Post by Symmetry Systems

Everyone in Data+AI security is talking about context graphs. Ask most vendors what one actually is and you'll get boxes and arrows. Here's what most miss: context is not universal. A context graph built into an LLM tracks and supports reasoning across a conversation. A context graph built for AI security tracks what AI can reach, what it's authorized to reach, and what it's actually done to provide insight into what an identity acting on behalf of someone else can do. Same term. Completely different structure, different entities, different purpose. The context required to answer 'what can this agent reach' is different from 'what should be able to done' which is different again from 'has it tried to do something outside of policy.' The structure has to be designed around the questions you're trying to answer. At Symmetry, we believe a security context graph built for Data+AI starts with three inputs: → Permissions — what an identity and any delegated identity can do → Policies — what it should do, expressed as graph views → Telemetry — what it actually did Wire all three to the same Data x Identity structure and you can answer why, not just what. We built ours before it had a name. Here's how it works. https://hubs.ly/Q04fzDW40