AI security insights, research, and product updates from the Humanbound team.
Moderation APIs catch harm and injection attempts but fail to enforce domain-specific policy. A cross-domain evaluation shows why production LLM systems need both moderation and policy reasoning layers.
The security industry is applying shift-left to AI agents. But AI agents aren't deterministic. The gap between testing on deploy and staying secure in production is where risk accumulates.
The AI agent security market is fragmenting into enforcement, identity, and control plane vendors. But the incident data tells a different story: most agents ship without any adversarial testing at all.
Agent security incidents keep sharing one root cause: agents deployed without adversarial testing. Enforcement is needed, but it is phase 3 of a lifecycle most organizations are entering at phase 1.
AI agent security won't be adopted through better dashboards. It'll be adopted when it disappears into existing workflows. Embed adversarial testing into your terminal via MCP - security becomes a conversation, not a context switch.
Why point-in-time audits fail, and what a mature AI security programme actually looks like. A continuous assurance framework addressing visibility, testing, and operations gaps in enterprise GenAI deployments.
Claude Code Security didn’t kill security tooling—but it did signal that frontier AI labs are now security providers, and that our current models don’t cover the behavioral, contextual, and systemic risks of autonomous agents. This is where security needs to go next.
Your CISO dashboard tracks vulnerabilities across infrastructure, apps, and cloud—but may miss the fastest-growing risk surface: Shadow AI. See how it shows up, why legacy tools miss it, and how Humanbound reveals AI use in a governed, continuously monitored registry.
