Ai Governance

Why frontier AI labs restrict dangerous dual-use biology, how layered safeguards and responsible scaling work, and what security leaders should know.

How frontier AI labs restrict weapons and CBRN uplift: dual-use risk, export controls, responsible scaling, and layered safeguards for security leaders.

How AI labs detect AI misuse with monitoring, classifiers and threat intelligence, plus the AI trust and safety lessons CISOs can apply to their own AI.

A CISO guide to the OWASP MCP Governance & Risk Project: the four non-negotiable gates (owner, logging, scope, review), the Tier 0-4 classification, the eight-factor risk model, and how it maps to the OWASP MCP Top 10, LLM Top 10, NIST AI RMF, ISO 42001 and SOC 2. Plus a free tool that runs the check for a specific server.

A CISO guide to abliterated models: how "abliteration" surgically removes an LLM's refusal behaviour without retraining, why refusal-removed offensive-security releases such as GLM-5.3-CYBERSECURITY-FP8 are spreading on Hugging Face, where the real risk sits (shadow AI on laptops and agents with tools), how to detect them in your environment, and a free calculator that scores the risk of a specific deployment.

An AIBOM is an SBOM for AI systems — a machine-readable inventory of every model, dataset, and dependency in a product, plus each one’s license and provenance. Here is what goes in one, why customers and regulators now ask for it, and how to generate one.