Company's Solutions

What Pillar Does
Pillar helps organizations move from lacking a reliable inventory of what AI they have, where it runs, and what data it touches-to full control over their AI landscape, with deep context, advanced testing, adaptive runtime controls, and AI governance enforcement.

We provide end-to-end AI security and governance across the entire lifecycle, from code to runtime.
We discover all AI assets across code repositories, data platforms, endpoints, and SaaS tools, including shadow AI.
Our AI-SPM maps security posture and prioritizes risks.
Red teaming validates real vulnerabilities with proof of exploitation.
Adaptive guardrails protect at runtime. Governance policies enforce compliance and access controls across every AI asset.
Each component enriches the next, creating a closed feedback loop that adapts security to each unique use case.

AI Discovery & Inventory
Agentless integration with code, AI/ML and cloud platforms automatically discovers all AI assets across your environment.
We build a complete AI-BOM: models, datasets, prompts, tools, agents, MCP servers, notebooks, and coding agents. Each asset is mapped to owner, department, use case, and lifecycle stage.
Shadow AI detection surfaces unauthorized AI tools and models across managed and unmanaged endpoints-including experiments outside version control that never pass through security gates.

AI Security Posture Management (AI-SPM)
After identifying all AI assets, Pillar evaluates security posture through static and dynamic analysis.
Dynamic threat modeling with real-time posture scoring identifies your biggest exposures.
Supply chain vulnerability detection flags risks in model artifacts, frameworks, and dependencies.
Risk scoring maps directly to OWASP LLM Top 10, MITRE ATLAS, ISO 42001, EU AI Act, and our SAIL framework.
Visual representations show how a threat actor may move through interconnected agentic systems.

Risk Validation & Attack Surface Mapping
Unlike generic LLM testing, Pillar attacks your full AI system-including tools, APIs, data connections, and business logic.
Our multi-agent attack system executes multi-step adversarial simulations that mirror sophisticated adversary behavior: prompt injection, data exfiltration, privilege escalation, unauthorized tool execution, and system prompt leakage.
Every vulnerability is validated with proof-of-exposure evidence including prompts, responses, and tool invocations.
RedGraph extends this by interacting with your AI agents through the browser exactly as an attacker would.
It performs active reconnaissance, launches coordinated multi-turn attacks against web-accessible AI applications, and maps your entire AI estate as nodes and edges-revealing the attack paths that actually matter.
For third-party AI apps where you don't have access to underlying code, Pillar offers black-box testing with just a URL and credentials.

Runtime Guardrails & Monitoring
Adaptive guardrails are calibrated by red teaming findings from your environment-not generic rules.
This means protection tunes precisely to the context manipulation, tool abuse, and data exfiltration paths that actually threaten your applications.
Security guardrails block prompt injection, jailbreaking, and reconnaissance attempts.
Data privacy guardrails identify, mask, or block PII, PHI, secrets, and sensitive data.
Safety guardrails detect toxic or harmful content.
Topical guardrails enforce code of conduct and topic boundaries based on custom policies.
Malicious use detection flags attempts to weaponize AI for keylogging, C2, or other attacks.
Full telemetry captures every prompt, response, tool call, and tool response with session, user, and application-level analytics.
Long-term log retention with SIEM/SOAR integration supports compliance auditing, incident response, and threat hunting.

Governance & Policy Management
Define and enforce policies for every AI asset in your environment.
The Policy Center provides centralized control over AI usage across the organization with real-time compliance visibility.
Policy Enforcement sets rules for approved models, data handling, and licensing requirements.
Compliance Mapping aligns AI assets to GDPR, SOC 2, HIPAA, EU AI Act, and ISO 4200.
Violation Alerting automatically flags non-compliant assets and unapproved deployments.
Access Controls manage permissions and ownership across AI projects.
The Issues dashboard tracks open and resolved findings by severity, asset type, and first-seen date-with direct ticketing integration for immediate remediation workflows.
Custom policies let you define organization-specific rules beyond standard frameworks, ensuring AI usage aligns with your security requirements and business policies.

Prominent Case Study

Pillar works with organizations deploying AI at scale across different industries, from banking and healthcare to technology, manufacturing, and retail.
While use cases vary, the core challenge is the same: how do you move fast with AI while maintaining security, compliance, and control?

Each industry operates under its own regulatory frameworks-banking regulators and PCI-DSS for financial services, HIPAA for healthcare, GDPR for data protection, and emerging AI-specific requirements like the EU AI Act and ISO 42001.
Pillar maps AI assets to these standards, validates compliance posture, and enforces policies that align with your specific regulatory environment.

Customer story: Global US Financial Institution

Challenge: This institution has one of the strongest security teams in the industry.
When AI adoption accelerated - with pressure from management to move faster - they built manual verification processes, leveraged existing tools, and worked with Microsoft to use what was available. Despite significant effort, they couldn't keep pace with organizational demand.
The tools left gaps in coverage and detection quality, and real threats were getting through.
They reached out to Pillar for better runtime protection.

Solution: The team came to Pillar for runtime guardrails.
We replaced their existing solution with stronger detection of evasion and reconnaissance attempts.

Once runtime controls was deployed, we walked them through the entire AI lifecycle to make sure they weren't missing anything.
We started with deep AI discovery - scanning their code repositories and mapping their AI projects.
Not just to find shadow AI, but to give them depth into each system: configurations, business logic, tool connections, MCP servers, and runtime behavior

From there, we defined governance policies including model allow lists and unsafe model file detection, then red teamed priority systems and validated exploitable vulnerabilities with full evidence.

Impact: In a few days, we replaced their in-house solution with a unified platform that gave them stronger runtime protection, deep visibility into the internals of every AI system, governance enforcement across all projects, and validated security through red teaming-without the manual processes that were slowing them down.