AxiSentinel extends continuous AI compliance evaluation to civil aviation and aerospace: flight-control assistance, air traffic management, predictive maintenance, airport security, and the drones and electric air taxis now entering certified service, evaluated against the rules of ICAO, EASA, the FAA, the UK CAA, CAAC and every other civil aviation authority whose airspace the aircraft actually flies through.
For a century, aviation safety has been proven through exhaustive, one-time certification: a design frozen, tested against a standard, and approved. That model assumes the system does not keep changing after approval. AI breaks that assumption, in the cockpit, in the tower, and now in aircraft that have no cockpit at all.
Every continuous AI-monitoring platform built so far was built for models that run in a data center or, at best, on a factory floor. None of them were built to evaluate an AI system certified to fly, to separate aircraft in controlled airspace, or to decide which bag gets flagged for a second look.
Aviation AI sits under both civil aviation safety law and general AI law, in every jurisdiction an aircraft or a drone operates in. AxiSentinel tracks both layers and builds RegDef packages the moment a standard is stable enough to evaluate against.
Working papers to the ICAO Assembly and the Global Air Navigation Plan's automation thread set safety and human-responsibility principles for AI across certification, operations and air navigation.
Europe's working framework for machine learning in certified aviation products, built around a three-level classification of how much a system does without a human in the loop.
Brings AI embedded as a safety component of a product regulated under the EASA Basic Regulation (EU) 2018/1139 into the Act's high-risk regime.
The EU's drone traffic-management framework: identification, geo-awareness and tactical-deconfliction services across designated U-space airspace.
Replaces the current individual-waiver system with a published rule for routine beyond-visual-line-of-sight drone operations.
A powered-lift certification basis applied individually to each eVTOL manufacturer's design, rather than a single blanket standard.
A pro-innovation, risk-based strategy built on existing aviation regulation rather than a new AI-specific regime.
The only civil aviation authority to have issued a complete type, production, airworthiness and operating certificate stack for a passenger-carrying eVTOL aircraft.
General-purpose AI risk-management frameworks that already apply to any AI component inside an aviation system, flight-critical or ground-based.
From a single predictive-maintenance model to an airline's full flight-operations stack, AxiSentinel evaluates aviation AI against whichever standard actually applies, anywhere the aircraft, the drone or the airspace sits.
Nothing about AxiSentinel's core architecture changes for aviation. What changes is which RegDef packages are switched on and what telemetry the agents capture, from a flight-support system to a baggage scanner.
Agents capture flight-support, ATC decision-support, UAS/eVTOL flight-computer and MRO telemetry on the cadence you configure, Always On or scheduled, never limited to a periodic audit cycle.
Every telemetry event evaluated against whichever aviation safety, airspace-management or AI-specific rule set actually applies, per event.
Every audit record is linked to the one before it in a signed, tamper-evident evidence chain, verifiable from the first event, built for the same scrutiny a safety investigator or a notified body would apply.
Tracks how much human judgment sits behind an AI-assisted flight, maintenance or air-traffic decision, the exact question EASA's Level 1 to Level 3 classification and the UK CAA's human-AI teaming principle both ask.
Ongoing evaluation of airport-security, biometric and ATC decision-support models for error-rate skew and decision transparency.
Engine-health and MRO decision models monitored against real fleet performance, not vendor benchmark data.
Built to the operational-authorization evidence a drone or eVTOL operator will need under FAA Part 108 and the EU's U-space framework.
A compliance-state change on one aircraft, drone fleet or ground system propagates network-wide as it happens.
AXI-Node agents deploy on avionics-adjacent networks, airline operations centers, airport systems and fully air-gapped government aviation facilities through the .axibatch format, zero network connectivity required.
Nothing becomes a compliance finding until a qualified auditor reviews and signs it, not for a cockpit AI assistant and not for a baggage-screening model.
Evidence generated in-region to satisfy the data-handling rules of whichever civil aviation authority is involved, EASA, the FAA, the UK CAA, CAAC and GCC regulators included.
The same architecture monitoring a robot fleet or a hospital's diagnostic AI now monitors a flight-control assistance system or an air-traffic decision-support tool. What changes is which RegDef packages are switched on.
Aviation AI is scaling, in the cockpit, in the tower and in aircraft with no cockpit at all, faster than the assurance industry built to evaluate it. AxiSentinel's architecture already does this for software AI; aviation is a new RegDef surface on the same platform, not a new product.
Whether it's a single predictive-maintenance model or an airline's full flight-operations stack, AxiSentinel evaluates it the same way it evaluates any AI system: on the cadence you configure, Always On or scheduled, with full evidence, and with a human signature before anything counts as a finding.
