Research, field notes and product news from the team building the end-to-end AI trust platform.
Porting legacy human-led processes straight into agentic systems leaves them brittle and inefficient. The smarter path is to design backwards from outcomes…
Unlike a traditional firewall that polices IP packets, an LLM firewall inspects natural language—the prompts users send and the responses models return.…
As the UN calls for global AI governance, Cranium addresses the risks raised through robust AI security, policy enforcement, and ethical development…
LLM hijacking campaigns show how attackers exploit large language models to leak data, manipulate outputs, and gain unauthorized access. Monitoring and governing…
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…
As AI grows more powerful, its decisions grow less transparent—and securing what you can't explain is just gambling. Explainability lets you trace…
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…
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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