RAIDT — One-Pager

Polished summary for any commercial / partnership / standards conversation. Use as email attachment, conversation primer, or printout.

RAIDT — A run-level evidence framework for governing generative AI

Responsibility · Auditability · Interpretability · Dependability · Traceability

What it is

RAIDT is a peer-reviewed governance framework for individual GenAI runs in organisations. It produces two linked artefacts:

1. A run-level evidence pack — bounded record of one configured GenAI run (prompt, model deployment, retrieval context, parameters, safeguards, output, human review, final use)
2. A 5-pillar scoring profile — observable assessment of governance readiness scored 1–5 across Responsibility, Auditability, Interpretability, Dependability, Traceability

Why now

The compliance burden on organisations using generative AI is shifting from policy commitments to evidence. The EU AI Act, ISO/IEC 42001, NIST AI RMF, and UK regulator guidance (ICO, FCA, MHRA, SRA) increasingly require organisations to evidence what happened in a specific AI-assisted decision and how it was governed. Existing tooling (model cards, principles, generic risk registers) governs at the model or policy level — not at the run level where contestability and accountability live.

What's new about RAIDT

• Run-level evidence — the unit at which disputes, audits, and accountability actually operate
• Cross-standards mapping — EU AI Act, ISO/IEC 42001, NIST AI RMF + GenAI Profile
• Empirical validation using influence methods (RAG, PEFT/LoRA, RLHF/DPO) as governance interventions
• Sector playbooks ready in healthcare, finance, education, environment, crisis, supply chain, cybersecurity, public policy, law, R&D, creative industries, planning, ageing societies

Who it's for

• Audit firms — defensible methodology for AI assurance engagements (Big-4, mid-tier)
• Regulated organisations — operational evidence to satisfy regulators (NHS, banks, insurers, law firms)
• AI assurance vendors — academic-backed methodology IP for product differentiation
• Public-sector procurement — methodology-as-procurement-standard

Academic foundation

Trilogy of peer-reviewed papers:

1. Foundations — RAIDT as run-level governance evidence framework (design science methodology)
2. Empirical Validation — measuring governance readiness using influence methods
3. Interoperable Governance — policy pathways across EU AI Act, ISO/IEC 42001, NIST AI RMF

Plus the Configured Runs manuscript on the configured run as a run-level evidence object for accountable GenAI.

Lead and team

• Mohammad Ali Akeel (PhD researcher, lead developer) — University of Portsmouth
• Prof. Mark Xu (lead supervisor) — School of Organisations, Systems and People
• Dr Awais Shakir Goraya (co-supervisor)
• Dr Salem Chakhar (co-supervisor)

Standards engagement

Active engagement with: BSI ART/1 (UK national AI committee), AI Standards Hub, AISI external research, NIST GenAI Profile working groups.

Contact

mohammad.akeel@myport.ac.uk

Trade marks: RAIDT™ and RAIT™ pending registration with UKIPO. © University of Portsmouth / Mohammad Ali Akeel, 2026.