Fionn Labs
Institutional Context

About Us

Applied AI assurance for real-world governance constraints

Fionn Labs was established to address a recurring gap in AI deployment programs: policy requirements are often clear in principle but weak in execution. Teams frequently know what they must satisfy, yet lack methods that translate those expectations into reliable technical and operational behavior.

Our focus is the design of those methods. We work where assurance quality directly affects mission outcomes, regulatory acceptance, and public trust. This includes defense and federal programs, aerospace and space systems, and large enterprises operating under complex compliance obligations.

The lab model is intentionally practical. Research questions are selected for their operational consequence. Outputs are built to support decisions made by engineering leaders, policy owners, legal teams, and oversight stakeholders under real program pressure.

Institutional Profile

Institutional Profile

How we define the work

Institution

Fionn Labs is an applied research lab focused on AI assurance, policy translation, and governance implementation for high-consequence systems.

Mission

We help technical and policy teams move from ambiguous requirements to concrete methods, operational procedures, and defensible evidence.

Orientation

Our approach favors scientific depth, explicit assumptions, and implementation quality over broad trend language.

Mission Orientation

Mission Orientation

How this focus translates into real program method design

First-time readers should expect domain specificity. Methods are shaped by mission conditions, command structures, and review obligations rather than generic model optimization goals.

Contested Edge Autonomy

Define mission-phase decision semantics, implement runtime uncertainty thresholds, and bind override and escalation logic to explicit command authority paths.

Typical Artifacts

  • - Decision-event and uncertainty trace schema
  • - Escalation and intervention authority matrix
  • - Scenario-based degradation test dossier

BVLOS Flight Governance

Implement policy-to-control translation for flight decision boundaries, tie release gates to assurance criteria, and maintain continuity across versioned autonomy behaviors.

Typical Artifacts

  • - Autonomy release gate criteria package
  • - Control-to-procedure crosswalk for mission phases
  • - Flight readiness assurance case summary

Proliferated LEO Decision Networks

Engineer distributed control checkpoints, lineage capture across node boundaries, and rapid escalation playbooks for cross-platform anomaly response.

Typical Artifacts

  • - Cross-node lineage and provenance ledger design
  • - Distributed checkpoint and anomaly playbook
  • - Mission-level governance telemetry dashboard spec

Human-Machine Teaming

Codify role-transition logic, intervention triggers, and after-action evidence capture so mission tempo can increase without governance ambiguity.

Typical Artifacts

  • - Role-transition protocol and decision-rights map
  • - Human override trigger and response specification
  • - Post-mission assurance review template
Research Leadership

Research Leadership

Execution credibility for funded program delivery

Fionn Labs is structured to connect technical assurance design, policy interpretation, and implementation accountability in one delivery model.

Founding Research Lead

  • - Digital engineering practitioner in aerospace production environments
  • - Doctoral research focus in AI/ML systems and decision-traceability architecture
  • - Program experience bridging technical delivery and regulatory-oriented governance

Leads method architecture, research program design, and technical assurance model development.

Policy and Legal Integration Lead

  • - Senior legal expertise in technology and regulatory interpretation
  • - Contract and policy translation support for high-consequence operating contexts
  • - Cross-functional coordination across legal, risk, and engineering stakeholders

Leads policy-to-controls interpretation quality, governance accountability language, and review-readiness communication design.

Engineering and Governance Delivery Model

  • - Research-to-implementation workflow design
  • - Evidence system engineering and review forum support
  • - Operational procedure integration in constrained mission environments

Maintains delivery discipline from research outputs to deployable governance workflows and measurable program outcomes.

Operating Principles

Operating Principles

What anchors quality in delivery

Research rigor is paired with implementation accountability.
Policy and engineering are treated as one integrated system.
Assurance evidence is designed into delivery, not retrofitted at review time.
Governance decisions require explicit ownership and measurable controls.
Program Architecture

Program Architecture

Framework-informed, implementation-led

Architecture Principle: One control model, many operating contexts

Program architecture should not fragment by domain. A resilient model starts with shared control primitives, then layers mission-specific constraints for defense autonomy, edge sensing, aerospace safety, and enterprise governance. This prevents policy drift and reduces rework when systems move across environments.

Architecture Principle: Evidence is a system, not a document

Assurance evidence should be generated by design through logging structures, control checkpoints, and review workflows. In complex systems, late-stage evidence assembly is fragile and expensive. An evidence system approach improves decision quality and review velocity simultaneously.

Architecture Principle: Policy tempo and technology tempo must coexist

Competitive programs need rapid iteration, while governance needs stable accountability. Architecture should support controlled experimentation: bounded risk envelopes, explicit decision gates, and progressive assurance thresholds that allow growth without abandoning security or compliance posture.