Deterministic evaluation
Inputs are canonicalized and hashed. Policies are pinned by hash. The evaluator does no I/O, reads no clock and has no hidden state.
GI/PPI evaluates proposals against fixed, versioned policies and returns allow or deny with explicit reason codes. Every decision is sealed into a hash-chained ledger you can replay later, so drift is visible instead of silent.
Replaying a decision recomputes it from the stored proposal and the pinned policy. A different result means tampering or unintended change. Values shown are illustrative.
Inputs are canonicalized and hashed. Policies are pinned by hash. The evaluator does no I/O, reads no clock and has no hidden state.
Each decision is chained to the one before it. Editing any entry breaks the chain at that point, and signed anchors catch truncation or a rewritten history.
Any decision can be re-run against the original proposal and policy version. If the answer changes, you find out immediately.
The same engine sits in front of the places changes are made, and it denies by default when anything is missing or unclear.
GI/PPI is not an AI, not a recommendation system and not a probabilistic engine. Like a measurement device, it must return the same value when the same measurement is taken twice. If it cannot, it is considered broken.
Organizations add overlays to narrow what the base policy allows. Any overlay that would widen authorization fails closed when the policy loads.
Licensed entitlements meter evaluations through credits recorded with the same append-only, auditable principles as the decisions themselves.
Missing config, a policy that no longer matches its pin, or a decision that cannot be recorded: every one of them is a deny.
Read the research behind GI/PPI, or ask for access to the engine and its evidence tooling.
Read the paper