Video summary

EU Consigo descobrir tudo sobre você na internet. mostrando na prática que não existe privacidade...

Main summary

Key takeaways

Technology

Technological concepts / what the tools do

The video demonstrates—using “educational” examples—how three OSINT-style tools can expose personal data that users believe is private. The underlying claim is that, when used correctly, publicly available traces can be correlated across the web to reconstruct identity and activity.

1) PIN Ice (reverse face recognition)

Purpose: Upload a photo of a person and find where that face appears online.

Core concept: Reverse facial recognition—the service scans the internet for matching or associated public appearances of the face.

Workflow shown:

  • Upload/select a photo.
  • Start a search.
  • View results with approximate location/details, with deeper results gated behind paid access or login.

Type of outputs shown:

  • Lists of websites where the person’s face was found (e.g., news sites, article pages).
  • Ability to click through to identify which photo was matched.

Access control:

  • More detailed results require payment and/or logging in.

2) Gerhunt (email-to-profile/service discovery)

Purpose: Enter a person’s email address to discover where the email is registered and which services are active—described as “public and legal manner.”

Workflow shown:

  • Enter an email and submit.
  • The tool prompts for login.

Type of outputs shown:

  • For a Google-related example, it surfaces active Google services.
  • Mentions retrieval of identifiers such as:
    • account type
    • account ID
    • workspace ID
    • additional account-related data

Core concept: Linking an email identity to registered services and account metadata that can be cross-referenced.

3) SpiderFoot (large-scale OSINT correlation / scanning)

Purpose: Run broad scans using 200+ modules to uncover hidden public information across the web.

Core concept: Automated OSINT gathering and data correlation—producing interconnected results (graphs/charts) from many sources.

Workflow shown:

  • Create a new scan with a target (examples include full name).
  • Select target types such as: domain (IPv4/IPv6), hostname, subnet, Bitcoin address, email, phone, name, etc. (with some required formatting like quotes).
  • Tag relevant items and run modules.

Type of outputs shown:

  • Summary of what was found (e.g., “four items” found).
  • Correlation view (in the example, correlation was “none”).
  • Graphs/charts to navigate relationships between entities.
  • Navigation to open results by entity (e.g., usernames) and see where they appear.
  • Log detailing the scan activity:
    • what was searched
    • how many modules were used
    • how long it took
  • Direct linking to source documents:
    • an identified account within a document (example referenced “Scribe” in the transcript), with clickable links to where the information came from.
  • Search within documents to find references.

Overall message / caution

The video argues there is no real privacy if people leave data trails online.

  • It warns that Brazilian data leaks (including “Telegram dashboards”) are common.
  • It states that the tools can expose identity through public data aggregation and correlation.

Main speakers / sources

  • Speaker: The video’s narrator/host (no specific name provided in the subtitles).
  • Demonstrated tools (sources referenced by name): PIN Ice, Gerhunt, SpiderFoot.

Original video