Trust & Verification
How MLNavigator approaches offline review workflows, deployment records, energy reporting, and data handling in high-assurance environments.
Note: This page stays at a public-summary level. Detailed implementation materials are reserved for private review.
Threat Model
What We Protect Against
- ✓Unauthorized data exfiltration during inference
- ✓Unexpected changes in approved software or model packages
- ✓Configuration drift without a review trail
- ✓Unclear runtime behavior during review or incident response
- ✓Log deletion or modification
What We Do Not Claim
- ✕AI outputs are “true” or “correct”
- ✕Protection against hardware-level attacks
- ✕Protection against malicious operators with root access
- ✕Model safety or alignment guarantees
- ✕Perfect numerical reproducibility across architectures
Field notes from customer and operator interviews:
- "People want the tech, but they do not trust it yet." — Product stakeholder, regulated enterprise
- "IT is spearheading AI governance, and teams follow once controls are published." — Technical director, enterprise integrator
- "Every employee signs our AI directive." — Senior vice president, component manufacturer
Operational Records
The draft spec outlines retained records that support later review of how a run was performed.
The public site keeps this description at a high level: records can preserve deployment context, operational history, and output provenance for review workflows.
Record Scope
Public descriptions focus on categories rather than schemas: deployment context, approved configuration, operational history, and output provenance.
Operating Consistency
Declared operating rules
Public materials focus on the fact that operating rules are documented and reviewable.
Recorded change context
Teams need enough context to interpret changes over time without reconstructing them from scratch.
Review boundaries
Public claims stay at the boundary level: what the records support and where human judgment still applies.
Note: Public descriptions stay at the governance level and avoid implementation-specific thresholds or controls.
Energy Measurement Method
We discuss energy reporting publicly at a high level so teams can compare local deployment approaches without relying on vendor marketing.
Public methodology summary
The public methodology summary stays qualitative: establish a stable baseline, run a representative workload, and report conditions clearly enough for internal comparison.
Required Reporting
- • Deployment environment
- • Test window
- • Operating conditions
- • Reporting assumptions
Repeatability Rules
- • Comparable environments
- • Consistent reporting window
- • Representative workload selection
- • Consistent reporting conditions
No Third-Party Collection
Explicit Statement: This website does not include third-party tracking scripts, analytics pixels, or telemetry that reports to external services. adapterOS is designed without them.
We do not use Google Analytics, Facebook Pixel, Mixpanel, Segment, or similar services.
Allowed Minimal Logs
Minimal server access logs for security and debugging. IP addresses, timestamps, request paths. Typically retained for 30 days. Not shared with third parties.
Opt-In Only
Contact form submissions are stored to respond to inquiries. Newsletter signup is explicit opt-in only. No pre-checked boxes.