Enterprise AI Governance & Safety Advisory
Most AI governance fails before the engineering begins. I work across the full distance - from board risk appetite to production guardrails and evals.
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The Problem
"You can't run your business on AI that's governed like a side hustle."
AI governance fails in a predictable place. Not in the boardroom, where frameworks get approved. Not in the risk committee, where they get celebrated. It fails when it meets the reality of AI development - where documents full of "shoulds" meet a team that deals in specifics and a technology that evolves daily.
There should be guardrails. Models should be evaluated. Agents should be catalogued. Data provenance should be maintained.
Should is not a control. It never will be.
And the development team, left to interpret vague mandates disconnected from technical reality, will make their own decisions. The framework joins the graveyard of well-intentioned slides. The governance gap widens.
87% of executives claim to have clear AI governance frameworks — but fewer than 25% have fully implemented the technical tools to manage risks.
Source: IBM Newsroom
Risk triage, use-case funnels, and leadership upskilling that treats AI as a regulated business asset - not a technology experiment run by whoever raised their hand first.
Most engagements deliver the policy and leave. I stay in the room with the engineers - translating governance requirements into specific, implementable controls for your actual stack. Your pipelines, your agent orchestration, your evaluation infrastructure.
Autonomous multi-agent systems don't wait for governance to catch up. Decision-traceability, red-teaming, observability, and constraint architecture for AI that acts without a human in the loop.
15+
Years in AI/ML
10+
Years AI Governance & Safety
30+
Enterprise Engagements
About
I've spent twenty years at the intersection of technical AI and organisational reality - from early ML research and enterprise AI agent deployment, through large-scale global change programs at Shell and Philips, to shaping the IEEE standards, OWASP membership, and AI Safety research that will define AI governance for the decade ahead.
"I help executives turn a vision into reliable assets, and engineers turn experiments into trusted systems. ."
That's what makes this work. A risk taxonomy that maps to your actual technology. Governance requirements that your engineers can implement without interpretation. Board assurance that is technically honest, and a board who are confident in having those conversations.
At SingularityNET I built safety frameworks for frontier AGI systems. At nib I'm operationalising ISO 42001 and NIST AI RMF through governance boards, LLM evaluation pipelines, and agentic observability infrastructure - sitting with engineers to work through exactly how guardrails land in their stack, and sitting with the risk committee to explain what that means in terms they can act on.
Expertise
A framework built across twenty years at the intersection of technical depth and organisational reality. Designed to span the full distance - from board-level risk strategy to the engineering controls that make governance real.
Converting AI complexity into decisions your leadership team can own. Every recommendation traces to P&L impact, risk reduction, or regulatory positioning — not just technical compliance.
IEEE- and ISO-standards-based frameworks that survive an audit committee meeting. Not governance theatre — systems your regulators, board, and legal team can rely on when they need to.
From neural network research through frontier AGI safety work. Evaluation pipelines, red-teaming, and guardrails that scale from your first LLM deployment to autonomous multi-agent systems operating without human oversight.
Governance frameworks fail when the organisation doesn't own them. I embed capability — training leaders, building governance boards, and running the change programs that make AI risk management a durable operating practice, not a project that ends with the final slide deck.
Impact
Right now, somewhere in your organisation, someone is deploying an AI model that your risk team hasn't evaluated. An engineer is making a governance decision by default because nobody could articulate real requirements. A vendor is promising "responsible AI" with no mechanism you can audit, and no-one who can challenge their pitch.
The governance gap isn't a future problem. It's already costing you - in regulatory exposure, in audit unreadiness, in decisions you won't be able to explain when you need to and teams too nervous to use AI.
<1%
of organisations have fully operationalised responsible AI
Source: World Economic Forum
<28%
of respondents are confident they can secure AI used in core business operations
Source: Cloud Security Alliance & Google Cloud
>60%
are already using or plan to use agentic AI this year
Source: Cloud Security Alliance & Google Cloud
Insights
I've spent more time than is healthy over the past few months observing something oozing out of the woodwork across multiple online AI communities.
Read more — Echo in the Machine - The Emergence of Group AI PsychosisThe "Stochastic Parrot" observation has always been intriguing to me.
Read more — The Narrow Band: On Models and RealityThere has been a subtle shift in the boardroom conversation about AI.
Read more — An 'AI-Ready' Culture is More Than Just TrainingThe discipline that was supposed to prevent AI harm produced frameworks, principles, and declarations — then largely watched as the industry did whatever it was going to do anyway. The same pattern is repeating in AI governance right now.
Read more — Should Is Not a Control: How AI Ethics Built Its Own GraveyardMost audit committees still treat AI governance as an IT risk. By the time they catch up, the decisions have already been made and the liability has already accrued.
Read more — What Your Audit Committee Should Be Asking About AI Decisions (And Why They're Not)Using large language models to evaluate large language models introduces a class of systematic bias that most evaluation pipelines are not designed to detect.
Read more — The LLM-as-Judge Problem Nobody Is Talking AboutThe gap between policy and practice in enterprise AI is not a technical problem. It is a leadership one.
Read more — Why Most AI Governance Frameworks Fail Before They StartAs autonomous agents proliferate, the risks are no longer hypothetical. They are operational.
Read more — Red-Teaming Multi-Agent Systems: What Boardrooms Must UnderstandA practical look at how international standards translate into audit-ready enterprise posture.
Read more — IEEE P2863: Shaping the International Language of AI AccountabilityEnterprise AI projects rarely fail on the model. They fail on the organisation around it.
Read more — From Pilot Purgatory to Production: The AI Deployment ProblemMLOps pipelines that create genuine accountability require more than logging. They require intent.
Read more — The Inference Audit Trail: Making Every AI Decision AccountableDecision-traceability and hard constraint layers are the new safety-belts of enterprise AI.
Read more — Constraint Architecture for Autonomous AgentsOne of the inherent weaknesses of Large Language Models (LLMs) like GPT-4 is their struggle with grounding in reality.
Read more — To the Curators, Go the Spoils: The Rising Power in Graph-Enhanced AI ModelsThe rapid advancement of Artificial Intelligence (AI) offers businesses of all sizes the opportunity to gain a competitive edge.
Read more — Joining the AI Party: Practical Steps for Smaller BusinessesThe rapid advancement of Artificial Intelligence (AI) and other emerging technologies has created new opportunities and challenges for businesses, requiring them to adapt and evolve.
Read more — Human Resources: Empowering the organisation to make responsible-use decisionsEthical debt in artificial intelligence refers to the accumulated cost of unresolved ethical issues that arise during a system's design, development, and deployment.
Read more — Ethical debt - Rusting the machinery of progressInnovation in technology continues to accelerate, and the advent of quantum computing is undoubtedly one of the most significant developments in recent years.
Read more — Quantum Computing & AI: Making a tricky explanation impossible?Emerging technologies, particularly artificial intelligence (AI), machine learning (ML), and generative AI, present both opportunities and challenges for businesses across industries.
Read more — AI Governance for the BoardGenerative AI offers significant opportunities for business transformation through automating content generation, enhancing creativity, and unlocking new revenue streams.
Read more — Interim Policy for Generative AIThe AI use-cases are mounting up for small and medium enterprises (SMEs).
Read more — SMEs and Start-ups: Use AI and keep the trustThank you for providing to the people of Australia an opportunity to respond to key questions regarding Australia’s AI Strategy in the form of the AI Action Plan Consultation Paper.
Read more — Australia’s AI Action Plan – An Open ResponseThis reality of AI tech adoption in our private and public institutions struggles to fit into frameworks created to promote responsible and ethical use which focus heavily on the development process as the locus to effect positive outcomes.
Read more — Implementing AI Ethics: Complex architecturesMLOps, the big-bet for scaling Machine Learning, promises seamless development through to in-life use of models using automated, DevOps CI/CD workflows, but what does this mean for an AI Ethics discipline that has focused its fire-power on single-shot development projects?
Read more — MLOps - Fast-tracking AI Ethics?Contact
If you're a CRO, CISO, CDO, or board member trying to get ahead of AI risk — rather than catch up to it — I'd like to hear from you.
Whether that's building your first governance framework, hardening an enterprise-scale agentic deployment, stress-testing what your engineers have already built, or preparing your leadership team for what's coming — bring the specific problem. I'll tell you directly whether and how I can help.
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