Software and architecture delivery experience
Governance for Sensitive Sectors
AI governance and auditing for health, research and education environments.
I provide independent AI governance and assurance support for organisations that need practical controls, clear accountability, and reliable outcomes in high-trust settings.
This includes policy and ownership models, AI behavior audits, monitoring frameworks, privacy-aware controls, and integration assurance across legacy and modern platforms.
Start governance early to prevent quality, safety, privacy, and compliance issues from scaling with your rollout.
Audit what AI actually does under real conditions
Unbiased advice before major vendor commitments
Governance that fits existing systems and operations
Governance Services
Capability ModelAI Governance Strategy
- Policy and control framework design
- Ownership, approvals, and escalation pathways
- Operational governance aligned to business risk
AI Behavior Auditing
- Output quality and consistency checks
- Scenario testing for hallucination and edge cases
- Traceability review for high-impact decisions
Monitoring & Drift Controls
- KPIs, thresholds, and alert design
- Drift detection and review cadence
- Evidence packs for operational reporting
Human Oversight Design
- Approval gates for high-impact workflows
- Exception handling and incident response
- Clear accountability and rollback controls
Data, Privacy & Access Controls
- Data handling and retention controls
- Access and permission reviews
- Prompt and source-governance standards
Vendor & Toolchain Due Diligence
- Third-party provider risk assessment
- Lock-in and continuity risk review
- Portability and contract risk guidance
Governance programs are tailored for health, research, education, SME, and enterprise contexts so controls are practical, not theoretical.
Governance Approach
Execution PathwayBaseline
Assess current AI workflows, integration dependencies, risk exposure, and control maturity.
Implement Controls
Define governance ownership, quality thresholds, human oversight, and monitoring routines.
Audit & Improve
Run assurance cycles, test outcomes, and continuously improve controls as AI scope expands.
Business Benefits
Practical ImpactWhat You Gain
- Lower risk exposure in customer-facing and operational workflows
- Clear evidence for executive, board, and procurement stakeholders
- Higher trust in AI-assisted decisions and automation outcomes
- Stronger continuity as AI usage expands across teams
Who This Supports
- Health services introducing AI into care support, administration, or operational workflows
- Research groups handling sensitive data, evidence, and repeatable analysis workflows
- Education providers introducing AI into learning, support, and knowledge systems
- Enterprise programs needing auditable controls across complex legacy environments
Legacy Integration Assurance
Integration Risk ControlWhy Legacy Matters
AI quality depends on the systems and data it touches. I validate integration boundaries, dependencies, and fail-safe pathways to prevent fragile outcomes in production.
- Boundary and dependency testing across connected platforms
- Data contract validation and schema drift checks
- Fallback and recovery patterns for operational resilience
Common Risks Addressed
- Undocumented legacy rules creating output inconsistencies
- Connector permissions and access-control exposure
- Fragile integrations causing cascading failures
- Limited observability when AI quality drops unexpectedly
Book a Governance Strategy Session
Independent AdvisoryWe will review your AI use cases, risk posture, sector obligations, and integration complexity, then define a practical governance and audit plan aligned to your operating environment.
After the first discussion, you will have clear next-step options, key risks, and a practical implementation path. I typically respond within one business day.