AI GOVERNANCE
AI Governance & Assurance
Find out what AI is already running in your organisation, what it exposes, and what has to be true before the next system goes live.
AI governance is usually written as policy and then contradicted by what is already happening. Staff have tooling nobody approved, company data is leaving through browser extensions, and the systems that are formally approved have no measurement after launch. This work starts with evidence rather than a framework.
What an engagement produces
- A findings report with severity, evidence and owner for each item
- An inventory of AI usage discovered during assessment
- A remediation sequence ordered by exposure, not by ease
- Draft policy and approval-path documents your teams can adopt
- A monitoring specification for systems already in production
Capabilities
What this covers.
- AI readiness assessment
- A structured review of where AI would actually help, what data would have to move for it to work, and which of those moves your current controls permit. The output frequently rules things out, which is the point.
- Shadow AI and data-leakage assessment
- Where company data is already being sent, through which tools, by which parts of the business, and what it exposes. Assessed from egress and identity evidence rather than from a staff survey.
- Governance frameworks
- Approval paths, named ownership, acceptable-use boundaries and model-selection criteria — written to be enforceable by the people who have to enforce them, and short enough to be read.
- Model and application evaluation
- Independent assessment of an AI system already in use or about to launch: retrieval quality, grounding, prompt-injection exposure, permission leakage, and behaviour at the edges of its intended scope.
- Monitoring and operational controls
- What gets measured after go-live, what threshold triggers a human, who receives the alert, and how a bad release is rolled back. Governance that stops at launch is documentation, not control.
What this is not
Xcelerates is not a certification body and does not issue regulatory attestations, audit opinions or compliance certificates. This is engineering assessment. It produces evidence and remediation your legal, risk and compliance functions can act on — and it is designed to be handed to them.
Related
AI Engineering
Generative AI, agents and retrieval systems engineered to run in production — with evaluation, cost control and failure behaviour designed in rather than added after launch.
Cloud & Platform
Cloud architecture, delivery pipelines, reliability and security-aware engineering — so what gets built can be operated by the people who inherit it.
Next step
Tell us what has to work.
Describe the problem in your own words — the system, the constraint, the thing that keeps not shipping. A senior engineer reads every enquiry and replies with a view rather than a brochure.
What happens next
A reply from an engineer
From someone who could scope the work. Not an automated sequence.
A conversation, not a pitch
Thirty to forty-five minutes on the problem and the constraints.
A written position
What we would do, what it would take, and whether we are right for it.