Research, field notes and product news from the team building the end-to-end AI trust platform.
The line between human language and programming languages is blurring as AI agents interpret plain-language instructions as executable code. This shift democratizes…
Agentic AI acts like an employee—making decisions, writing code, and taking actions—but without identity, oversight, or IAM policies. Traditional security built for…
Questionnaire-based vendor diligence and self-attestations no longer provide real assurance in the AI era. Evidence-based, continuous technical assessment and AI Bills of…
Unlike a traditional firewall that polices IP packets, an LLM firewall inspects natural language—the prompts users send and the responses models return.…
Public LLMs offer baseline safety, but their built-in safeguards leave real gaps in security, privacy, and compliance. A dedicated AI firewall adds…
AI agents now make decisions and take actions with growing autonomy, introducing real but often invisible risk. Without AI-native oversight, enterprises are…
Agentic AI can perceive, reason, and act autonomously, but that autonomy fails when an agent’s actions diverge from human intent. Closing this…
The NIST AI Risk Management Framework applies to vendor systems as much as your own. Learn how to extend Govern, Map, Measure,…
AI agents bring both opportunity and unique risk, and most cybersecurity programs have gaps they can't see. This framework maps AI agent…
Regex is transparent but brittle, LLMs are context-aware but non-deterministic, and Transformers stay powerful yet opaque. Cranium's deterministic semantic labeling combines their…
See how Cranium helps your organization accelerate the secure adoption of AI — from your first model to your entire agentic supply chain.
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