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Investigating Gcp Incidents

Community
trilwu
investigating-gcp-incidents

Investigate a security incident in Google Cloud — establishing what audit logging exists before trusting a gap, reconstructing activity from Cloud Audit Logs, triaging service-account and OAuth abuse, following Security Command Center findings, and scoping IAM and resource changes. Use when responding to a suspected GCP compromise, investigating a leaked service-account key, working a Security Command Center or Event Threat Detection alert, or reconstructing what a principal did across a GCP organization.

Overview

Publishertrilwu
Repositorysecskills
Skill nameinvestigating-gcp-incidents
Stars
144
Forks
15
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by trilwu on GitHub. Read the source before you install it.

Installation

Install the Investigating Gcp Incidents AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/trilwu/secskills.git /tmp/secskills
mkdir -p .claude/skills
cp -r /tmp/secskills/secskills-defense/skills/investigating-gcp-incidents .claude/skills/investigating-gcp-incidents
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Investigating Gcp Incidents in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Investigating Gcp Incidents on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Investigating Gcp Incidents is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Investigating GCP Incidents

The completeness of a GCP investigation is decided before the incident, by which audit logs were enabled. The single most damaging mistake is reading an empty query result as "nothing happened" when the real answer is "that log category was never turned on." Establish visibility first; conclude second.

GCP's audit model is not AWS's and not Azure's. Two categories are always on and two are mostly off, and knowing which is which is the difference between a scoped investigation and a false all-clear.

When to Use

  • Responding to a suspected compromise in a GCP project or organization
  • Investigating a leaked or abused service-account key
  • Working a Security Command Center, Event Threat Detection, or Chronicle alert
  • Reconstructing a principal's activity across projects
  • Scoping IAM policy or resource changes after a suspected privilege escalation

When NOT to Use

  • The incident is in AWS — use investigating-aws-incidents; in Azure or Microsoft 365 / Entra — use investigating-azure-incidents or investigating-m365-entra
  • Attacking GCP rather than investigating it — use exploiting-cloud-platforms
  • A GKE cluster compromise specifically — start here for the cloud-plane view, then use defending-kubernetes for the cluster-plane
  • Deciding whether an alert is even an incident — use triaging-security-alerts first

Establish Visibility Before You Conclude

GCP Cloud Audit Logs come in four streams, and their defaults are the whole game:

StreamDefaultCan disable?What it captures
Admin ActivityAlways onNo — written even if the Logging API is disabledConfig and metadata writes: IAM changes, resource create/delete
System EventAlways onNoGoogle-initiated actions on your resources
Data AccessOff (except BigQuery)YesReads and data-plane access — who read the bucket, the secret, the dataset
Policy DeniedOnNo (can exclude from storage)Access blocked by policy — VPC-SC, org policy

The consequence you must internalize: Data Access logging is off by default everywhere except BigQuery. So "did the attacker read the secret / download the bucket / exfiltrate the dataset?" is usually unanswerable from logs unless Data Access logging was enabled in advance. Do not report "no exfiltration occurred" — report "Data Access logging was not enabled, so read activity cannot be confirmed or ruled out." Those are different findings, and only one is honest.

Retention also differs by stream. Admin Activity and System Event land in the _Required bucket — 400 days, not configurable. Data Access and Policy Denied land in _Default30 days unless someone extended it or routed a sink to longer storage. Check the retention before assuming history exists.

bash
# What audit config is actually in effect at the org / project level
gcloud organizations get-iam-policy ORG_ID --format=json | jq '.auditConfigs'
gcloud projects get-iam-policy PROJECT_ID --format=json | jq '.auditConfigs'

Reconstructing Activity

bash
# Everything a principal did (Admin Activity is always present)
gcloud logging read \
  'protoPayload.authenticationInfo.principalEmail="attacker@example.com"' \
  --project=PROJECT_ID --freshness=30d --format=json

# IAM policy changes — the escalation signal
gcloud logging read \
  'protoPayload.methodName:"SetIamPolicy"' --freshness=30d --format=json

# Service-account key creation — persistence
gcloud logging read \
  'protoPayload.methodName="google.iam.admin.v1.CreateServiceAccountKey"' \
  --freshness=30d --format=json

Read the request fields, not just the method name. authenticationInfo carries the principal and, critically, whether a call was made as a service account via impersonation. requestMetadata.callerIp and callerSuppliedUserAgent locate the source; a gcloud user agent from an unexpected ASN on a service account is a strong signal.

Service-Account and OAuth Abuse

Service accounts are the center of most GCP incidents, because they hold durable credentials and are routinely over-privileged.

  • Impersonation chains. iam.serviceAccounts.getAccessToken and ...signJwt/...signBlob let one principal act as another. Trace the chain: who impersonated what, and does the terminal identity hold more than the origin? This is GCP's primary lateral-movement and escalation primitive.
  • Key creation is persistence — a user-managed key survives a password reset and an MFA change. Every CreateServiceAccountKey on a privileged SA during the incident window is a finding.
  • OAuth grants and the Workspace boundary. A domain-wide-delegation grant lets a GCP service account act across Google Workspace; that crosses into investigating-m365-entra-style identity territory and is easy to miss from a pure-GCP view.

Follow Security Command Center, Don't Restart From Zero

If Security Command Center is enabled, Event Threat Detection has likely already correlated some of this — anomalous IAM grants, service-account key abuse, and exfiltration patterns surface as findings. Start from SCC findings to seed the timeline, then corroborate each against the raw audit logs rather than trusting the finding alone. SCC in the Standard tier is far thinner than Premium/Enterprise; confirm which tier is licensed before assuming a detection would have fired.

Rationalizations to Reject

  • "The logs show no data access, so nothing was exfiltrated." Data Access logging is off by default. Absence of the log is absence of the logging, not absence of the access. Report the visibility gap.
  • "Admin Activity is empty for that window, so the account was idle." Check the principal spelling and the project — activity is logged in the project whose resource was touched, which may not be the one you are querying.
  • "It's only 30 days back." That is the _Default bucket. Admin Activity is 400 days; if the relevant action was an IAM or resource change, the history is longer than you think.
  • "The service account made the call, so it was legitimate automation." Service accounts are exactly what attackers impersonate. Check whether the call came via getAccessToken/impersonation and from what source.
  • "SCC didn't flag it, so it didn't happen." Standard-tier SCC detects a fraction of what Premium does, and Data Access-dependent detections need the logs enabled. A quiet SCC is not an all-clear.

Reading External Sources

Fetch public advisories, specifications, and vendor reports as Markdown:

bash
curl -sL "https://defuddle.md/<url>"      # scheme in the path is optional

This strips page boilerplate — roughly 78% fewer tokens on a prose page — and returns the full text rather than a summary, so you can grep it and trust a negative result.

Three things it is not for. Fetch JSON and API responses raw, because readability extraction mangles structured data. Fetch authenticated or JavaScript-rendered pages directly, because it retrieves them anonymously. And never route adversary infrastructure (phishing links, C2, malware hosting), client-owned hosts, or engagement URLs through it — the request leaves your machine to a third party, and for live adversary infrastructure it also tips off the operator.

Some sites block the extractor and return an error blob rather than the page — {"error":"Failed to fetch: 418 I'm a teapot"} from freedesktop.org, for instance. That is the fetch being refused, not the source saying the thing does not exist. Re-fetch the URL directly before drawing any conclusion from it.

References

  • investigating-aws-incidents, investigating-azure-incidents — the same discipline in the other two clouds
  • defending-kubernetes — the cluster-plane view when the incident is in GKE
  • responding-to-incidents — the overall IR framing this feeds
  • hardening-cloud-posture — enabling the Data Access logging whose absence this skill keeps running into

Frequently asked questions

What does the Investigating Gcp Incidents AI skill do?

Investigate a security incident in Google Cloud — establishing what audit logging exists before trusting a gap, reconstructing activity from Cloud Audit Logs, triaging service-account and OAuth abuse, following Security Command Center findings, and scoping IAM and resource changes. Use when responding to a suspected GCP compromise, investigating a leaked service-account key, working a Security Command Center or Event Threat Detection alert, or reconstructing what a principal did across a GCP organization.

Why use Investigating Gcp Incidents on TypingMind?

Because you install it once and use it with any model. Investigating Gcp Incidents is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Investigating Gcp Incidents in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/trilwu/secskills/tree/main/secskills-defense/skills/investigating-gcp-incidents. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Investigating Gcp Incidents?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Investigating Gcp Incidents?

As many as you like. As long as a model supports skills, you can use Investigating Gcp Incidents with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Investigating Gcp Incidents AI skill free?

Yes. It is published on GitHub by trilwu under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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