Safety & Governance
The hard problems: red teaming, bias, interpretability, alignment, and the governance frameworks that might actually matter. No hand-waving.
Deep Dives and Frameworks
Implementation playbooks, operator patterns, and durable analysis.
Signals, Maps, and Watch Lists
Production-oriented analysis, benchmarks, and market/system intelligence.
External tools
Execution tooling is separate
Swarm Signal keeps the analysis layer. Use BoredTools for reusable production templates and trackers.
Red Teams Found Agents Leak More Than Models
Red teams found agents are far more vulnerable than standalone models. Mixed attack strategies hit 84.3% success rates. Memory poisoning persists across sessions. Every tool is a potential exfiltration path.
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.