Defense AI Model, Data & Policy Assurance
Commercial referenceDEF-020
When contacting BivectorAI, quote DEF-020 — Defense AI Model, Data & Policy Assurance.
Who this is for
AI governance leads and mission-software release managers responsible for keeping model versions, data lineage, and policy state matched to the mission scope a release was approved for.
The problem you already have
A model that passed testing at one site isn't automatically approved everywhere it gets deployed
Model versions, data lineage, policy changes, monitoring evidence and rollback state don't drift in sync. A model can pass every technical check at the site where it was tested while quietly no longer matching the sensors, environment, or data pattern at a different site it gets shipped to — and nobody notices until an incident forces the question.
The model can stay exactly the same while what feeds it quietly changes underneath it
An upstream sensor or data pipeline can change its output format — a unit, a field encoding, a schema version — without the AI model itself changing at all. The model keeps running, the version number stays the same, and nothing about "still running" tells you it's now misreading every input that depends on the changed field.
The review doesn't touch your model weights or training data
Never requested, and structurally rejected if submitted: model weights, training data, or production inference infrastructure access.
Real-World Operational Scenario: The Model That Was Approved for One Base, Not All Twelve
Scenario: A drone-detection model update is tested and cleared at Base Alpha.
Six weeks of live data, new sensor calibration, strong results — 94% detection accuracy, zero false positives in the test window. The team ships the same model build to all twelve bases running the same detection capability, since it's the identical artifact and it passed testing.
WITHOUT this check: Deployment tooling sees "same model version, tests passed" and pushes to all twelve bases uniformly. Base Delta has different terrain, different sensor placement, and a data pattern the Alpha test never saw. The 94% accuracy at Alpha doesn't transfer — but nothing in the rollout process catches that until Delta's detection rate has quietly degraded for weeks.
WITH this check: The evidence certificate binds the tested authority to Base Alpha's exact sensor, environment, and data-domain generation. Base Delta is reported as FULL_REVALIDATION_REQUIRED — not because the model is bad, but because it was never actually tested against Delta's conditions. The other eleven bases stay on the prior generation until each is individually evaluated or explicitly included in the tested scope.
What you provide
Redacted model-version, data-lineage, and policy-change metadata already logged by your MLOps and governance pipeline. No model weights or training data are required.
What BivectorAI Provides
An Authority State, Not a Pass/Fail Flag
Every generation is classified as one of several named states — AUTHORITY_INHERITABLE, AUTHORITY_ATTENUATED, SHADOW_ONLY, LIMITED_DEPLOYMENT, HUMAN_REVIEW_REQUIRED, FULL_REVALIDATION_REQUIRED, or BLOCKED — reflecting exactly what's actually been tested, not a single pass/fail signed off once and assumed to hold everywhere.
Drift That Restricts Only What It Actually Affects
A model performing poorly in one narrow operating condition doesn't have to take the whole capability offline — only the affected function is attenuated, while everything still validated stays in service.
Rollback Checked Against Today's Environment, Not Yesterday's
Restoring a prior model version doesn't automatically restore its old authority if the schema, platform, or dependencies have changed since — compatibility is re-verified before old authority is reinstated.
Hash-Sealed Certificates for Post-Event Review
Every deployment, update, and rollback decision traces back to the exact data, model, policy, and evaluation generations that justified it at the time.
What we will never ask you for
Never requested, and structurally rejected if submitted: model weights, training data, or production inference infrastructure access, or your system's core design data. Every submission is checked against this rejection list before any evidence logic runs — a malformed or over-broad submission is blocked and reported back to you, not silently processed.
How it actually runs
Engagement path & current acquisition terms
DEF-020 · Tier B — Mission Software
Secures a defined diligence and exclusive-negotiation period. Any option credit is agreed in writing as part of the signed commercial terms.
Starting integration engineering, customer-safe adaptation, local/private deployment work, validation and acceptance evidence for the agreed environment.
Scope-dependent consideration for negotiated exclusivity in a defined field, program, platform class, territory and rights scope.
Engineering support, update access, security maintenance, evidence infrastructure and agreed sustainment services.
Applied to attributable net program revenue under the definitive agreement, subject to negotiated exclusions and accounting rules.
Commercial boundary. These are indicative starting terms, not a binding offer. Final price depends on exclusivity scope, field of use, territory, sublicensing, technical-data rights, integration obligations, export-control review, support requirements and definitive agreement.
IP boundary. Exclusive FOU does not automatically transfer ownership of BivectorAI Background IP. Worldwide/all-defense rights, sublicensing, source-level technical-data rights or assignment require separate negotiated consideration.
Scope the DEF-020 diligence and integration boundary
Request DEF-020 Acquisition Discussion →Email fallback: contact@bivectorai.com
Defense technology is integrated through qualified local/private engagement. No public Defense API and no automated Defense checkout are provided.