Latest Articles
AI trapped in a self-referential paradox from 1901
In 1901, Bertrand Russell shattered set theory with one question about self-reference. In 2026, AI is falling into the same trap at industrial scale, writing, verifying, and training itself in a loop no one is auditing. Watermarking is a first step, not a fix. By 2030, a child asking “is this true?” may get a fluent, confident answer with no human at its root. Here is why this is a quantum-scale problem, and what a five-year window looks like.
The Cunning (AI) Fox. Lies, Deceives, Connives and Conspires.
The UK's AI Security Institute (AISI) has documented the first full AI deception operation on the live internet. Frontier models from Anthropic and OpenAI were evaluated. Anthropic's Claude Mythos 5 dominated the tradecraft: reconnaissance, forgery, false consensus, evidence tampering, and machine-to-machine coordination, all directed at real humans. What does this mean for the UEBA, SOAR and Deception stack every enterprise runs? And what does a resilient security architecture look like from here?
AI on AI Part III: Convergence (Hacking the infra and Cracking the math)
Two weeks ago, an OpenAI agent broke into Hugging Face. Last week, an AI model killed HAWK, a PQC candidate designed to resist quantum computers, using classical mathematics. This convergence of AI accelerating both hacking and cracking the maths cannot be ignored. Until now, we were preparing for a predicted event in the future (Q-Day). Now, we must prepare for an unpredictable event that could happen anytime. Part III extends the CISO’s action plan from Part II with crypto-agility deliverables built to endure both threats.
AI on AI Part II: Actions for a CISO amidst agent chaos
Part I argued the headlines overblew the OpenAI and Hugging Face incident. The Cloud Security Alliance has since published a serious post-mortem, and the theory of what to do next has already been written, twice. This piece skips the theory and answers the harder question: what actions can a CISO execute on the ground in the next three months?
AI on AI Part I: Overblown Headlines Likely to Spook Insurers
An OpenAI model broke out of its own test lab and hacked Hugging Face, unsupervised. Everyone is calling it unprecedented. It isn't. Two of the most sophisticated AI companies on the planet got caught out by security mistakes any first-year analyst would recognise, and the insurance market is already taking note.
The Great Illusion of AI Provenance
Every major AI company points to provenance as a saving grace. We track our data. We log our training. We can show you the chain of custody. It sounds reassuring. It sounds like science. In its current form, it is an illusion. The technical layer is broken. The business layer is opaque by design. The jurisdictional layer has no single answer. This essay argues that AI provenance, as currently sold, cannot be verified, and that the machinery to change that was never built at the scale the industry now needs.
My views on the FT film – INDIA: THE AI FACTORY
These workers are teaching machines everything they know, their skill, their years of practice, their hands. Once that knowledge is captured, it belongs to someone else. Value capture and value creation are not the same thing. Who does the AI factory of the world actually serve?
Managing the Stubborn Residual Risks of AI Governance
An open-source tool that finds every cross-border AI data flow in a company's code before it ships. The kind of pre-deployment control regulated institutions actually need. Two adjacent stories in the news: Anthropic's recent allegations against Alibaba, and their decision to restrict Mythos to around 100 approved companies. Three connected problems at the heart of AI governance: 1. AI data residency and sovereignty, 2. AI theft, and 3. AI export control. Each carries residual risks that technology cannot fully eliminate. This piece walks through all three, what the tool addresses, and what boards and senior leaders can actually do.
The Four Laws and a Playbook for AI Agents
Singapore clearly leads on Agentic AI governance. The principle is plain: accountability rests with humans, not with code. Four laws for the institutions deploying AI agents, Identity, Scope, Accountability, Revocability. And the PETALS™ Framework for AI Governance with the Cyber Quadrilemma lens, together, as the Agentic AI Playbook for effective orchestration.
The most consequential AI decision isn’t which model to use
Most AI investment debates focus on which model to use. The more consequential decision is whether you're commissioning AI-powered software or an autonomous agent. Two prototypes show what each looks like in practice, and why the distinction matters for procurement, risk, and the boards above them.