One Platform to Secure your AI Stack

An unapproved coding agent runs on a developer machine with access to a code repository containing cloud credentials in the project root. Pillar flags it before any data leaves the endpoint.

A model outside the org's approved list reaches a production environment. Pillar identifies it and blocks inference until it clears the approval workflow.

An agent connects to an MCP server not on the organization's allowlist. Pillar surfaces the unauthorized connection and triggers a policy violation alert.

A coding agent skill contains shell execution patterns with network egress capabilities, enabling unrestricted command execution in production. Pillar detects the dangerous patterns in the tool definition before the skill reaches runtime.

An indirect injection in a support chat agent pivots through the Search tool to execute unauthorized queries against a production database. Pillar traces the injection across tool invocations and blocks execution.

An agent holds write access to production code repositories with no human-in-the-loop confirmation gate. Pillar scores it as a high-risk excessive agency violation and flags it for remediation.

Taint analysis detects a customer-facing agent reading PII from a production database, serializing it, and posting it to an external webhook. Pillar captures the full egress path from source query to destination payload.

An MCP server returns an API key inside an error message response. Pillar intercepts it before it reaches an external log or client.

A container running an AI model holds wildcard cloud storage permissions on a bucket with customer PII and financial records. Pillar scores the permission combination as a critical exposure.

A publicly accessible LLM proxy instance runs with no authentication and full access to internal APIs. Pillar detects the open endpoint during infrastructure scanning and flags it as a critical external exposure.

A model uses an unsafe serialization format and originates from an unverified publisher. Pillar blocks deployment after scanning model provenance against your approved list.

A coding agent downloads a compromised package and uses write access to production code repositories to push malicious code. Pillar detects the multi-step chain at the library download stage.

Most red teaming tests models in isolation, missing the tools, data connections, and multi-agent workflows that create real attack paths in production. RedGraph maps your live AI environment as an attack graph, exposing exploitable paths across the full system that model-only testing never reaches.

A support chat agent connects to a Search tool, which pivots to an internal project management system. Pillar discovers the lateral movement path and scores the blast radius before attackers reach it.

A compromised agent propagates malicious instructions to downstream agents in an orchestration chain. Pillar catches the instruction at the first hop before it propagates.

An agent chains tool calls across file system access, network egress, and a cloud storage API in a single session, moving data outside your environment without any single action triggering a threshold. Pillar tracks the full tool invocation sequence and flags the chain as a multi-step exfiltration pattern.

A compromised agent in an orchestration chain passes malicious instructions to downstream agents, propagating unauthorized behavior across the workflow. Pillar intercepts the poisoned instruction at the first handoff before it reaches dependent agents.

An agent inherits broad organizational permissions from its maker's identity and operates without scoped access controls. Pillar detects the delegated identity mismatch and flags the agent as operating outside its intended permission boundary.