AliAkarma
I build agentic AI systems that know their own limits. My work focuses on the gap between autonomous capability and institutional accountability — designing architectures where AI agents can be stopped, audited, and corrected when they behave unexpectedly. I'm a 4th-year IT student at the Islamic University of Madinah and have published 16 peer-reviewed papers and manuscripts on AI governance, adversarial robustness, and constrained multi-agent systems.
I study how to make autonomous AI systems fail safely: designing governance architectures that prevent unintended actions before they propagate through real-world infrastructure.
Current Research Frontier
Autonomous Safety Governance
Safety-Critical Multi-Agent Systems
"Investigating cryptographic trust-anchors and constrained reasoning for large-scale agentic deployments."
Why This
Research
Matters
I study how to make autonomous AI systems fail safely: designing governance architectures that prevent unintended actions before they propagate through real-world infrastructure.
My work addresses the alignment problem in deployed agentic systems — exploring how we can build autonomous pipelines that remain safe and governable when exposed to adversarial inputs, distributional shift, or misaligned incentives. I approach this through the intersection of safety engineering, formal governance frameworks, and empirical failure-mode analysis.
Research Landscape
Recent Publications
EAGF: A Four-Pillar Ethical AI Governance Framework for Trustworthy Cybersecurity in 5G Renewable Energy IoT Systems
Salman Jan, Ali Akarma, Toqeer Ali Syed, Munir Azam Muhammad, Shahid Kamal
Agentic AI-enhanced digital twins for Smart City civil infrastructure: A secure, autonomous and auditable management framework
Toqeer Ali Syed, Ali Akarma, Ali Alatify, Muhammad Tayyab Naqash, Abdulaziz Alqurashi
ADAPT: An Agentic AI Framework for People with Disabilities and Neurodivergence
Muhammad Shoaib Siddiqui, Toqeer Ali Syed, Ali Akarma