Critical AI Security Questions We Help You Solve
Leading organizations trust Pillar to secure every phase of their AI journey— from discovery to deployment. Here are the high-impact questions we help you answer so you can innovate with confidence.
Oversight
Visibility & Discovery
Do we have complete, real-time visibility into all our AI models, agents, prompts, and datasets—across every cloud and platform?
Modern organizations often have AI assets—including models, datasets, agents, and prompts—scattered across code bases, MLOps stacks, and shadow IT platforms. Without real-time discovery and inventory, hidden risks, compliance violations, and unapproved deployments can go undetected.
Pillar provides automated, continuous discovery and fullinventory of all AI assets, integrating directly with your code,data, and cloud platforms. This eliminates blind spots, enablescompliance with frameworks like ISO 42001, and ensures youalways know what’s running, where, and who is responsible—empowering proactive risk management from day one.
“For the first time, our security team sees every model, dataset, and prompt in a single dashboard—no more chasing blind spots.”
ciso,
Global e-commerce
How do we discover and track Shadow AI activities happening beyond our managed platforms?
R&D and AI teams are downloading untrusted models directly to their laptops, experimenting with sensitive datasets outside version control, and configuring agentic apps with permissions that never pass through security gates. These endpoint-level AI activities exist in a parallel universe—invisible to SAST/DAST tools, unchecked by supply chain policies, and unmonitored by traditional security controls.
Pillar extends AI discovery to the endpoint-level with an agentless approach that detects local AI deployments, downloaded models, datasets, notebooks, coding agents, cli apps, and MCP servers. This endpoint data feeds directly into your asset inventory, creating a complete and unified picture of your entire AI landscape— to guarantee full asset discovery and governance.
“For the first time, our security team sees every model, dataset, and prompt in a single dashboard—no more chasing blind spots.”
ciso,
Global e-commerce
AI spm
Security Posture Management
What are the most critical security and privacy risks in our AI development and production environments—right now?
AI systems are exposed to evolving threats—ranging from prompt injection and supply chain attacks to model theft and data leakage. These risks can impact every stage of the AI lifecycle, from development and testing to live deployment.
Pillar continuously scans for high-impact vulnerabilities and prioritizes risks across your entire AI stack. With dynamic threat modeling, AI fingerprinting, and real-time posture scoring, you get actionable insights on where your biggest exposures are—mapped directly to industry standards like OWASP LLM Top 10 and MITRE ATLAS. This lets you focus resources on what matters most, before attackers do.
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"What impressed us most about Pillar was their holistic approach to Al security."
Evaluate
AI Red Teaming
How do we continuously assess and simulate real-world attack scenarios against our AI applications and agentic flows?
Traditional testing is not enough to secure modern AI systems.Adversaries are constantly developing new techniques—frommulti-turn attacks to advanced model poisoning and jailbreaks.
Pillar equips your team with continuous AI-driven red teaming: automated, multi-step attack simulations tailored to your applications and agentic workflows. We benchmark your defenses against real-world threats, deliver comprehensive evidence and risk reports, and provide actionable recommendations—so you can stay ahead of attackers and build security into every update.
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"We needed a security partner that not only pinpoints vulnerabilities but also helps remediate them automatically."
How do we stress-test third-party AI tools and organizational copilots for data exposure and security risks?
Enterprises are rapidly deploying third-party AI applications, copilots, and custom chatbots, often without access to the underlying code. This creates a critical security blind spot, as traditional testing methods cannot assess these "black box" systems for security vulnerabilities, leaving confidential corporate data exposed to potential leaks and misuse.
Pillar’s AI Red-Teaming engines stress-test any accessible AI application with zero integration required—only a URL and credentials are needed. Our AI-powered adversarial agents automatically probe for data access boundaries and execute custom attack scenarios to simulate real-world threats. This allows you to rapidly discover and remediate data exposure risks in any third-party AI tool, ensuring they are safe for enterprise use and compliant with internal security policies.
"We needed a security partner that not only pinpoints vulnerabilities but also helps remediate them automatically."
Powered by Real-World Threat Intel
Al Applications Monitored
Al Interactions Analyzed
Identified Attacks
Defense
AI Runtime Protection
How can we monitor interactions with our AI systems and identify any anomalies and risks during usage?
Once AI systems go live, teams often lose visibility into what data flows through them, how agents behave, or when policies are violated—leaving gaps in threat detection and audit readiness.
Pillar captures complete, session-level logs of every AI interaction—across prompts, tool calls, agent actions, and responses. We flag anomalies like prompt injections and manipulated outputs in real time. Pillar also integrates with your existing logging systems to ingest external logs for analysis, enabling deep investigations and automated detection without breaking your current workflow.
"Integrating Pillar’s advanced guardrails added a vital layer of protection to our infrastructure."
How can we create and enforce strict policies to prevent prompt injection, data exfiltration
and unauthorized actions in real-time?
Attackers exploit weaknesses in AI input and output handling to inject malicious prompts, exfiltrate data, or trigger unauthorized behaviors—often bypassing legacy security controls.
Pillar deploys adaptive, model-agnostic guardrails to scan all inputs and outputs in real time. Our platform enforces least-privilege on AI actions, blocks prompt injection, and automatically detects anomalous instructions or data flows. With instant alerts and actionable response playbooks, you can contain threats before they escalate and ensure your AI systems remain trustworthy.
"Integrating Pillar’s advanced guardrails added a vital layer of protection to our infrastructure."
govern
Compliance and Monitoring
Where are our compliance gaps with emerging AI laws like AI Act and ISO 42001—and how do we close them?
Regulatory requirements for AI are rapidly evolving, with new mandates like the EU AI Act and ISO 42001 demanding rigorous oversight of AI models, data, and processes. Non-compliance can lead to legal penalties, reputational harm, and operational disruption.
Pillar delivers automated compliance evidence collection, policy enforcement, and audit-ready reporting for every model, dataset, and interaction. Our platform maps your AI projects, assets, and workflows to the relevant regulatory frameworks, highlights any gaps, and guides you in implementing the controls and documentation needed for continuous compliance—simplifying audits and reducing regulatory risk.
"Pillar stood out by delivering a comprehensive, adaptable solution that seamlessly integrates with our platform."
See Pillar in action
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In your 30 minute personal demo, you will learn how Pillar:
Seamlessly integrates with your code, AI and data platforms and provide full visibility into AI/ML assets.
Automatically scan and evaluates your AI assets for security risks.
Enables you to create and enforce AI security policies in development and runtime.
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