Autonomous Defense Transformers: Security-Native Reasoning for Digital Infrastructure

Modern digital infrastructure is defended by systems that are fundamentally reactive. Telemetry is

collected after actions occur, detections trigger after damage begins, and response is gated by

human triage operating under time pressure. This architecture fails against AI-speed adversaries

whose attack loops operate orders of magnitude faster than human decision cycles. We introduce

Autonomous Defense Transformers (ADT), a security-native model class designed to reason

continuously over live infrastructure state, interpret threats under uncertainty, validate defensive

actions against explicit constraints, and generate audit-grade evidence as a first-class output. ADT

is defined by five core design principles: defense-first pretraining, continuous model-level

reasoning, integrated actuation under constraints, zero-trust alignment, and guardrailed learning.

We present a complete system architecture separating context ingestion, threat interpretation,

action validation, actuation, and audit trail generation. We provide a technical comparison with

SIEM, SOAR, rule engines, and LLM-wrapper approaches, and define an evaluation framework

focused on containment correctness, evidence completeness, and cost-weighted false positives.

Deployment results from the PulseADT production system demonstrate 359x faster detection (0.8

min MTTD vs. 287 min industry average), 200x faster response (2.1 min MTTR vs. 420 min

industry average), and 95% false positive reduction (1.2% vs. 23.5% industry average) across

680,000 protected assets. We conclude by discussing implications for enterprise resilience,

regulatory enforcement, and national infrastructure security, with particular attention to African

computing contexts.