Tag Archives: AI Governance

When Adoption Becomes The Goal, Risk Becomes Invisible By Design

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This article examines how AI risk is obscured when organisations prioritise adoption over governance. Drawing on real-world examples, it argues that widespread AI usage is already endemic; but largely shallow, uncontrolled, and poorly understood. In regulated environments, optimising for uptake before addressing data lifecycle, verification, leakage, and accountability is not innovation, but a dangerous substitution of metrics for responsibility.

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Unable To Load Conversation: Why ChatGPT Is Not Infrastructure

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A case study in how “AI support” fails the moment it actually matters. This article documents the loss of a critical ChatGPT workspace conversation through backend failure, followed by a support process that denied reality, looped incompetently, and ultimately could not accept its own diagnostic evidence. It exposes systemic fragility, misplaced corporate faith in “Copilot”, and why treating LLMs as reliable infrastructure, especially in regulated environments, is reckless.

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Series Wrap-Up: Reconstructing Time, Truth, and Trust in UK Financial Services Data Platforms

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This series explored how UK Financial Services data platforms can preserve temporal truth, reconstruct institutional belief, and withstand regulatory scrutiny at scale. Beginning with foundational concepts such as SCD2 and event modelling, it developed into a comprehensive architectural pattern centred on an audit-grade Bronze layer, non-SCD Silver consumption, and point-in-time defensibility. Along the way, it addressed operational reality, governance, cost, AI integration, and regulatory expectations. This final article brings the work together, offering a structured map of the series and a coherent lens for understanding how modern, regulated data platforms actually succeed. Taken together, this body of work describes what I refer to as a “land it early, manage it early” data platform architecture for regulated industries.

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From Build to Run Without Losing Temporal Truth: Operating Model Realities for Regulated Financial Services Data Platforms

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This article explores why most regulated data platforms fail operationally rather than technically. It argues that the operating model is the mechanism by which architectural intent survives change, pressure, and organisational churn. Focusing on invariants, authority, correction workflows, and accountability, it shows how platforms must be designed to operate safely under stress, not just in steady state. The piece bridges architecture and real-world execution, ensuring temporal truth and regulatory trust persist long after delivery.

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Cost Is a Control: FinOps and Cost Management in Regulated Financial Services Data Platforms

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This article positions cost management as a first-class architectural control rather than a post-hoc optimisation exercise. In regulated environments, cost decisions directly constrain temporal truth, optionality, velocity, and compliance. The article explains why FinOps must prioritise predictability, authority, and value alignment over minimisation, and how poorly designed cost pressure undermines regulatory defensibility. By linking cost to long-term value creation and regulatory outcomes, it provides a principled framework for sustaining compliant, scalable data platforms.

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From Threat Model to Regulator Narrative: Security Architecture for Regulated Financial Services Data Platforms

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This article reframes security as an architectural property of regulated financial services data platforms, not a bolt-on set of controls. It argues that true security lies in preserving temporal truth, enforcing authority over data, and enabling defensible reconstruction of decisions under scrutiny. By grounding security in threat models, data semantics, SCD2 foundations, and regulator-facing narratives, the article shows how platforms can prevent silent history rewriting, govern AI safely, and treat auditability as a first-class security requirement.

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Aligning the Data Platform to Enterprise Data & AI Strategy

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This article establishes the data platform as the execution engine of Enterprise Data & AI Strategy in Financial Services. It bridges executive strategy and technical delivery by showing how layered architecture (Bronze, Silver, Gold, Platinum), embedded governance, dual promotion lifecycles (North/South and East/West), and domain-aligned operating models turn strategic pillars, architecture & quality, governance, security & privacy, process & tools, and people & culture, into repeatable, regulator-ready outcomes. The result is a platform that delivers control, velocity, semantic alignment, and safe AI enablement at scale.

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Building Regulator-Defensible Enterprise RAG Systems (FCA/PRA/SMCR)

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This article defines what regulator-defensible enterprise Retrieval-augmented generation (RAG) looks like in Financial Services (at least in 2025–2026). Rather than focusing on model quality, it frames RAG through the questions regulators actually ask: what information was used, can the answer be reproduced, who is accountable, and how risk is controlled. It sets out minimum standards for context provenance, audit-grade logging, temporal and precedence-aware retrieval, human-in-the-loop escalation, and replayability. The result is a clear distinction between RAG prototypes and enterprise systems that can survive PRA/FCA and SMCR scrutiny.

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Temporal RAG: Retrieving “State as Known on Date X” for LLMs in Financial Services

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This article explains why standard Retrieval-Augmented Generation (RAG) silently corrupts history in Financial Services by answering past questions with present-day truth. It introduces Temporal RAG: a regulator-defensible retrieval pattern that conditions every query on an explicit as_of timestamp and retrieves only from Point-in-Time (PIT) slices governed by SCD2 validity, precedence rules, and repair policies. Using concrete implementation patterns and audit reconstruction examples, it shows how to make LLM retrieval reproducible, evidential, and safe for complaints, remediation, AML, and conduct-risk use cases.

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AI Agents for Temporal Repair, Metadata Enrichment, and Regulatory Automation

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This article explains where AI agents genuinely add value in regulated Financial Services data platforms — and where they become indefensible. It distinguishes “useful” agents from regulator-defensible ones, showing how agents can safely detect temporal defects, propose repairs, enrich metadata, and draft regulatory artefacts without bypassing governance. Using practical patterns and real failure modes, it defines a strict operating model — detect, propose, approve, apply — that preserves auditability, replayability, and SMCR accountability while delivering operational leverage that survives PRA/FCA scrutiny.

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Integrating AI and LLMs into Regulated Financial Services Data Platforms

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How AI fits into Bronze/Silver/Gold without breaking lineage, PIT, or SMCR: This article sets out a regulator-defensible approach to integrating AI and LLMs into UK Financial Services data platforms (structurally accurate for 2025/2026). It argues that AI must operate as a governed consumer and orchestrator of a temporal medallion architecture, not a parallel system. By defining four permitted integration patterns, PIT-aware RAG, controlled Bronze embeddings, anonymised fine-tuning, and agentic orchestration, it shows how to preserve lineage, point-in-time truth, and SMCR accountability while enabling practical AI use under PRA/FCA scrutiny.

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The Risks of Self-Hosting DeepSeek: Ethical Controls, Criminal Facilitation, and Manipulative Potential

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Self-hosting advanced AI models like DeepSeek grant unparalleled control but poses severe risks if ethical constraints are removed. With relatively simple modifications, users can disable safeguards, enabling AI to assist in cybercrime, fraud, terrorism, and psychological manipulation. Such models could automate hacking, facilitate gaslighting, and fuel disinformation campaigns. The open-source AI community must balance innovation with security, while policymakers must consider regulations to curb AI misuse in self-hosted environments before it becomes an uncontrollable threat.

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