Staff AI Security Engineer

Why You’ll Love This Role

We are seeking a talented and experienced Staff AI Security Engineer to help build Cribl’s new AI Systems team. In this pivotal role, you will design, implement, and operationalize security and governance frameworks that enable rapid AI adoption to scale safely across Cribl’s internal systems and workflows. This is a foundational role on a newly established team tasked with providing the shared infrastructure, security guardrails, and reusable patterns needed to turn AI from fragmented experimentation into durable company capabilities.

You will be instrumental in bringing security, governance, and safety to Cribl’s rapidly expanding AI footprint, including API tokens, secrets management, MCP security, shadow AI mitigation, AI telemetry, and compliance readiness. The team’s mandate is to provide the “paved road” for AI at Cribl: secure access, governed integrations, reusable workflows, and a platform that enables teams to move faster without creating security, compliance, or operational risk.

This role will be part of the Corporate AI Systems team and will report directly to the Chief Information Security Officer (CISO). It will partner closely with stakeholders across Security, Enterprise Applications, Product, Engineering, IT, Legal, and the various business teams adopting AI to ensure Cribl’s AI capabilities scale securely and pragmatically.

As An Active Member Of Our Team, You Will...

As the Staff AI Security Engineer, you will be the foundational builder of Cribl’s AI security and governance layer. Your key responsibilities will include:

• AI Security Architecture & Governance: Define, threat model, and operationalize the security architecture for Cribl’s internal AI platform, including standards, controls, approval patterns, and secure-by-design guidance for AI use cases before they scale into production.
• Shadow AI Discovery & Remediation: Partner with Business Operations to maintain visibility into AI tools, licenses, API tokens, MCP servers, and ad hoc workflows in use across the company, and monitor for ungoverned or high-risk patterns that require remediation.
• MCP Security & Registry Management: Own the framework for vetting MCP servers, maintaining an approved registry, defining risk tiers, and enforcing secure connection patterns as MCP adoption expands across teams.
• Secrets, Identity & Token Protection: Establish secure patterns for secrets management, non-human identities, scoped credentials, OAuth-based access, and token governance to enforce least-privilege access and reduce credential exposure in AI builds.
• Prompt Injection Defense & Safe Execution Controls: Design and deploy guardrails for prompt injection defense, deterministic validation, human-in-the-loop approvals, and additional controls for high-risk workflows that combine sensitive data, untrusted content, and external action.
• AI Telemetry, Detection & Incident Response: Build monitoring and detection for AI-specific security events (e.g., anomalous API calls, token abuse, data exfiltration, prompt injection attempts) and integrate into the broader enterprise incident response program.