Fragmented global mandates
Conflicting frameworks, from the EU AI Act to evolving US privacy obligations, require evidence that travels across jurisdictions.
Governance intelligence for boards, legal teams, and AI risk owners.
Board-Ready AI Oversight
Convert model risk, data lineage, and regulatory uncertainty into defensible evidence your executives can act on.
In the regulatory gray area, auditable AI intelligence becomes the basis of public trust, investor confidence, and fiduciary defensibility.
Conflicting frameworks, from the EU AI Act to evolving US privacy obligations, require evidence that travels across jurisdictions.
Boards face exposure when AI leaks proprietary data, hallucinates material claims, or trains on unconsented copyrighted datasets.
Employees using unauthorized AI inside enterprise workflows create silent data exfiltration and intellectual property risk.
Stress-testing for LLMs and proprietary neural networks to detect algorithmic drift, bias propagation, black-box vulnerabilities, and reproducibility failures before deployment.
Mapping data ancestry so every model input can be traced to lawful origin, consent posture, intellectual property status, and privacy obligations.
Active middleware that monitors live AI inputs and outputs to prevent data leakage, hallucination exploits, and non-compliant processing in real time.
Surface hidden model drift, bias propagation, and black-box risks before they reach production decisions.
Map every model input to its lawful origin, consent posture, and IP status — an audit trail your board can rely on.
Block data leakage, hallucination exploits, and policy violations with active middleware monitoring AI in real time.
Every workstream produces evidence executives can defend: validation records, lineage maps, control telemetry, and risk narratives suitable for regulators, investors, and insurers.
AI Compliance & Governance practitioner, MSc Data Science candidate at Loughborough University, and founder of Govtelligence. Rayhan helps organisations use fewer, better AI tools with clearer data controls, risk thresholds, and executive accountability.
Rayhan combines hands-on AI evaluation work, business analysis, public sector consulting exposure, and enterprise research engagements to translate technical model risk into language boards, legal teams, and operators can act on.
Loughborough University, with prior BSc Economics from the University of Westminster.
Developing structured leadership, consulting, problem-solving, and executive communication capability.
AI Adoption Strategist at Prolific, AI Trainer at Outlier, Prompt Engineer at DataAnnotation, and AI Talent Member at Turing.
EY Technology Risk, KPMG Audit, Goldman Sachs Operations, Bloomberg ESG, and IBM AI ethics credentials.
MSc research on temporal generalisation, SHAP/LIME stability, fraud detection, and explainability monitoring.
Independent consulting exposure across AlphaSights, Guidepoint, NewtonX, Tegus, and public sector AI procurement work.
Auditability, lineage, and live compliance infrastructure for enterprises deploying high-stakes models.
Schedule Executive BriefingWe begin with model inventory, risk exposure, data lineage, and shadow AI discovery to establish where fiduciary, regulatory, and operational liabilities concentrate.
We convert ambiguous AI usage into documented control evidence: regulatory mapping, audit trails, validation records, and escalation protocols.
The objective is governed velocity. Controls are designed around deployment latency, model scalability, vector database security, and developer workflow.
Directors receive a clear view of algorithmic trust, residual risk, governance maturity, and investment priorities tied to fiduciary duty and brand equity.