All field notes

Cross-border engineering works when ownership, security, and verification are designed in. This whitepaper describes a delivery model that pairs US accountability with offshore engineering capacity, and the controls that make it reliable.

Generative AI is only as good as the enterprise content it can reach, trust, and cite. This whitepaper defines data readiness for LLM and agent systems, how to assess it, and how to close the gaps before they become production failures.

React and Next.js power modern web apps. How to hire developers who ship fast, SEO-ready production front ends—whether in-house or through an offshore partner.

A prompt template is a reusable, parameterised prompt managed as a versioned artefact rather than a string in application code. This explainer covers why inline prompts become unmaintainable, how variable injection creates security risk, why prompt changes need the same gates as code, and how to organise templates.

AI literacy is the practical understanding people need to use AI systems well and judge their output. This explainer covers what different roles actually need to know, why tool training is not literacy, which misconceptions cause the most damage, and how to build capability that changes behaviour.

AI risk tiering classifies systems by the impact they can cause, so that governance effort follows risk rather than applying uniformly. This explainer covers what actually drives a tier, why uniform controls fail in both directions, how to stop teams under-classifying, and when a system should be re-tiered.

Algorithmic transparency covers what organisations disclose about the automated systems affecting people. This explainer covers the different audiences and what each needs, why publishing technical detail is not transparency, what disclosure is increasingly expected, and how to be transparent without exposing what should stay closed.

An AI inventory records every AI system an organisation operates, and building one almost always reveals more than governance knew about. This explainer covers what each entry must capture, how to discover systems nobody registered, why the inventory underpins every other control, and how to keep it from going stale.

Shadow AI is AI used inside an organisation without sanction or oversight, and prohibition reliably drives it further underground. This explainer covers why people use unsanctioned tools, the specific risks that follow, why blocking fails, and what actually brings usage into visibility.

Autonomous agents take actions, not just generate text, which changes what governance has to control. This whitepaper sets out a practical agentic AI governance framework: permissions, gates, evaluation, audit, and accountability.

Agents hold credentials, call tools, and act on untrusted content, which makes them a new class of attack surface. This whitepaper lays out the threat model and a defense-in-depth security architecture for production agents.

AI systems fail in production for human reasons more often than technical ones. This whitepaper gives operating leaders a change management approach for AI: role redesign, trust building, adoption engineering, and measurement.

Financial institutions already run mature control frameworks. AI has to fit inside them, not around them. This whitepaper maps LLM and agent controls to model risk management, fair lending, data governance, and third-party risk expectations.

You cannot unit-test a model into correctness. This whitepaper sets out a complete evaluation and testing strategy for AI systems: golden datasets, metrics, LLM-as-a-judge with calibration, safety testing, regression gates, and production sampling.

Legal teams are under pressure to do more with flat budgets and cannot tolerate fabricated citations or privilege leaks. This whitepaper maps where AI delivers in legal operations and the controls each use requires.

Resilient supply chains detect disruption early and respond fast. This whitepaper explains where AI strengthens sensing, planning, and response, what data it needs, and how to deploy it with human control over consequential decisions.

You cannot operate what you cannot see, and AI systems hide their failures inside plausible outputs. This whitepaper defines an observability reference model for LLM applications and agents: traces, metrics, quality, cost, drift, and alerting.

Blockchain bugs are expensive and irreversible. How to outsource blockchain development safely—what to verify, why audits matter, and how to pick a partner.

A data protection impact assessment identifies and mitigates privacy risk before a system is built. This explainer covers when one is required for AI systems, what the assessment must actually examine, why AI raises questions conventional assessments miss, and how to keep it a design tool rather than paperwork.

Data residency governs where data may be stored and processed, and AI systems touch it in more places than teams expect. This explainer covers why inference is processing, which components hold residency-relevant data, how residency differs from sovereignty, and what a compliant AI architecture looks like.

De-identification removes or obscures information that identifies individuals, and it is weaker than commonly assumed because re-identification from combinations of attributes is often possible. This explainer covers the techniques, the difference from pseudonymisation, why free text is hardest, and how to use it honestly in AI pipelines.

Multi-region AI deployment distributes inference and data across geographies for latency, residency, or resilience. This explainer covers which driver applies, why data residency produces a different architecture from latency optimisation, how model availability varies by region, and the operational cost of running several regions.

Pseudonymization replaces identifying values with tokens that can be reversed by whoever holds the mapping. This explainer covers how it differs from anonymisation, why the mapping is the whole security question, where it fits in AI pipelines, and why it does not remove data protection obligations.

Purpose limitation requires that data collected for one purpose is not reused for an incompatible one, and AI projects collide with it constantly because they reuse existing data at scale. This explainer covers what compatibility means, why training is usually a new purpose, and how to design around the principle.