AI governance services
AI governance with confidence, control and evidence
InfraTech AI Governance takes enterprises from fragmented AI oversight to a governed operating model: discover what is running, assess the risk, embed approval gates, govern agentic AI and keep audit-ready evidence across the lifecycle.
- ISO 27001 information security
- ISO 9001 quality management
- Cyber Essentials Plus
- ISO 42001 aligned AI governance
- Leeds based, national reach
AI governance is now a board-level operating challenge
AI is being embedded into products, workflows, decisions, software delivery and customer operations. At the same time, regulatory expectations, enterprise procurement requirements and internal risk controls are becoming more demanding. The EU AI Act, ISO/IEC 42001 and the NIST AI Risk Management Framework all push organisations to prove that AI is governed in practice, not just described in policy.
Seven questions decide whether you can.
- Inventory. What AI systems and use cases do we have, where are they operating and who owns them?
- Approval. What risks, policies and approval requirements apply before an AI initiative moves forward?
- Delivery. How do we embed governance workflows and approval gates into architecture, build and release?
- Drift. How do we know whether AI remains within its approved state after deployment? Most governance work answers the first three questions and stops. This is the one that decides whether any of it held.
- Autonomy. How much autonomy should an AI system or agent be allowed to exercise?
- Evidence. Can we trace changes, decisions and governance evidence when assurance or audit is required?
- Pace. How is AI adoption changing, and is governance keeping pace with it?
- Where do you stand? Most organisations can answer the first three and not the last four. A short governance conversation will tell you which of the seven is actually exposing you, and what the evidence would need to show. Talk to the team
The AI you did not approve is already in use
Shadow AI is rarely malicious. People reach for whatever helps them do the job, and AI is now inside tools they already had. The exposure is not the tool itself, it is that nobody knows what has been pasted into it.
InfraTech discovery surfaces unsanctioned AI activity and maps it to teams, processes, data and exposure, so the response can be proportionate. Some of it gets sanctioned and brought under control. Some of it gets stopped. All of it stops being invisible.
Seven capabilities that make AI governance operational
- AI Discovery and Registry01 · See what you haveBuild visibility before governance. A governed view of AI systems, use cases, owners, data sources, vendors, agents and operational boundaries, so leaders know where AI is being used and who is accountable.AI landscape and use case discoveryUse case classificationSystem, actor and ownership mappingTrust boundary definitionAdoption and diffusion mappingGoverned inventory and baseline stateSee what AI you have. Understand where it sits. Know who is accountable.
- AI Risk Management and Policy Governance02 · Decide before you buildTurn AI intent into governed decisions. Risk classifications, policy requirements, approval workflows and controls settled before AI moves into build, deployment or operational use.AI risk register and assessment criteriaGovernance requirement definitionPolicy controls and governance constraintsWorkflows, approval gates and kill criteriaApproved decision artefactsDecision traceabilityEstablish what AI is allowed to do before it does it.
- Governance Guardrails and Control Gates03 · Put policy into deliveryConnect policy requirements with architecture reviews, development checkpoints, release approvals, operational readiness and rollback control, so governance sits inside delivery rather than beside it.Guardrails and pre-execution control gatesArchitecture validation and policy based controlsNon-functional and security architecture requirementsBuild and release policy validationProgressive rollout governanceRollback protocols and operational readinessMove from AI governance documentation to governance in execution.
- Continuous AI Governance and Monitoring04 · Govern after go-liveMove beyond the point in time assessment. Monitor governed state, detect drift, trigger review and keep controls aligned as systems, data, models and usage evolve.Continuous state monitoring and baseline comparisonDrift and governance deviation detectionImpact and blast radius analysisDrift reporting and impact assessmentRuntime governance review triggersClosed loop governance rule evolutionContinuously know whether AI remains within its governed state.
- AI and Agent Governance05 · Govern autonomyGovern AI agents and autonomous workflows with defined operating modes, human oversight, risk based escalation and clear criteria for intervention or rollback.Autonomy level definitionObservation, Assisted, Semi-Autonomous and Autonomous modesHuman in the loop oversightRisk, compliance, incident and drift aware autonomy adjustmentGoverned transitions and versioned autonomy statePredefined kill criteriaGive AI and agents only the autonomy their risk and operating conditions allow.
- Traceability and Audit Readiness06 · Keep the evidenceMaintain the evidence trail that assurance, audit and executive oversight need, linking decisions, controls, changes, ownership and outcomes across the AI lifecycle.Versioned AI system stateCode, infrastructure and data schema change trackingPolicy, adoption and autonomy change trackingDecision traceability and change impact mappingImmutable audit recordsAudit readiness measurementKnow what changed. Understand why. Maintain the evidence.
- AI Governance and Adoption Insights07 · Show the boardGive leadership a clear view of adoption, approval velocity, governance performance, drift, risk exposure and audit readiness across business units and technology environments.Adoption tracking and diffusion monitoringTime to approval and gate pass ratesDeployment velocity and stabilityDrift frequency and incident correlationArchitecture conformanceAudit readiness status and executive insightUnderstand where AI is growing, and whether governance is keeping pace.
- Not sure which of the seven you are missing?Most organisations are running two or three of these informally and evidencing none of them. A short governance conversation will tell you which gap is actually costing you, and what the evidence would need to show before the next tranche of AI investment is committed.Talk to the team
Seven governance outcomes
- Observable AI governance. Control posture you can look at, not a policy you have to believe.
- Shadow AI visibility. What is running, who is running it and what it can reach.
- Evidence-linked controls. Every control tied to operational activity that proves it is working.
- Clear ownership and decision rights. Named accountability and defined escalation before something goes wrong.
- Audit-ready traceability. The trail already exists when the question is asked.
- Continuous governance monitoring. Risk, exceptions and drift tracked as they happen.
- Faster and safer AI adoption. Teams move quicker because the guardrails are real and everyone knows where they are.
Controls that run with the work
A control applied at the end is a gate. A control applied during execution is governance. The difference shows up as weeks of rework in regulated sectors, so we place the checks where the decisions are made.
Design
Policy requirements translated into engineering controls, workflow rules and human approval points before a line of code exists. Data access, model choice and oversight decided deliberately.
Build and deploy
Standards validated during delivery. Guardrails, evaluation and agent behaviour testing run as part of the pipeline, with exceptions raised while they are still cheap to fix.
Operate
Continuous monitoring of risk, exceptions, drift and control posture in production, with traceability from business intent through to the behaviour users actually see.
An AI governance framework you can actually operate
AI governance is a continuous loop, not a project with an end date. Seven stages take you from discovering what is already running, through putting controls into the work, to proving decisions and improving oversight as adoption grows.
- Discover
- Decide
- Control
- Monitor
- Govern autonomy
- Prove
- Improve
Discover
Identify the AI use cases, systems, stakeholders, ownership and operating boundaries already in play, sanctioned or not, then set the baseline everything else is measured against.
Output
A complete AI inventory and an agreed enterprise context.
Decide
Establish the rules of operation: risk management requirements, policies, governance workflows, approval criteria and the boundaries AI is allowed to work inside.
Output
Agreed risk appetite, policy and approval criteria.
Control
Connect governance to the technology environment so controls operate alongside the development, data, infrastructure, cloud and monitoring platforms you already run, from architecture through to release.
Output
Policy running as engineering controls, not paperwork.
Monitor
Track governed state, exceptions and drift continuously in production, with traceability from business intent through to the behaviour users actually see.
Output
A live view of control posture, exceptions and drift.
Govern autonomy
Extend agentic AI through controlled stages rather than in one step, with approval points, guardrails and behaviour testing sized to the risk of each stage.
Output
Autonomy extended at a pace the controls can carry.
Prove
Keep decision traceability across the lifecycle, so every model choice, data access decision and release can be evidenced on request rather than described after the fact.
Output
Audit-ready evidence, produced as a by-product of delivery.
Improve
Evolve the controls as AI, regulation and operating conditions change, so governance keeps pace with adoption instead of lagging a release behind it.
Output
Governance that matures as adoption grows.
Show your working, do not describe your intent
Regulated organisations are not judged on the quality of their AI policy. They are judged on whether they can evidence what happened. InfraTech maps the governance evidence trail against the frameworks that apply to you.
- EU AI Act. Classification of high-risk AI systems under Article 6 and the obligations that follow from it.
- ISO/IEC 42001. The AI management system standard. We design controls and evidence to align with it. We do not issue certification, and neither does any consultancy.
- NIST AI Risk Management Framework. Govern, map, measure and manage, applied as an operating structure rather than a reading list.
- FCA AI governance guidance. Accountability, oversight and consumer outcome expectations for financial services.
- NHS DTAC. The Digital Technology Assessment Criteria applied to clinical and administrative digital tools.
Knowing which regime applies, and where the boundaries sit, is part of the work. Blurring them is exactly what an informed buyer notices.
Governance you can see, not governance you assert
InfraTech AI Governance is powered by Purple Cortex, an enterprise AI execution control plane built to bridge governance strategy and technology execution. It does not replace your existing platforms. It operates as an overlay, connecting governance through APIs and policy with data platforms, model infrastructure, DevOps tooling, IDE environments and your own AI teams.
Role-based access, trust boundary enforcement, data ownership mapping and an immutable audit store, deployed as SaaS, in your VPC, on premises or air-gapped. Govern what you already use.
- 01
Discovery and Context Intelligence
Capture topology, ownership, trust boundaries and adoption context, so governance starts from what is actually running rather than from an architecture diagram that was accurate two years ago. - 02
Decision and Governance Intelligence
Translate intent into risk classification, policy, approvals and constraints, with the decision record captured as the decision is made rather than reconstructed afterwards. - 03
Architecture Synthesis
Convert approved intent into a validated, build-ready architecture, so what gets built is the thing that was actually approved. - 04
Execution Orchestration
Connect governance with build, progressive release and operational workflow, through your existing CI/CD pipelines, source control, cloud environments and monitoring platforms. - 05
Control, State and Continuous Learning
Capture system state, detect drift and hold a closed loop between what was approved and what is running, so the governance model improves as adoption changes.
Governance built into delivery
- We start with what is actually running. Discovery before policy, because a control written for a system nobody uses protects nothing.
- Advisory-led. Governance operating model, policy design and risk alignment shaped around your organisation, not lifted from a template.
- We engineer controls, we do not just document them. Governance requirements become workflow rules, approval points and evidence capture.
- Lifecycle-based. Governance spanning ideation, assessment, design, build, release, operations and continuous improvement, rather than a gate at the end.
- We keep the evidence as we go. Traceability is a by-product of how the work is done, not a project someone runs before an audit.
- Framework-aware. Designed to support alignment with the EU AI Act, ISO/IEC 42001 and the NIST AI RMF.
- We treat governance as an enabler. Visible risk is manageable risk, and manageable risk is what lets an organisation say yes to AI faster.
- We work with your existing estate. No wholesale replacement of your security, risk or service management tooling.
AI governance FAQs
What is InfraTech AI Governance?
A service for establishing and operating governance across the AI lifecycle. It covers understanding your AI landscape, managing AI risk and policy, connecting governance with AI delivery, continuously monitoring governed state and maintaining traceability as AI evolves.
Is this an AI governance platform?
InfraTech delivers AI governance as a service, combining advisory support, operating model design and technology-enabled controls. The control plane capability behind it is Purple Cortex, described in its own section above.
Can InfraTech help us build an AI registry?
Yes. A governed inventory and enterprise context for AI use cases and systems, covering ownership, classification, organisational boundaries, adoption mapping and baseline state.
How does AI risk management work?
It translates AI intent into a risk register, policy controls, governance constraints, approval criteria and decision records, all settled before execution proceeds rather than reviewed after it.
How is continuous AI governance different from a periodic assessment?
A periodic assessment evaluates a system at a point in time. Continuous governance monitors state over time, compares it with a baseline, detects drift and triggers impact analysis or review when runtime deviations occur.
Can InfraTech support agent governance?
Yes, through defined operating modes, human oversight, risk-based escalation and controlled movement between autonomy levels, with predefined kill criteria agreed before an agent is given more room.
Can governance be integrated with development and deployment?
Yes: architecture validation, build validation, CI/CD workflows, progressive rollout, rollback protocols and release controls, with integrations to IDEs, source control, cloud environments and monitoring.
How does InfraTech support audit readiness?
By linking governance decisions, ownership, controls, changes, impact analysis and evidence records across the AI lifecycle, so the trail already exists when the question is asked.
Do you guarantee compliance with specific AI regulations or standards?
No. We help establish the controls, evidence and operating model needed to support alignment with frameworks such as the EU AI Act, ISO/IEC 42001 and the NIST AI RMF. Regulation-specific assurance or certification is a separate engagement, agreed separately.
Does the service replace our existing AI, data or DevOps platforms?
No. It works with the enterprise systems you already have, connecting through APIs and policy rather than asking you to migrate first.
Start with visibility, then control, then evidence
InfraTech can help you establish the governance foundations, operational controls and evidence model needed to scale AI responsibly. Start with AI visibility, move to risk and policy control, embed governance into delivery, then monitor, evidence and improve as adoption grows.
Where this sits: AI Transformation Advisory establishes enterprise reality and prioritises the right opportunity. Digital Engineering designs, builds, modernises and integrates the solution. AI Operations as a Service runs, monitors and continuously improves it in production. AI Governance maintains control, accountability, traceability and evidence across every stage.
