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Complete AI Security Across Your Entire Stack
Pillar gives you end-to-end visibility and control over your AI assests — from SaaS and cloud to code and endpoints.
Homegrown AI
Do we know the security posture of every AI application our teams are building — before it reaches production?
Engineering teams ship AI fast: internal agents, RAG pipelines, custom models, and LLM-powered workflows. But homegrown AI introduces risks that traditional AppSec tools miss, from training data poisoning and insecure model configurations to prompt injection vulnerabilities at the code level.
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Pillar integrates into your development lifecycle to discover, assess, and protect every homegrown AI application. We scan code repositories, model configurations, RAG pipelines, and prompt templates for vulnerabilities, then surface prioritized findings with remediation guidance before anything reaches production. Red teaming validates your defenses against real-world attacks, while runtime guardrails enforce policy on every production interaction.
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Agentic Endpoint
Do we know which AI agents are running on our endpoints — and what those agents can access?
Employees run AI coding agents on their workstations daily: IDE assistants, CLI tools, code generators, and dozens more. These agents execute commands, chain tool calls, and connect to production systems through MCP servers and local integrations. Security teams have zero visibility. Configurations sit scattered across home directories, MCP servers store hardcoded credentials, and shadow AI proliferates without IT approval.
Pillar extends to the endpoint to discover every AI coding agent, MCP server, plugin, and configuration across your fleet. We analyze permissions, credentials, access scopes, and auto-run settings, then provide step-by-step remediation. At runtime, we monitor every prompt, tool call, and command to detect prompt injection, tool poisoning, and data exfiltration. Deploy in minutes via MDM with no developer workflow disruption.
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AI Gateway Security
Is our AI gateway enforcing security — or just routing traffic?
AI gateways serve as the central access point for every AI interaction in your organization. But most focus on routing, rate limiting, and cost management, not security. Without deep inspection, sensitive data leaks through prompts, injection attacks pass undetected, and unauthorized users access models they shouldn't.
Pillar integrates with leading AI gateway frameworks, adding a security enforcement layer without re-architecting your stack. Pillar inspects every prompt and response in real time for sensitive data exposure, prompt injection, toxic content, and policy violations. Granular access controls enforce who can use which models, with what data, and under what conditions.
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MCP & Tool Security
Do we know what MCP servers and tools our agents are connected to — and what those integrations can access?
MCP servers and tool integrations give agents direct access to databases, APIs, file systems, and external services. But they also represent the largest unmonitored attack surface. Hardcoded credentials, overly permissive scopes, and poisoned tool descriptions can turn any integration into an entry point for data theft, privilege escalation, or supply chain attacks.
Pillar discovers and inventories every MCP server and tool integration across your AI environments, analyzing each connection for hardcoded secrets, excessive permissions, unsigned configurations, and vulnerable dependencies. At runtime, we validate that tool calls match their declared schemas and flag anomalous behavior, like a tool accessing resources outside its expected scope.
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Agentic AI Security
How do we ensure our AI agents are acting within scope, operating with least-privilege permissions, and not propagating compromised behavior across workflows?
An agent can access a file, call an API, and write to cloud storage in a single session. No individual step looks malicious, but the full chain constitutes data exfiltration. Because agents operate under their maker's broad organizational identity with no scoped boundary enforcement, the system can't distinguish intended behavior from unauthorized action.
Pillar tracks tool invocations across every agent session, mapping multi-step chains to detect cascading exfiltration and privilege escalation before data leaves your environment. The platform validates that every agent operates within its intended permission scope and flags actions that deviate from declared intent, catching identity mismatches when agents inherit overly broad access. In orchestration workflows, Pillar inspects every inter-agent handoff and intercepts poisoned instructions before they reach dependent agents.
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Embedded-AI
How do we test and secure AI features embedded in SaaS platforms we don't own, can't inspect, and didn't build — but that have access to all our data?
AI agents embedded in enterprise SaaS platforms operate as black boxes inside your environment. You don't control the model, can't access its internals, and can't red team what you can't inspect. Yet these features run in production with access to your most sensitive data and zero runtime visibility.
Pillar secures embedded SaaS AI from two angles. Black-box red teaming probes AI features from the outside, testing for prompt injection, indirect injection via shared content, data over-exposure, and access control weaknesses. At runtime, Pillar monitors how embedded agents behave in production, flagging anomalous access patterns, manipulated responses, and behavior that deviates from expected baselines.
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