Muhammad Nabeel Amin

Muhammad Nabeel Amin

Curious Software Engineer and AI Researcher

Operationalizing LLM Governance with Enterprise Knowledge Graphs

1 minute read

Published:

Large language models amplify institutional knowledge yet they also magnify the risk of hallucinated citations and policy drift. My current research integrates enterprise knowledge graphs as both a grounding substrate and a verifiable audit trail. Retrieval augmented generation pipelines typically treat knowledge stores as passive context providers. I invert this relationship by requiring the model to declare explicit graph traversals before composing a response. Each traversal is validated against schema rules and access control policies so that the model cannot fabricate entities or reference embargoed data.

To translate this mechanism into operational governance, we embedded the traversal logs into our compliance dashboard. The dashboard computes adherence metrics such as provenance completeness and policy coverage per business unit. Risk officers can drill down to the exact subgraph that informed a recommendation and confirm whether the underlying data sources were approved for that audience. When deviations occur, the system triggers a mandatory justification workflow where developers must either update the ontology or retrain the model to respect existing constraints.

The interplay between language models and curated knowledge graphs also fosters continuous improvement. Analysts contribute new entities through a moderated curation queue that simultaneously updates the ontology and retrains the retrieval index. Because the governance feedback loop is now data-centric, teams iterate faster while remaining within formal compliance boundaries. The approach demonstrates that scalable LLM oversight is achievable when knowledge representation, access policies, and generative reasoning are engineered as a unified socio-technical system.

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Interpretable Foundations for Trustworthy Agentic AI

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Agentic artificial intelligence systems promise autonomous adaptation and self-directed problem solving, yet their adoption in regulated domains hinges on verifiable transparency. In recent deployments I have observed that practitioners still rely on coarse attribution estimates derived from gradient saliency maps, even though these signals often collapse under distributional drift. I argue that an interpretable agentic stack must start with causal specification of decision objectives. Structural causal models provide a formal scaffold that distinguishes policy intent from the mutable patterns surfaced by data-driven planners. By encoding policy constraints as counterfactual queries, it becomes possible to debug agent trajectories with surgical precision.

Field Experiments in Human-in-the-Loop Machine Learning

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Laboratory benchmarks rarely capture the socio-technical friction encountered when machine learning systems operate alongside frontline practitioners. To investigate this gap, I designed a series of field experiments across public health clinics that employ human-in-the-loop triage models. The studies revealed that data scientists often underestimate the latency introduced by manual override pathways. Clinicians needed interpretable uncertainty cues, not binary predictions, in order to calibrate their trust. We therefore redesigned the interface to surface calibrated risk intervals and provenance notes summarising the data regimes most responsible for each recommendation.