Build and operate enterprise-grade security controls to keep AI adoption safe across the organization.

Lead enterprise discovery, inventory, and visibility for all AI usage, including tracking approved systems, third-party SaaS tools, open-source models, developer plugins, and shadow AI integrations.
Architect, build, and maintain automated remediation workflows and policy enforcement engines that reduce AI risks without slowing engineering or business velocity.
Design and deploy threat mitigation controls for AI-specific vulnerabilities, including direct and indirect prompt injection, jailbreaking, training data poisoning, and insecure output handling.
Partner with the Enterprise Data Protection team to implement context-aware data loss prevention (DLP) controls that prevent unauthorized data ingestion or leakage into AI model pipelines.
Perform security assessments for AI applications and agents, including integrations such as MCP servers and tool connectors, plus third-party AI products.
Research and apply emerging AI security frameworks and tooling, including OWASP Top 10 for LLMs, MITRE ATLAS, and NIST AI RMF.
Collaborate with cloud, infrastructure, and application security teams to review, document, and harden model hosting environments, AI workloads, and API gateways.

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Cybersecurity, or a related STEM field, or equivalent practical experience.
5+ years hands-on experience in enterprise security engineering, application security, cloud security, or security architecture.
Proven technical expertise in AI/ML security risks, including prompt injection, model evasion, data poisoning, insecure model supply chains, and LLM application vulnerabilities.
Experience implementing AI guardrails such as sensitive data and secrets detection, prompt-injection defenses, and tool-call policies across AI assistants, coding agents, and internally built agents.
Proven ability to build automated detection and remediation workflows.
Strong practical knowledge across enterprise security domains: Identity & Access Management, cloud infrastructure (AWS, GCP, or Azure), endpoint security, and Data Protection/DLP.
Proficiency in at least one modern programming or scripting language, such as Python or Go, for automation, API integrations, and custom security tooling.
Strong communication and cross-functional leadership skills, with a history of enabling business and engineering teams to adopt new technologies safely.

Relevant cloud or security certifications (examples listed): AWS Solutions Architect, Google Cloud Certified Associate, CCSP, PCNSE.

No. Palo Alto Networks will not sponsor applicants for work visas for this position.

Senior Enterprise AI Security Engineer

Job Description