Evidence & validation

Evidence you can inspect.

Two different things live on this page: the validation record for the platform itself, and the measured figures behind the claims made across this site.

Internal AI Security Validation — 2026

Self-issued engineering validation attestation.

This is an internal validation attestation issued by the OctopuShield engineering team. It is not an accredited third-party certification.

This record attests that the OctopuShield brand impersonation intelligence platform underwent an internal evaluation of its security, reliability and data-handling behaviour, carried out by its own engineering team against its documented architecture decisions.

Reference: OS-AIV-2026-0001
Year: 2026
Type: Internal validation attestation
Accreditation: Not accredited
Issuer: OctopuShield Engineering

All five presentation variants

These are five visual variants of one record, not five separate certifications.

These documents were reissued on 19 August 2026 to remove a legal-entity name that is no longer used. The content of the attestation — its scope, its date and its limits — is unchanged.

What you receive when we find one.

This is a complete finding report, filled with synthetic data. Nothing here is a real customer or a real detection — it shows the shape and the depth of what lands in your inbox.

Sample report →

Measured figures behind the claims.

Each figure below comes from a run that was recorded, with its date. Nothing here is extrapolated into a rate or a guarantee.

Figure	Value	Measured	What it does not mean
Active monitoring sources	9	20 Aug 2026	Not a claim of complete log coverage
Automated checks passed	383 / 383	20 Aug 2026	Covers the codebase's own test suite, not production behaviour under load
Runtime application dependencies	0	20 Aug 2026	Applies to the application runtime, not to the hosting platform
Records read in a single 90-second run	7,059	20 Aug 2026	A single measured run, not a sustained throughput rate
Independent monitoring sources	9	Sep 3, 2026	We do not depend on a single source; if one fails, collection does not stop quietly

What we have not measured.

These are published as unknown rather than estimated.

Detection coverage as a percentage of all impersonating domains.
Production uptime.
False-positive rate across a labelled dataset.