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Cloud Forensics Utils

20260916 open-source

Cloud Forensics Utils provides tools for forensics teams to collect evidence from Google Cloud Platform, Microsoft Azure, and Amazon Web Services.

Summary

Cloud Forensics Utils is an open-source library and CLI tool that collects evidence from cloud platforms for incident response, currently supporting Google Cloud Platform, Amazon Web Services, and Microsoft Azure. Its core is the libcloudforensics Python module, which implements the collection functions directly, while the CLI wraps those same functions for interactive use, so teams can either script against it or run it by hand during an investigation. It is built for forensics and incident response practitioners working across multi-cloud environments, rather than for general security operations or compliance reporting. It sits in the DFIRDFIRDigital forensics and incident response, investigating what happened during a security incident and containing it. The work includes preserving evidence, reconstructing activity and restoring systems. tooling space alongside other cloud evidence-acquisition utilities, filling a niche most general-purpose forensics suites do not cover natively. First committed in 2020 and maintained by 21 contributors, it has seen 15 commits in the past year, putting it in slow but active maintenance rather than dormant.

Cloud Forensics Utils provides tools for forensics teams to collect evidence from Google Cloud Platform, Microsoft Azure, and Amazon Web Services.

How Cloud Forensics Utils helps

Which disk should I pull as evidence without shutting down the running instance?

copies the target disk in the cloud platform and attaches it to an analysis instance, so the original resource stays live while the copy is examined

What did this cloud instance actually look like at the time of the incident?

exports a disk image that can be pulled down and analyzed offline, preserving the state without relying on the live host to stay unchanged

Can I run the same evidence-collection step by hand or from a script during an incident?

exposes the same collection functions both as CLI commands for interactive use and as library calls, so the identical logic can be scripted into an automated response

Which GCP project's resources am I actually collecting from when I have several configured?

takes an explicit project ID (or a comma-separated list for log operations), falling back to the project configured in the local SDK, and errors out rather than silently picking one

Where do the build resources for imaging an instance need to live relative to my storage bucket?

lets the zone used for the imaging instance be set explicitly, so evidence collection can be pointed at the same region as the destination bucket instead of a fixed default

What filesystem and container formats can the analysis environment actually inspect once evidence is attached?

provisions the imaging instance with added packages for filesystem and container introspection, extending what the analysis host can read on the acquired disk

all 5 features, with the evidence for each →

Languages

Not documented

Features

5 capabilities · 3 backed by code, an API document or a real run

Built from Cloud Forensics Utils's releases, docs, README and our own runs. “Seen” marks the month we first spotted a release the vendor didn't date. Group names and tags are our labels.

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What Cloud Forensics Utils offers you

  • Command-line interface
All capabilities 5 capabilities
GCP support verified Provides tooling for Google Cloud Platform, including project ID configuration and support for multi-project log operations. 7 releases · 7 other sources · first seen Aug 2020

release 7 total

  • New ListSnapshots method added to the GCP Compute module. 20260415 · Apr 2026 · source · release history
  • CreateDiskFromSnapshot now accepts explicit project and zone arguments. 20260415 · Apr 2026 · source · release history
  • Enables exporting disk images in supported formats when transferring to Google Cloud Storage 20250721 · Jul 2025 · source · release history
  • Adds CloudResourceManager OrgPolicy support 20241207 · Dec 2024 · source · release history
  • Adds get_project_metadata function for retrieving GCP project metadata. 20241205 · Dec 2024 · source · release history
  • BigQuery.Jobs module added to manage BigQuery jobs 20220114 · Jan 2022 · source · release history

docs

  • Library and CLI tool for collecting forensic evidence from GCP, Azure, and AWS cloud environments during incident response.

command line

  • --project — GCP project ID. If not provided, the library will look for a project ID configured with your gcloud SDK. If none found, errors. For GCP logs operations, a list of project IDs can be passed, as a comma-separated string: project_id1,project_id2,... 20210720 · Jul 2021 · command-line history
  • gcp — Tools for GCP 20200818 · Aug 2020 · command-line history
  • project — GCP project ID. 20200818 · Aug 2020 · command-line history

example

  • List all snapshots in a specific GCP project. cfu.gcp.compute.ListSnapshots(project='forensics-prod') 20260415 · Apr 2026 · source
  • Create a disk in a specific zone and project from an existing snapshot. cfu.gcp.compute.CreateDiskFromSnapshot(snapshot_name='my-snap', project='forensics-prod', zone='us-central1-a') 20260415 · Apr 2026 · source
  • Retrieve details like creation time and labels for a specific Google Cloud project. from cloud_forensics.utils import get_project_metadata metadata = get_project_metadata('my-gcp-project-id') 20241205 · Dec 2024 · source
Custom VM instance sizing shipped Allows users to specify the machine type or hardware configuration for forensics analysis virtual machines. 1 release · 2 other sources · first seen May 2026

release

  • StartAnalysisVm gains a new machine_type parameter to control the VM instance size. 20260505 · May 2026 · source · release history

example

  • Invoke the API directly with the new machine_type field. import cloud_forensics_utils as cfu cfu.StartAnalysisVm(disk_id='disk-123', machine_type='n1-standard-4') 20260505 · May 2026 · source
  • Start a forensics analysis VM with a specific machine configuration (e.g., high-memory instance). cfu start_analysis_vm --machine_type 'n1-highmem-4' <disk_id> 20260505 · May 2026 · source
AWS resource targeting verified Allows users to specify the AWS availability zone in which resources are located. 2 releases · 1 other source · first seen Aug 2020

release

  • Disks and instances can now be created in explicitly provided zones instead of only defaults. 20260415 · Apr 2026 · source · release history
  • Implements Suspend() command for cloud instances 20250116 · Jan 2025 · source · release history

command line

  • zone — The AWS zone in which resources are located, e.g. us-east-2b 20200818 · Aug 2020 · command-line history
Zone-aware image export shipped Allows users to specify the zone for Cloud Build imaging instances to optimize resource locality. 1 release · 1 other source · first seen Sep 2026

release

  • Adds zone parameter (default 'us-central1-a') to the ExportImage method, allowing callers to specify the zone for Cloud Build imaging instances.⁠︀︄︇︀︅︉︀︅︀︀︃︀︀︅︅︀︅︊︀︄︆︀︃︄⁠ 20260916 · Sep 2026 · source · release history

example

  • Export a GCP compute image to a specific zone rather than accepting the default us-central1-a — useful when your GCS bucket and build resources are in a different region. image.ExportImage( gcs_output_folder='gs://my-forensics-bucket/exports/', image_format='qcow2', output_name='disk-export', zone='europe-west1-b' ) 20260916 · Sep 2026 · source
Azure resource targeting verified suggested Allows users to specify a default Azure resource group name in which resources are created. 2 other sources · first seen Aug 2020

Suggested capability: Remote forensic collection. This is our guess, not confirmed.

docs

  • Library and CLI tool for collecting forensic evidence from GCP, Azure, and AWS cloud environments during incident response.

command line

  • default_resource_group_name — The default resource group name in which to create resources 20200818 · Aug 2020 · command-line history

Lines in monospace are the tool's own words: help text parsed from its source, or an endpoint from its API document. Under release, a line is our summary of a dated release. Under docs, it is quoted from the tool's own documentation or website. Each links back to the source it came from.

Score card

  1. Shipping velocity releases a month, last 12 months
  2. Documentation quality release-notes rubric, 0–100
  3. Breaking-change discipline how rarely a release breaks users, and notice given
  4. Maintenance risk quiet time against its own cadence, maintainers
  5. Innovation & surface new capabilities, API and MCP surface
  6. Community health
  7. Contributor trend distinct committers, this year against last
  8. Maintainer concentration bus factor now against a year ago
  9. Issue backlog trend issues closed per issue opened, 12 months
  10. Responsiveness median time to a maintainer’s first reply

Nine scores for Cloud Forensics Utils, each built from the releases, notes and API history this site tracks — and, on Researcher and Business, where it stands among every measured tool in its category and how that has moved.

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Each score is 0–100 on a published rubric, derived from data this site already shows. A dimension we cannot measure says so — it is never scored 0 and never enters a rank.

How this is scored
  • Shipping velocity — releases a month over the last 12 months, on a log scale where 16 a month scores 100. Needs 3 releases in the window.
  • Documentation quality — the release-notes rubric published on the analytics page, unchanged.
  • Breaking-change discipline — the share of releases carrying a breaking change (none scores 100, half or more scores 0). Only projects that label breaking changes are measured: a clean record cannot be told from an unlabelled one.
  • Maintenance risk — quiet time measured against the tool's own release cadence (quiet longer than 90 days and three typical gaps scores 0), plus the number of active maintainers and whether contributors are growing, where known. Higher is lower risk.
  • Innovation & surface — new capabilities in the feature ledger this year, API and MCP operations added, and the share of commits that are features: the mean of whichever we hold.
  • Community health (open-source tools only) — each is a direction, not a size: contributors this year against last, the bus factor now against a year ago, and issues closed per issue opened, where steady scores 50, doubled 100 and halved 0; plus the median time to a maintainer's first reply (an hour scores about 100, a month or more 0). A commercial card has no repository and carries none of the four.
  • Documented delivery (commercial tools) — when a vendor's documented new capabilities have slowed against its own baseline, that counts against the overall standing as heavily as two low dimensions. Steady delivery, or too little history to judge, leaves the standing alone; it is never a sixth score.
  • Open-source tools are scored on repository signals; commercial tools on their published changelog. Ranks compare tools within one category and one profile and need at least five measured peers; the overall standing needs three ranked dimensions. Best-in-breed or consolidator is a shape, not a quality, and is never scored.

Release-note detail over time

See how much detail Cloud Forensics Utils puts in its release notes, quarter by quarter, and compare it with tracked tools in the same category. This history uses up to ten retained, reviewed notes per tool; sparse or blank older quarters may reflect that limit.

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Website history

adk.dev, every change

Google's 40 pages as they looked each month from August 2026 to October 2026: 79 renders, 1 change of tagline or palette. Step through them, play them, or put any two months side by side.

Website history is included with Researcher and Business.

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Releases

8 entries

  1. September 20261
  2. May 20261
  3. April 20261
  4. July 20251
  5. January 20251
  6. December 20242
  7. January 20221

    8 entries

    Project health

    Issue, contributor and maintainer activity in google/cloud-forensics-utils, 2023-10 – 2026-09, as of 2026-09-25. Twelve-month figures first; the trend lines run the whole window.

    1. Contributors 5 +2 vs prior 12 months
    2. Maintainers 1 +1 joined · −1 left, last 12 months
    3. Bus factor 1 committers behind half the commits
    4. Community-filed 100% of issues opened, last 12 months
    5. Median time to close 5.2 days first maintainer reply 5.2 days
    6. Open backlog 24 of 179 issues ever filed
    1. Issues opened / month 0 · 0 communitymaintainers
    2. Open backlog 24
    3. Contributors / month 2
    4. Commits / month 3
    5. Stars 508
    Issue themes, last 12 months
    • other 100%

    Counts from the project's public repository and git history; no individuals are identified. Maintainers are the committers covering 80% of the last year's commits. how we measure this

    Libraries its release ships

    What its latest release locks, checked against known advisories.

    None of its release's images build from a lockfile we can read.

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