Practitioner Insights on
AI, Security & Delivery

A comprehensive practitioner guide to ISO 42001 — the international standard for AI Management Systems. Covers requirements, implementation roadmap, gap assessment and certification readiness.

A practitioner's guide to designing delivery frameworks that scale with organisational ambition — governance structures, delivery rhythm, risk management and team empowerment.

How to implement Zero Trust principles across Azure, AWS and GCP while maintaining enterprise GRC compliance — identity, segmentation and continuous verification.

A comprehensive side-by-side comparison of the three leading AI governance frameworks — helping organisations select the right approach for their context, industry and risk tolerance.

A complete roadmap for building a career in AI governance, risk and compliance — covering roles, salary bands, required skills, certifications and career transition pathways.

How GDPR applies to AI systems, automated decision-making, LLMs and generative AI — the essential compliance guide for Data Protection Officers navigating the AI landscape.

The dual relationship between AI and cybersecurity — using AI for threat detection and defence while securing AI systems against adversarial attacks and compromise.

How AI is reshaping modern cyber warfare — autonomous offensive capabilities, AI-powered defence systems, and the geopolitical implications for national security.

How AI is fundamentally transforming the IT service integration industry — what firms must change, new delivery models, and strategic positioning for the AI era.

A rigorous, evidence-based assessment of the existential risks posed by Artificial Superintelligence — separating credible safety concerns from speculation and hype.

Is the AI investment boom a speculative bubble or the bedrock infrastructure of the next technological era? A data-driven assessment of hype cycle position and long-term value.

Practical frameworks for human-AI collaboration — how AI augments rather than replaces human capability across knowledge work, creative work and complex decision-making.

A comprehensive adversary threat reference covering every known method to breach, manipulate and compromise AI systems in production — from adversarial attacks to supply chain exploits.

Profiling the top 10 companies in each of five AI industry segments — products, research, hardware, services and integration — with market data and competitive analysis.

A comprehensive guide to AI bias — 14 bias types, documented case studies, fairness metrics, mitigation frameworks and governance requirements for responsible AI deployment.

A reflective, practical guide to cultivating genuine love, care and empathy — the habits, mindsets and daily practices that build authentic character in life and leadership.

All 10 phases of the AI development lifecycle — business problem definition, data engineering, model training, evaluation, deployment, monitoring and governance frameworks.

The AI supply chain is not simply a new version of the software supply chain. It is categorically more complex, more interdependent, and more vulnerable to attack vectors that have no equivalent in traditional software security. A conventional application depends on libraries and infrastructure.

Both the Axios and LiteLLM attacks hinged on compromised maintainer accounts. In Axios's case, the primary maintainer's npm credentials were stolen. In LiteLLM's case, the PyPI publishing token was exfiltrated from a CI/CD environment. In neither case was the application code itself vulnerable — the compromise happened at the identity layer, not the application layer. The software was clean; the publishing mechanism was not. This is a fundamental shift in the threat model for open-source security: the question is no longer "is this code safe?" but "was this code published by who we think it was, through a process we can verify?"

The US National Security Agency (NSA), in its March 2026 joint publication on AI and machine learning supply chain risks, formally recognised this complexity by framing AI as a layered supply chain in its own right — one where data, models, software, infrastructure, hardware, and third-party services are interconnected components that all influence the confidentiality, integrity, and availability of the resulting AI system.