RESPONSIBLE AI · ACCOUNTABILITY · AGENTIC AI ASSURANCE

Responsible autonomy. Clear accountability. Verifiable control.

AI governance should enable useful AI transformation without sacrificing human accountability, operational trust or control. I connect organisational leadership, implementation and adoption with the security, privacy, legal and technical expertise required for each use case. Where agentic AI can act on business systems, we assess the boundaries, permissions and evidence needed to keep humans in control.

Our focus and approach

Governance that supports AI adoption and execution

Start with clear business ownership and risk-based decisions. Identify which policies, legal requirements, safety boundaries, reviews and escalation paths are proportionate to the actual application. Embed these safeguards in the implementation plan with relevant technical and risk specialists, rather than making documentation or bureaucracy the end goal.

Agentic AI Assurance & Control Framework (AACF)

Our Agentic AI Assurance & Control Framework (AACF) examines practical questions: What information can an AI agent access? What actions can it take? Who must approve important changes? Could a malicious message trick it into breaking those rules? Can someone later check exactly what it did?

Illustrative example: before an AI assistant can place supplier orders, verify spending limits, human approval rules and records of every action. This is a testing scenario, not a claim about a client system.

Influence → Authority → Effect

An attempted manipulation is not automatically a proven compromise. We distinguish observations, plausible exposures, reproduced vulnerabilities and demonstrated effects, while recording uncertainty and evidence limitations.

Independent evidence and reconstructability

An AI system should not be the only one judging whether its own actions were safe. Keep records that a responsible person or separate system can inspect, make it possible to stop or correct risky actions, and assign a clear human owner for decisions and follow-up.

Focused agentic AI control assessments

For organisations adopting AI agents with tool and data access, AACF is a developing method to organise questions about permissions, trust boundaries, human approval, logs, containment and evidence. A scoped engagement may produce documented observations, prioritised risks, recommended safeguards and explicit limitations. Specialist testing is performed only within agreed authorisation and with appropriate expertise. This is neither a certification nor a claim of exhaustive security verification.

Put proportionate controls around the AI work that matters

Discuss how governance can support a wider AI transformation, or request a focused conversation about an AI-agent control question. We will clarify your business context and involve the relevant specialists where needed.