Artificial Intelligence

prepare business data for ai a single funnel on plinth

Business Data for AI: Essential Prep Steps to Avoid Risk

Most AI projects stall between a convincing demo and something the business can depend on, and the reason is almost never the model. It is the material underneath it: duplicated customer records, nine versions of the same price list, scanned PDFs no parser can read, and permissions that were never designed to be queried by a machine. This guide sets out a seven-step preparation programme for the business data estate — inventory, quality profiling, structure and formats, metadata, classification and security, the delivery layer, and the measurement that keeps it honest. It closes with realistic costs, timelines, what each AI use case actually demands, and the mistakes that quietly stall these programmes.

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ai assurance vs ai governance a two interlocking puzzle blocks

AI Assurance vs AI Governance: Essential Guide to Avoid Risk

AI governance sets the rules, roles and decision rights for the AI you run. AI assurance is the evidence that those rules were followed and that the system behaves as claimed. The two words are used interchangeably, and the confusion costs money: policies nobody tests, or tests nobody asked for. This guide separates them cleanly with a side-by-side comparison table, the assurance techniques that actually produce evidence a buyer or regulator will accept, a RACI grid for who owns what, what the UK’s assurance-led approach and the EU AI Act each demand, realistic cost profiles, a 90-day plan to stand both up, and the mistakes that make an assurance programme worthless.

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Prompt Injection Risk: Essential Safe Assessment Guide

Prompt injection is the one attack class against business AI that has no structural fix, because instructions and data reach a language model through the same channel. This guide turns that into something you can manage: where injected instructions actually enter a business system, how to scope an assessment so it finishes in days, a ten-question likelihood and impact matrix anchored to observable facts, a control map showing which measures leave a low residual and which depend on the model behaving, how to build an injection corpus and test the boundary rather than the model, how to record findings in a register an auditor can follow, who owns the risk and what UK and EU regulators expect, realistic costs, and a 90-day plan.

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AI Agent Security: Essential Guide to Safe Tool Access

The moment you connect a language model to a ticketing API, a finance system, a mailbox or a shell, you stop shipping a chat feature and start shipping a new class of privileged user. This guide covers the engineering work that keeps that user contained: how to classify and scope tools into risk tiers, why an agent needs its own identity and short-lived credentials rather than a shared service account, how to contain the blast radius of a single compromised run with sandboxing and default-deny egress, where to place human approval gates so they are decisive rather than theatre, what actually works against indirect prompt injection arriving through retrieved content, what to log so an incident is investigable, how to test the controls before launch, and a 90-day rollout plan with realistic costs and named owners.

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AI red-teaming - ai red teaming what to test before launch a hexagonal shield upright

AI Red-Teaming: Essential Tests to Run Before a Safe Launch

Most AI systems reach launch having been tested only by people trying to make them work. Adversarial testing asks the other question: what happens when someone actively tries to make the system misbehave. This guide sets out what to test before go-live — how to scope the exercise and define harm in your own domain, the five attack classes that matter for business deployments, whether to run it internally, buy it in or automate it, how to score findings so severity cannot be renegotiated after the fact, which fixes actually hold, what the work costs in person-days and pounds, and the evidence pack that answers an enterprise security questionnaire and maps onto the EU AI Act, ISO 42001 and the NIST AI Risk Management Framework.

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ai governance framework for smes a four stacked hexagonal plates

AI Governance Framework: Essential SME Guide to Avoid Risk

Most AI governance frameworks are written for banks. They assume a risk committee, a model validation team and a compliance officer with nothing else to do, so the 60-person business downloads the template and never uses it. This guide sets out the version that actually works at SME scale: six components, four risk tiers, five questions that decide the tier, four roles instead of a committee, controls configured inside the platforms you already license, and an evidence pack that answers an enterprise security questionnaire in an afternoon. It maps the whole thing onto ISO/IEC 42001, the NIST AI Risk Management Framework and the EU AI Act, sets out a 90-day rollout plan, states the real cost in person-days, and lists the mistakes that waste a year.

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eu ai act compliance checklist uk a three stacked hexagonal plates

EU AI Act Compliance: Essential UK Checklist to Avoid Risk

Brexit did not put UK companies outside the EU AI Act. The Regulation follows the market and the output, so a Manchester software firm selling into Dublin, a recruitment platform screening candidates in Berlin and a consultancy whose model scores French loan applications are all inside its reach. This guide turns Regulation (EU) 2024/1689 into a working checklist for UK teams: the dates that already bite, how to inventory your systems, how to work out whether you are a provider or a deployer, how to classify risk against the prohibited and high-risk tiers, the twelve technical obligations that attach to a high-risk system, what to demand from general-purpose model vendors, and the penalties waiting at the end.

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AI Model Exit Strategy: Proven Guide to Avoid Costly Risk

Model portability is an engineering property, not a legal one. This guide sets out where AI lock-in actually forms across five layers — the API surface, the prompt estate, embeddings, fine-tunes and the evaluation record — and what a switch really costs at each level of preparation. It covers the abstraction patterns that pay for themselves, why an evaluation harness is the instrument that makes a switch defensible, which assets transfer between models and which do not, the contract terms that keep the door open, and a 90-day runbook you can run without pausing delivery.

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ai procurement checklist a three interlocking rings

AI Procurement Checklist: Essential Guide for Safe Buying

Buying AI is not like buying a database. The product changes after you sign, your data may never come back, and legal, security and IT each see a different danger. This guide sets out a complete AI procurement checklist for all three teams, organised the way a purchase actually moves: intake and triage, three parallel reviews, evidence instead of assurances, scoring with three possible outcomes, and the contract clauses worth arguing over. It covers training rights, output ownership, retention limits, prompt injection, model provenance, cost ceilings and exit paths, plus how to keep the whole process fast enough that nobody bypasses it.

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