Research — AI Governance & Assurance

Requirements you can test.

Technical AI governance turns a requirement for an AI system into a control that can be tested, challenged, falsified and audited. AI assurance is the reproducible evidence that those controls actually hold under defined conditions.

The assurance cycle

From requirement to evidence.

INSPEKTORIUM does not ask whether a system is broadly “safe.” It investigates specific properties: a requirement becomes an invariant, the invariant becomes a control, and the control is only trusted once a test produces evidence under a defined fault.

01

Requirement

A property the system must hold.

02

Control

The mechanism that enforces it.

03

Fault

A deliberate way to break it.

04

Test

Run the fault against the control.

05

Evidence

What actually held, and where.

The full chain

Requirement → Technical invariant → Control → Failure condition → Test → Measurement → Pass / fail → Known limitation

Controls, concretely

Five requirements, five controls.

Each control is a specific, testable mechanism — not a policy statement.

Requirement

Each answer must trace to a retrievable source.

→ Control

Source binding

Claims are bound to source spans; unbound claims are rejected.

Requirement

Only authorized steps may run.

→ Control

Authorization gate

Every step passes an explicit authorization gate before it executes.

Requirement

An invalid state must not produce output.

→ Control

Fail-closed block

The workflow fails closed and blocks rather than emitting a best guess.

Requirement

Results must reflect the current inputs.

→ Control

Checkpoint rejection

Authenticated checkpoints reject stale or replayed results.

Requirement

Confidence must be well-formed to be usable.

→ Control

Calibration rejection

Malformed confidence is flagged as an invalid calibration state.

What AI assurance means here

It means

Collecting reproducible evidence about whether specified controls or properties of an AI workflow actually hold under defined conditions. Negative results and known failures are part of the research, not something to hide.

It does not mean

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Certification

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General AI safety

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Regulatory approval

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Guaranteed compliance

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Guaranteed truth

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Guaranteed robustness