.
4. Adjust your templates so the railcar log collects new types of data you now know you’ll need.

Every time your systems or AI stack changes in a meaningful way, ask: Which runbooks and railcar log templates does this impact?

Integrating Analog Logs with Digital Workflows and Best Practices

The railcar log is most powerful when woven into your broader incident management practices.

Early Detection and Clear Ownership

• Tie monitoring alerts (including AI-specific signals like unusual prompt patterns or API usage spikes) to standard runbook entry points.
• Assign incident commander and scribe roles explicitly at declaration time.
• The scribe is the railcar log owner for the duration of the incident.

In the log, the first page for each incident should record:

• Who declared the incident.
• Who is IC (Incident Commander) and scribe.
• Where the canonical digital workspace is (ticket ID, Slack channel, war room link).

Automation Where It Helps

Automation can:

• Open tickets when specific alert thresholds are met.
• Create incident chat channels with the right people.
• Pre-populate structured fields with known data.

Your runbooks should state:

• Which steps are automated.
• Which outputs must be manually copied or summarized into the railcar log.

The analog log is not a full export of your tools; it’s a curated narrative that references them.

Tracking Execution Across Tools and Phases

As the incident moves from:

1. Detection & triage
2. Containment & mitigation
3. Eradication & recovery
4. Post-incident review

…the railcar log should continue to:

• Capture key decisions and timestamps.
• Reference specific runbook steps and branches taken.
• Note which systems or tickets now own the next actions.

For example:

2026-02-24 10:43 – Followed AI-IR-02 runbook, Section 3b (Prompt Injection Suspected). Containment branch selected: disabled external prompt source and rotated key. See Jira IR-3245 for worklog.

At the end of the incident, during the post-incident review, the railcar log becomes a backbone for reconstructing the storyline and identifying documentation updates.

Conclusion: Old Paper, New Problems, Better Control

Modern, GenAI-driven incidents challenge traditional response models. Systems are more complex, responsibilities span more teams, and threat patterns are still emerging.

In that environment, a single analog railcar log offers something uniquely valuable:

• A stable narrative that moves with the incident across phases and tools.
• A human-friendly anchor in the noise of alerts, dashboards, and chat threads.
• A bridge between structured runbooks and messy real-world execution.

Combined with well-designed runbooks that include branching logic and are regularly updated for evolving AI systems, the railcar log turns chaotic firefighting into a disciplined practice. And when integrated tightly with your digital workflows—detection, automation, ownership, reviews—it helps ensure that every incident, no matter how novel or AI-driven, is handled with clarity and coordination.

In a world of fast-moving GenAI threats, the most effective incident response may be part cutting-edge automation, part robust process, and part old-fashioned ink on paper.