Agentic Artificial Intelligence for Ethical Cybersecurity in Uganda: A Reinforcement Learning Framework for Threat Detection in Resource-Constrained Environments

• URL: http://arxiv.org/abs/2512.07909v1
• Date: Mon, 08 Dec 2025 05:44:25 GMT
• Title: Agentic Artificial Intelligence for Ethical Cybersecurity in Uganda: A Reinforcement Learning Framework for Threat Detection in Resource-Constrained Environments
• Authors: Ibrahim Adabara, Bashir Olaniyi Sadiq, Aliyu Nuhu Shuaibu, Yale Ibrahim Danjuma, Venkateswarlu Maninti, Mutebi Joe,
• Abstract summary: This study proposes an Agentic Artificial Intelligence (AAI) framework that integrates reinforcement learning, an explicit ethical governance layer, and human oversight.<n>A CPU-optimized simulation environment was developed using a five-node network topology that mirrors key elements of Uganda's critical digital infrastructure.<n>The AAI framework achieved a 100 percent detection rate, zero false positives, and full ethical compliance, compared with 70 percent detection and 15 percent false positives for the baseline system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Uganda's rapid digital transformation, supported by national strategies such as Vision 2040 and the Digital Transformation Roadmap, has expanded reliance on networked services while simultaneously increasing exposure to sophisticated cyber threats. In resource-constrained settings, commonly deployed rule-based intrusion detection systems lack the adaptability and ethical safeguards needed to address evolving attack patterns, leading to undetected breaches and excessive blocking of legitimate traffic. This study proposes an Agentic Artificial Intelligence (AAI) framework that integrates reinforcement learning, an explicit ethical governance layer, and human oversight to deliver adaptive and trustworthy cybersecurity. A CPU-optimized simulation environment was developed using a five-node network topology that mirrors key elements of Uganda's critical digital infrastructure and generates both benign and malicious traffic, including phishing, ransomware, and distributed denial-of-service attacks. A Q-learning agent, operating within clearly defined ethical constraints and subject to human auditability, was trained and evaluated against a traditional rule-based baseline. The AAI framework achieved a 100 percent detection rate, zero false positives, and full ethical compliance, compared with 70 percent detection and 15 percent false positives for the baseline system. These results demonstrate that agentic, ethically governed reinforcement learning can substantially improve cybersecurity effectiveness and fairness in CPU-only, resource-constrained environments, offering a practical pathway for operationalizing responsible AI in Uganda's national cybersecurity strategy.

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