Safety & Governance
The hard problems: red teaming, bias, interpretability, alignment, and the governance frameworks that might actually matter. No hand-waving.
Guides and explainers
Detailed guides and practical technical analysis.
Latest analysis
Recent research, benchmark reviews and technical updates.
Practical tools
Templates for budgets and project planning
BoredTools offers practical spreadsheets for budgets, freelance work and small projects.
Red Teaming AI Agents: A Practitioner's Guide
Red teaming AI agents is fundamentally different from red teaming standalone models. Agents have tools, memory, and credentials — each a new attack surface. This guide covers the OWASP agentic framework and a structured testing methodology.
EU AI Act vs US vs UK: Global AI Regulation Compared
Compare EU AI Act, US, and UK AI regulation on compliance, penalties, timelines, and impact on developers.
AI Safety Frameworks for Regulated Industries: Healthcare, Finance, and Government
Regulated industries face roughly three times the compliance burden of unregulated AI deployments. This guide maps the actual frameworks, enforcement timelines, and compliance costs for AI safety across healthcare, finance, and government in 2026.
Best AI Red-Teaming and Safety Testing Tools 2026
Your AI system will get attacked. The question is whether you find the vulnerabilities first or your users do. 8 red-teaming tools tested and compared.
EU AI Act vs US Executive Order vs UK AI Safety: Global Regulation Compared
EU AI Act, US executive orders, UK AI Safety, and China's algorithm rules compared side by side. What each means for your AI deployment.
Multi-Agent AI Has a Security Architecture Problem That Better Models Won't Fix
193 documented threats. Agent defection. Reverse SSH tunnels. Why better models won't fix multi-agent AI security — and what actually helps.
Alignment Works in English. In Japanese, It Backfires.
A new study shows the same alignment intervention that produces strong safety effects in English reverses direction in Japanese, increasing harmful outputs. Tested across 1,584 simulations, 16 languages, and three model families.
One Fake Source Broke Every Agent
A single misinformation article injected into search rankings crashed GPT-5's accuracy from 65.1% to 18.2%. The agents had unlimited access to truthful sources and couldn't be bothered to look.
Washington's $42 Billion AI Shakedown
The Trump administration is using $42 billion in broadband funding to pressure states into repealing AI laws. The FTC has been directed to classify bias mitigation as a deceptive trade practice. Meanwhile, the EU enforces the opposite.
We Built the Agent Internet Before Its Firewalls
Three CVEs in Anthropic's own MCP reference server. Over 8,000 production servers exposed to the internet. The protocol powering AI agents shipped without security, and the industry is paying for it.