Human accountability
AI systems should have a named owner, clear decision boundaries and escalation paths.
Trust centre
Trust, security and responsible AI.
The consultancy should earn confidence through verified credentials, visible methodology, technical knowledge, human oversight and transparent limits.
Responsible AI
Operational principles, not vague ethics slogans.
Privacy by design
Projects should minimise sensitive data exposure before model or vendor selection.
Security by design
Authentication, authorisation, secrets, logging and tool permissions are considered early.
Proportionate automation
Autonomous action should match the risk, reversibility, sensitivity and business impact.
Testing before deployment
Useful AI is evaluated against real tasks, failure modes and acceptance criteria.
Monitoring after deployment
Prompts, retrieval, workflows, guardrails and user experience should improve from evidence.
Delivery framework
Discover, assess, design, prototype, govern, deploy, measure, improve.
Not every project follows the same path, but every serious AI project needs clear decisions about value, risk, data, testing and ownership.
Discover
Understand the business problem, users, workflows, existing systems, available data and desired outcome.
Assess
Evaluate feasibility, AI suitability, data availability, security, privacy, integration complexity, expected value and risk.
Design
Define architecture, model strategy, tools, integrations, permissions, observability, human approval and success metrics.
Prototype
Build the smallest useful implementation capable of validating the hypothesis.
Govern
Define access controls, data handling, oversight, guardrails, audit requirements, escalation and acceptable use.
Deploy
Move to production with appropriate testing, operational controls and handover.
Measure
Compare actual outcomes against agreed success metrics.
Improve
Use evidence to improve prompts, models, retrieval, workflows, UX and guardrails.
Human oversight
Autonomy should be proportionate to risk.
Autonomous action should depend on reversibility, sensitivity, permissions and business impact. High-risk actions need human review.
- 1Request
- 2AI agent
- 3Retrieve, reason, prepare
- 4Risk and confidence check
- 5Human approval where required
- 6Action
- 7Audit and monitor
Security and assurance
Development principles are not fake client controls.
These are principles considered during AI project design. Specific controls are claimed for a client implementation only when they are actually implemented and verified.
AuthenticationAuthorisationLeast privilegeSensitive informationPrompt injectionTool permissionsSecrets managementAudit trailsHuman approvalData retentionThird-party riskRate limitingOutput validationMonitoringIncident response
Credentials
Verified and approved credentials only.
Courses and credentials are shown with exact titles. They do not imply vendor partnership, endorsement or official consultant status.
AI Foundations
OpenAI, issued 2026-08
Applied AI Foundations
OpenAI, issued 2026-08
Agents and Workflows
OpenAI, issued 2026-08
- AI Agents
- AI for Compliance Workflows
Transparency
When AI is not appropriate, the answer should be no.
Deterministic software, conventional automation or a rules engine can be the better recommendation when data quality is weak, risk is too high, cost exceeds likely savings or human judgement must dominate.
Discuss a responsible AI project