// INSIGHTS
Practitioner perspectives on AI security, adversarial testing, and governance from the team that builds the attack tools.
Operational technology is built around predictability, availability, safety and long equipment lifecycles. A control system is expected to perform a defined function, within known tolerances, for many years. Changes are controlled because even a technically correct modification can have consequences for production or safety.
READ ARTICLE →AI governance can establish accountability, document controls and authorise deployment. It cannot prove that a generative AI system will preserve the facts entrusted to it. Enterprises now need a second layer: continuous, evidence-based verification of AI outputs
READ ARTICLE →Restricted frontier models — evaluated under controlled conditions by a small number of organizations — exist in a space between research artifact and operational tooling.
READ ARTICLE →Capable Tool, Not God Mode The claim is simple. The claim is wrong. Somewhere between a LinkedIn post and a vendor pitch deck, the narrative solidified: modern AI-powered security scanners can replace a full red team. Minutes, not weeks. Comprehensive, not sampled. Autonomous, not human.
READ ARTICLE →The morning AI goes dark — and you realize the skills were never yours. A sharp look at borrowed competence, cognitive atrophy, and what you actually know.
READ ARTICLE →On Quantum Mechanics, Entropy, and the Only Honest Way to Secure AI.
READ ARTICLE →From CSLE and CybORG to Autonomous Network Agents. Reinforcement learning agents are moving from research ranges into production networks. What CSLE and CybORG have proven in the lab — and what the dual-use reality means for security teams.
READ ARTICLE →Why LLMs confabulate confidently false answers — the failure modes, mitigations, and the post-Transformer architectures emerging to fix it.
READ ARTICLE →The 2023–2024 AI strategy wave is maturing into a 2025–2026 implementation crisis. Here is what went wrong, and what a security-first remediation looks like.
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