AI Consultancy, Governance, Security & Implementation

AI governance and regulatory requirements

For the full scope, deliverables and process, see our AI governance consultancy page. Understand the AI tools and use cases in your organisation, including unapproved or unmanaged use. Define technical ownership, permitted information, oversight and evidence around the requirements your responsible teams have confirmed.

Where your responsible teams confirm that the EU AI Act or data-protection law applies to your role and jurisdiction, we implement the supporting IT controls and evidence. ISO/IEC 42001 and the NIST AI Risk Management Framework can be used as governance and risk references where agreed. Cloud Agile is not ISO/IEC 42001 certified, a certification body, a legal adviser or a regulator-approved assessor. Independent certification and your specialists' assessments remain separate.

• AI tool and use-case inventory, including unmanaged use.
• Use-case reviews and an AI risk and control register.
• Technical input to policies and assessments.
• Supplier and model information for each approved tool.
• Human oversight and decisions requiring client approval.

AI readiness and practical best practice

Choose use cases worth implementing. Review data quality, information access, existing processes, dependencies and the people who will use the tools. Agree a pilot, acceptance criteria and a way to measure the result.

We measure outcomes against the criteria you agree. We do not promise a return on investment.

• Suitable use cases and information preparation.
• Ownership and approved tools.
• Human review and documented decisions.
• Realistic adoption planning.

AI security and information protection

Protect the information and systems involved in AI. Review permissions, identities, supplier processing, integrations and the actions a tool or agent can perform. Implement the agreed safeguards and test them against the defined risks.

We use OWASP's published AI security material as a reference for applicable technical risks. It is a reference, not a certification.

• Least privilege and permission reviews.
• Data-loss prevention and sensitive-information handling.
• Prompt-injection and unsafe-output risks.
• Excessive agent permissions and insecure integrations.
• Logging and incident escalation where relevant.

AI implementation and integration

Deliver the agreed solution, from configured business tools to workflows, integrations and agents. We set up the technical controls, test the work and document how the service will operate.

We work with Microsoft Copilot, Power Automate and agents built on your Microsoft environment. Cloud Agile can implement the agreed work, so you do not need to find a separate IT implementer.

• Pilot and configuration.
• Integration with your existing systems.
• Testing and approval.
• Deployment and handover.

Adoption, evaluation and human oversight

Help people use the tools appropriately. Provide practical guidance, review quality and exceptions, and agree where a person must check or approve an output or action.

Your specialists own HR, professional, clinical, privacy and other specialist decisions within their roles.

• User enablement and documented usage.
• Output evaluation and human review.
• Escalation routes and acceptance evidence.

AI maintenance and continual improvement

AI needs attention after launch. Maintain the agreed configurations, integrations and access controls, review changes to tools or models, and monitor the measures that show whether the service remains useful and controlled.

We define what is maintained and by whom. Ongoing support can be a scoped AI engagement or part of co-managed services. Service hours, monitoring and human response are agreed explicitly.

• Approved updates and model or provider changes.
• Regression checks and output quality.
• Permission reviews and security findings.
• Cost and usage, documentation, incidents and improvement actions.

From a defined project to an ongoing partnership

A consultancy review can establish priorities. An implementation project delivers the agreed changes. Ongoing maintenance or co-managed support keeps the selected responsibilities operating. These can be separate or connected engagements.

What an engagement can deliver

Deliverables depend on the agreed scope.

• A defined AI scope, use-case inventory and technical responsibilities.
• A readiness and risk review with a prioritised control plan.
• Agreed configurations, integrations or deployed workflows.
• Test and evaluation results and implementation evidence.
• Technical guidance, handover and support ownership.
• A review and maintenance plan where included.