GLEMAD PUBLISHING
Technical papers, model reports, threat intelligence, security guidance, and institutional findings made available for examination.
REPORTS AND DOWNLOADS
5 PUBLICATIONSWhat the Central Bank of Nigeria’s data residency, continuous monitoring, and compliance requirements mean for security infrastructure, including where common vendor stacks can create exposure.
The technical architecture, threat model, action taxonomy, deployment patterns, and evaluation framework for Ollandi 5, Glemad’s current security reasoning model.
Modern digital infrastructure spans heterogeneous domains, cloud, identity, network, endpoints, and applications, each generating signals that must be interpreted in context. Single-model defensive systems, while effective within bounded scopes, face fundamental limitations when threats traverse domain boundaries or when coordinated response requires simultaneous action across distributed environments. We introduce Coordinated Defense Agents (CDA), a multi-agent architecture that extends Autonomous Defense Transformers (ADT) to operate as distributed, collaborative defensive systems. CDA defines formal protocols for inter-agent communication, shared state construction, collective hypothesis formation, consensus-based action coordination, and conflict resolution under uncertainty. Each agent maintains domain-specific expertise while contributing to a unified threat model through structured knowledge exchange.
Independent threat intelligence covering financial infrastructure, telecom networks, government systems, and critical services across Africa, with indicators, detection rules, and threat forecasts.
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.