Pillar Security

Pillar Security is a unified AI security platform that secures the entire AI lifecycle from development to deployment, built on the conviction that securing AI agents takes offense and defense working as one system rather than a stack of point tools. It has four connected pillars. AI Discovery and Posture uses agentless integrations with code repos, data platforms, AI/ML frameworks, IdPs, and endpoints to auto-discover and catalog every AI asset (models, datasets, prompts, tools, MCP servers, coding agents, meta prompts), detect shadow AI, map dependencies, and run static and dynamic AI Security Posture Management across assets and their interconnections. Red Teaming and Attack Surface Exposure is its standout: RedGraph offensive agentic testing (recognized by Gartner), red teaming for tool use that models how chained tool and API calls can be weaponized (revealing vulnerabilities that prompt-based testing cannot), and black-box target-based red teaming where adversarial agents stress-test any accessible AI application with just a URL and credentials. Runtime Guardrails track every tool invocation, decision pattern, and permission across agentic workflows, log every interaction with metadata for audits and threat hunting, and catch issues like an agent inheriting broad permissions from its maker's identity and operating outside its intended boundary. Governance and Compliance enforce AI policies, translate them into technical controls (approved models, licensing, data sovereignty), validate third-party AI before it touches internal data, and generate audit-ready reports. Business context connects all four so intelligence compounds: a red-team finding becomes a policy, the policy becomes a retest that proves it fixed, and runtime telemetry flows back into the asset map and the next round of attack simulations. This closed offense-defense loop is what distinguishes Pillar from the agent-native governance platforms and the model-security incumbents in the same cluster.