AI‑Powered Kill Chain 2030: A Reference Architecture for Sensor‑to‑Shooter
Designing a sub‑second, auditable, and sovereign pipeline from detection to effects across contested, coalition environments.
Executive Summary
This case study proposes a modular, sovereign AI kill‑chain architecture that compresses the detect‑decide‑deliver loop to sub‑second latencies while maintaining legal/policy audibility.
Operational Context
Mission Threads: Counter‑UAS, SEAD, maritime interdiction.
Latency Budget: 50–200 ms inference at edge; <1 s end‑to‑end for time‑sensitive targets.
Assurance: Human‑on‑the‑loop with policy guardrails and dynamic ROE.
Architecture
Edge Sensing & Pre‑Filter: EO/IR/RF sensors with on‑device model pruning and INT8 quantisation (see MLPerf Inference: Edge).
Data Fabric: Publish/subscribe with deterministic QoS and ABAC labels; align to NATO STANAGs incl. 4609 (FMV) and 4559 (ISR Library).
Decision Layer: Multi‑model fusion, Bayesian trackers, and rule‑checked action proposals with NIST AI RMF risk controls.
Effects & BDA: Kinetic/non‑kinetic actuators; automatic battle damage assessment pipelines.
Governance & Safety
Embed UK Defence AI Strategy principles and Responsible AI Senior Officers’ Report patterns; audit via tamper‑evident logs.
Security & Crypto
Crypto‑agility with NIST PQC, zero‑trust per NCSC Zero Trust.
Interoperability
Coalition readiness via NATO FMN Spiral profiles and CI/CD conformance testing.
Outcomes
Sub‑second targeting proposals with confidence bands.
Traceable decision trails for after‑action and legal review.
Resilience to EW/jamming via edge autonomy and degraded‑mode ops.
