Pillar Security is a comprehensive platform designed to discover, govern, and secure AI agents and workflows throughout the entire AI lifecycle. By connecting business context with technical security, it helps organizations maintain visibility and control over their agentic workforce. Pillar addresses the unique security demands of AI, bridging the gap between development and continuous runtime operation to enable safe AI adoption at scale. The platform enables organizations to map their AI attack surface, perform continuous risk assessments, and implement adaptive guardrails that protect against emerging AI-specific threats.

Some of the key features are:

• Complete AI Discovery: Automatically catalogs AI agents, models, prompts, frameworks, tools, MCP servers, and coding agents to identify shadow AI.
• Agentic Red Teaming: Executes multi-turn adversarial attacks against complete AI systems to test for vulnerabilities like tool misuse and permission escalation.
• Adaptive Runtime Protection: Detects malicious intent and enforces guardrails in real-time to monitor agent behavior and block unauthorized actions.
• Supply Chain Governance: Traces dependencies across models and datasets, scanning for vulnerabilities and misconfigurations in the AI supply chain.
• Compliance and Reporting: Automatically maps findings to standards like NIST, EU AI Act, and ISO while generating audit-ready reports.
• Taint Analysis: Traces sensitive data like PII and secrets as they flow through AI interactions to prevent leakage and unauthorized egress.

The platform operates by integrating directly into an organization's existing code, AI, and data pipelines to provide a unified security operating model. It identifies potential risks and vulnerabilities early in the development phase and enforces policy controls during runtime. By utilizing a continuous feedback loop between red teaming results and real-world runtime monitoring, Pillar ensures that security guardrails are always optimized to the specific business purpose of each AI agent. The solution is designed for enterprise-scale deployments, offering role-based access controls and the ability to be deployed within private cloud environments for maximum data control.

Some common use cases include:

• Preventing Data Exfiltration: Blocking agents from extracting sensitive customer information or PII and sending it to unauthorized external endpoints.
• Securing Homegrown AI: Protecting custom-built AI applications by validating tool definitions and enforcing least-privilege access before deployment.
• Managing Shadow AI: Identifying and cataloging unapproved AI agents running in the organization to prevent unauthorized access to sensitive company data.
• Compliance Automation: Streamlining the process of meeting regulatory requirements for AI usage by automating policy enforcement and mapping controls to industry frameworks.