Notes on trustworthy AI.
Practical writing on building generative AI you can defend — security red-teaming, the EU AI Act, governance and the gap between a demo and production.
How to ensure trustworthy GenAI in the enterprise
Trust isn't a feature you switch on at the end. Here are the seven pillars — and the testing discipline — that turn a promising pilot into AI your customers, auditors and regulators can rely on.
Read the article →Four frameworks that make threat modeling actually work
DREAD, Trike, OCTAVE and PASTA each solve a different problem. Together they give you a threat model your engineers, your CISO and your board all find credible.
Read article →How to ensure trustworthy GenAI in the enterprise
The seven pillars of enterprise trust — accuracy, security, privacy, governance, transparency, oversight and continuous assurance — and how to prove each one.
Read article →The state of GenAI safety
Why generative AI fails differently from traditional software, where it fails most, and what credible assurance actually requires.
Read article →EU AI Act readiness
What the Act asks of high-risk and GPAI systems, the timeline that matters, and how to build a defensible posture before enforcement bites.
Read guide →AI & Cybersecurity Compliance Check
An interactive self-assessment that scores your readiness against the EU AI Act, OWASP LLM Top 10, NIST AI RMF, ISO 42001, GDPR, NIS2 and DORA.
Run the check →