Why Current Governance Is Insufficient

Most MENA states address CBRN threats through vertically siloed arrangements in which health, defense, interior, and civil defense ministries hold overlapping mandates without a statutory coordinating authority, producing duplicate stockpiles, incompatible data, and unclear accountability during fast-moving events.Reference Farhat, Alinier and Bajow ^{2 -}Reference Mani, Plummer and Kuhn ⁴ International references already define obligations and core capacities under the International Health Regulations (2005), the World Health Organization (WHO) Health Emergency and Disaster Risk Management (Health EDRM), and chemical-incident guidance under the Organization for the Prohibition of Chemical Weapons (OPCW), yet implementation across the region remains imbalanced and reactive.Reference Mani, Plummer and Kuhn ^{4 ,} Reference Qzih and Ahmad ⁵ The policy question is therefore not whether coordination is desirable, but which model can deliver it given regional heterogeneity, contested sovereignty, and constrained budgets.

Choosing a Coordination Model

Three broad models exist: (1) a standing “supranational” body, exemplified by the EU’s Emergency Response Coordination Centre and Health Emergency Preparedness and Response Authority (HERA), which enable rapid cross-border assistance but depend on political and fiscal integration that MENA currently lacksReference Anderson, Forman and Mossialos ⁶; (2) ad hoc, event-triggered coalitions, which are politically low-cost but reproduce the fragmentation observed during large-scale emergenciesReference Briffa ^{7 ,} Reference Plümper and Neumayer ⁸; and (3) a nationally anchored, empowered council that coordinates internally and federates regionally, which we propose in this document. The latter model respects state sovereignty and is achievable without a regional agreement, and can interoperate with the WHO, the OPCW, and the International Atomic Energy Agency (IAEA) for training, knowledge sharing, and mutual aid. Centralization carries risks of single points of failure, politicization, and information bottlenecks, which we mitigate through subordinateness (decisions devolved to the lowest capable level), analog redundancy, and independent oversight (Table 1).

Table 1.

Implementation roadmap for an AI-enabled national CBRN preparedness system in MENA: governance, legal authority, financing, data governance, low-resource options, and risks/mitigations

Table 1. Long description

The table is organized into six columns: Dimension, Phase 1: Foundation (0 to 12 months), Phase 2: Build (1 to 3 years), Phase 3: Mature (3 to 5 years), Low-resource/conflict option, and Key risks and mitigations.

* Governance and legal authority: Starts with enacting decrees and establishing a Council. Progresses to codifying cross-border protocols and M o U s. Matures into an independent oversight body and regional federation. Low-resource option: Embed Council in existing disaster authorities. Risks: Centralization and politicization.

* Financing: Starts with ring-fenced budgets. Progresses to blended financing with W H O and World Bank. Matures into sustainability via insurance and levies. Low-resource option: Pooled regional procurement. Risks: Funding inequity.

* Data governance: Starts with common data schemas and stewards. Progresses to privacy-preserving analytics and cybersecurity. Matures into interoperability certification and K P I s. Low-resource option: Standardized paper-to-digital intake. Risks: Data breach and surveillance overreach.

* Health care-system leveling: Starts with national capability inventories. Progresses to standardized decontamination protocols. Matures into dynamic capacity balancing. Low-resource option: Use index as a gap map for mutual aid. Risks: Capacity overstatement.

* A I and decision support: Starts with cataloging use cases and M E N A datasets. Progresses to external validation and adversarial testing. Matures into operational deployment with human-in-the-loop. Low-resource option: Rule-based aids first. Risks: Bias and automation complacency.

Abbreviations: AI, artificial intelligence; CBRN, chemical, biological, radiological, and nuclear; IHR, International Health Regulations; MENA, Middle East and North Africa; XAI, explainable AI.

A National Emergency Management Advisory Council

The proposed National Emergency Management Advisory Council would operate at a senior inter-ministerial level with a defined statutory mandate, coordinating health-sector planning with ministries of defense, interior, civil defense, and foreign affairs (Figure 1). Its functions would include oversight of antidote stockpile distribution; national and international training and exercise programs; integration of poison centers; activation of cross-border contingency protocols; and ownership of a national CBRN incident database and preparedness dashboard. Legal authority, financing, and data governance are not afterthoughts but preconditions, and are specified in Table 1 alongside a phased implementation roadmap and minimum viable options for low-resource settings.

Figure 1.

National CBRN preparedness and response: conceptual systems architecture linking governance, preparedness, data systems, and AI-enabled decision support, with enabling conditions (legal authority, financing, data governance) and safeguards.

Figure 1. Long description

A horizontal tree diagram flows from a single root node on the left to multiple leaf nodes on the right.

Root Node: Emergency Management Advisory Council.

Primary Branches (Top to Bottom):

1. Governance and Safeguards (Red): Branches into Analog Redundancy and Hybrid Protocols, Privacy-by-Design and Access Control, Explainable A I and Human Accountability, Robustness and Cybersecurity Hardening, and External Validation and Generalizability.

2. A I-Enabled Decision Support (Purple): Branches into Simulation and Training (G A N scenarios), Intelligence and Triage (L L M / B E R T), Resource Optimization (R F / S V M plus R L), and Detection and Classification (C N N plus electrochemical).

3. Incident Response (Light Blue): Branches into Cross-border Protocol Activation, Communication Coordination, Activate Unified Command, and Intelligence and Threat Assessment.

4. Operational Level (Dark Blue): Sub-branches into Resource Management, which includes Training and Exercises, Coordinates with Incident Commander, Reports to National Emergency Management Director, Personnel Coordination, Equipment Deployment, Antidote Stockpile Management, and National C B R N Dashboard Oversight.

5. Strategic Level (Green): Sub-branches into International Liaison (I A E A Partnership, O P C W Liaison, W H O Coordination) and Inter-Ministerial Coordination (Ministry of Foreign Affairs, Ministry of Interior, Ministry of Defense, Ministry of Health, Direct access to the Prime Minister, and Reports to National Security Advisor).

Health Care System Leveling and Surge Coordination

Robust CBRN readiness requires mapping and standardizing the roles and capacities of the health care system.Reference Qzih and Ahmad ^{5 ,} Reference Anderson, Forman and Mossialos ⁶ Under the Emergency Management Advisory Council’s structure, all health care capabilities would be categorized and geolocated within a national preparedness index, distinguishing primary-, secondary-, and tertiary-level CBRN response. Following the field response, primary health centers would handle initial triage and communication; secondary facilities, basic decontamination and countermeasure administration; and tertiary facilities, full-spectrum management, including advanced decontamination for special, vulnerable populations (pediatric and elderly) and complex trauma.Reference Farhat, Tin and Shin ⁹ This leveling enables intelligent surge planning and is scalable for mass-gathering events such as the Hajj pilgrimage, where temporary mobile decontamination units, specially trained rapid-response teams, and streamlined communications must be available via interoperable systems. In low-resource or conflict-affected settings where tertiary capacity is scarce, the index doubles as a gap map that prioritizes mutual-aid requests and pre-negotiated cross-border referral.

The National CBRN Emergency Management Dashboard

A National CBRN Dashboard will serve as the central information hub, functioning as both a readiness tool and a dynamic incident-response interface. Developed under the governance of the National Advisory Council, it would integrate static data (facility readiness scores, antidote inventories) and real-time data (incident reports, meteorological data, biosurveillance indicators, and open-source signals), and provide high-risk-zone mapping, response simulation, triage decision trees, and geo-visualization of resource deployment. Its value depends on enforceable data governance, standardized schemas, data-sharing agreements, cybersecurity hardening, and privacy-preserving analytics, without which it becomes a liability rather than an asset (Table 1).

AI-Enhanced CBRN Response Capabilities

The integration of AI offers transformative opportunities to enhance response times, predict threat escalation, optimize antidote logistics, and support decision-making amid uncertainty. Simultaneously, it presents novel dual-use risks that require regulations and ethical guidelines. Research and pilot deployments have demonstrated that supervised machine learning models, namely random forest and support vector machine, can predict decontamination resource needs with >90% accuracy based on location, weather, and casualty load characteristics.Reference Briffa ^{7 ,} Reference Plümper and Neumayer ⁸ In these studies, such algorithms outperformed the static, scenario-based approaches prevalent across MENA health systems. Deep learning architectures, such as convolutional neural networks (CNNs), have achieved agent classification accuracies >99% when combined with real-time electrochemical detection systems.Reference Farhat, Tin and Shin ⁹ These high-accuracy deep learning algorithms enable the real-time identification and classification of CBRN agents during the critical early detection phase, allowing emergency responders to implement appropriate decontamination protocols and medical countermeasures within minutes rather than hours. These figures, however, derive from controlled studies on curated datasets and must not be read as evidence of operational readiness; deployment depends on representative regional training data, prospective external validation, demonstrated generalizability and robustness to distribution shift, and resilience to adversarial manipulation and cyberattack.Reference Chauhan ¹⁰ The integration of CNN-based detection systems into the proposed National CBRN Dashboard provides automated threat assessment capabilities that surpass traditional manual analysis methods, enabling the National Emergency Management Advisory Council to coordinate rapid, evidence-based efforts across multiple agencies and to alert regional and international partners.

Reinforcement learning, an AI approach that learns optimal decisions through trial-and-error simulations, presents another critical example of algorithmic integration in emergency management strategies. Unlike traditional planning methods that rely on predetermined protocols, reinforcement learning algorithms continuously test different response scenarios in virtual environments to identify the most effective resource allocation strategies. In CBRN emergency operation plans, these systems can simulate thousands of mass-casualty decontamination scenarios, learning to optimize critical decisions, such as the sequencing of antidote distribution, the deployment of medical personnel across multiple incident sites, and the prioritization of patient triage categories based on agent type and exposure severity. Through repeated virtual training, the algorithm can develop decision-making capabilities that exceed human planners in speed and accuracy, enabling more effective health care responses to actual CBRN incidents. According to a recent article, these models reduced decontamination throughput time while maintaining decontamination efficacy in mixed-agent scenarios.Reference Briffa ⁷ These dynamic frameworks evolve in response to system conditions and adjust to resource shortages or changes in situational awareness.

In addition to rapid response capabilities, advanced AI text-processing systems, or large language models, transform the way emergency planners gather information and develop strategies. AI systems, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer, can function as complex reading and writing assistants that can instantaneously process large amounts of text-based information.Reference Kegyes, Süle and Abonyi ¹¹ Unlike human analysts, who may require hours or days to review emergency protocols, scientific literature, and intelligence reports, AI systems can read and analyze thousands of documents simultaneously, identifying patterns and connections that are not discernible to humans.Reference Kegyes, Süle and Abonyi ¹¹ Natural language processing tools can scan unstructured social media, public communications, and field reports for indicators of threats. BERT-based systems can detect potential CBRN activity planningReference Chimba ¹² making them powerful tools for anticipatory logistics, policy activation, and risk communication strategies. Moreover, compared to conventional drill templates, generative adversarial networks can simulate highly realistic CBRN scenarios, including factors not typically represented in traditional drill templates.Reference Murugan, Gopalakrishnan and Sakthivel ¹³ These simulations can improve responder readiness and enable risk-sensitive training with near-realistic variables in local and international contexts. Each capability carries a proportionate risk, hallucination, bias, automation complacency, electronic vulnerability during radiological/nuclear events, and adversarial misuse. Simultaneously, generative models require regulation against adversarial misuse, while AI limitations, including accountability gaps, electronic vulnerability during radiological/nuclear attacks, and cybersecurity threats, necessitate hybrid manual-digital protocols, hardened backups, and governance with analog redundancy for operational continuity.

Nevertheless, Explainable AI systems are crucial in clinical and operational settings, where human accountability is essential.Reference Chauhan ¹⁰ Medical personnel must understand AI-generated recommendations to maintain clinical responsibility and legal compliance in life-or-death decisions. Transparent AI processes enable bias identification, clinical validation, and trust preservation while ensuring human oversight remains paramount during high-stress emergencies.Reference Chauhan ¹⁰

Innovation, Ethics, and Research Implications

Priority research includes externally validated, MENA-specific datasets; benchmarking against existing scenario-based planning; adaptive treatment systems for rare or mixed-agent exposure; and high-resolution computer vision to monitor persistent contaminants in hair and skin creases.Reference Dev, Balasubramanian and Ngoc ¹⁴ Equally important is implementation research on legal authority, financing sustainability, and equitable deployment across the region’s resource gradient.