Sr Cyber Engineer

How you can make a difference  

HealthEquity is expanding its use of AI. Teammates use generative AI tools, teams are building applications on large language models, and AI agents are beginning to connect to internal systems and data. These uses bring risks that existing controls were not designed for, including prompt injection, sensitive data leaving through prompts and tool calls, and agents operating with more access than they need.

This role builds HealthEquity's defenses for these use cases on two of our core security platforms, Cloudflare and Palo Alto Networks. It also owns core engineering for both platforms, because AI controls depend on the edge, network, and access layers beneath them.

This is a hands-on engineering role. You will design, build, deploy, and operate these controls yourself, and you will be accountable for how they perform in production.

What you’ll be doing 

Build defenses for AI use cases

• Discover and govern AI use. Build visibility into the AI applications, coding assistants, agents, and MCP servers in use across HealthEquity, including unsanctioned tools. Apply allow, monitor, isolate, or block policies based on application risk and data sensitivity.
• Protect member data in AI interactions. Design and tune data loss prevention for prompts, file uploads, responses, and agent tool calls so member PHI, PII, and financial data reach only approved AI services, without blocking legitimate work.
• Protect HealthEquity-built AI applications. Deploy runtime protections for production AI applications and agents, including prompt injection and jailbreak detection, sensitive data detection, topic and output controls, token-based rate limiting, and audit logging that protects the sensitive data it captures.
• Secure agent access to internal systems. Define how AI agents authenticate, which tools and data they can reach, and how each tool call is logged and attributed to a person or service. Route approved MCP servers through governed access paths and block connections that bypass them.
• Centralize access to AI models. Route LLM provider traffic through managed AI gateways for policy enforcement, usage and cost visibility, and credential protection. Provider API keys live in a secrets vault, never in source code, configuration files, or pipelines.
• Handle AI agents on member-facing sites. Work with product and fraud prevention teams to decide how member and employer sites and APIs treat AI crawlers and agents acting on a person's behalf, then enforce that decision with Cloudflare bot controls.
• Test what you build. Run adversarial testing against AI applications and agents, covering prompt injection, data exfiltration, tool misuse, and privilege abuse, and turn findings into control and detection improvements.
• Engage early. Partner with Application Security, Architecture, Data, Privacy, and Compliance to review proposed AI use cases, publish secure reference architectures, and turn risk requirements into enforceable controls.

Relevant capabilities include Cloudflare AI Gateway, AI Security for Apps (formerly Firewall for AI), AI Prompt Protection, and MCP server portals, along with Palo Alto Networks Prisma AIRS (AI Runtime Security, AI Gateway, and AI Red Teaming) and AI Access Security.

Own the Cloudflare platform

• Engineer and operate HealthEquity's Cloudflare environment: WAF, API Shield, Bot Management, DDoS protection, rate limiting, DNS, and TLS, plus Cloudflare One services (Access, Gateway, Tunnel, CASB, DLP, and Browser Isolation).
• Protect member and employer applications and APIs from account takeover, credential stuffing, scraping, and other automated abuse.
• Manage configuration as code with Terraform, peer review, and CI/CD, and build custom controls on Cloudflare Workers where native features fall short.

Own the Palo Alto Networks platform

• Engineer and operate Palo Alto Networks next-generation firewalls (hardware, VM-Series, and Cloud NGFW), Panorama and Strata Cloud Manager, Prisma Access, and GlobalProtect.
• Design and maintain segmentation, App-ID and User-ID policy, decryption policy, and security profiles for Advanced Threat Prevention, Advanced URL Filtering, Advanced WildFire, Advanced DNS Security, and Enterprise DLP.
• Lead policy hygiene (unused, shadowed, and overly broad rules) and lifecycle work, including PAN-OS upgrades, high availability, and timely response to vendor security advisories.
• Automate with PAN-OS and Strata Cloud Manager APIs, Terraform, Ansible, and Python.

Detect, respond, and improve

• Send Cloudflare and Palo Alto Networks telemetry to the SIEM and build detections for AI-related activity, such as prompt injection attempts, sensitive data sent to AI services, and unusual agent behavior.
• Serve as an escalation point for incidents involving edge, network, and AI controls, and take part in the on-call rotation.
• Track and report control coverage, enforcement rates, false-positive rates, and time to remediate.
• Write design documents, runbooks, and standards; lead design reviews and mentor other engineers.

What you will need to be successful

• 7+ years in security, network, or infrastructure engineering.
• Deep hands-on engineering experience with either Palo Alto Networks (PAN-OS, Panorama or Strata Cloud Manager, and GlobalProtect or Prisma Access) or Cloudflare (WAF, API Shield, Bot Management, and Cloudflare One), and production experience with the other.
• Hands-on experience securing generative AI, such as controlling AI application use, applying DLP to AI traffic, deploying an AI gateway or LLM firewall, or securing an LLM-based application or agent. Because this field is new, we weigh what you have built and run in production more heavily than years of experience.
• Working knowledge of AI-specific threats, including direct and indirect prompt injection, sensitive information disclosure, excessive agency, and tool misuse, and of the OWASP Top 10 for LLM Applications, the OWASP Top 10 for Agentic Applications, and MITRE ATLAS.
• Strong networking fundamentals: TCP/IP, routing (including BGP), DNS, TLS and PKI, HTTP, proxies, and IPsec.
• Infrastructure-as-code and automation experience with Terraform, Git, CI/CD, and Python.
• Experience integrating identity protocols (SAML, OIDC, OAuth 2.0, and SCIM) with an enterprise identity provider such as Microsoft Entra ID or Okta.
• Experience in a regulated environment that handles sensitive health or financial data, for example under HIPAA, PCI DSS, or SOC 2.
• Clear written communication for technical and non-technical audiences.

Preferred

• Palo Alto Networks Next-Generation Firewall Engineer or Network Security Professional certification (an active PCNSE is also welcome).
• Hands-on experience with Prisma AIRS, Cloudflare AI Gateway, AI Security for Apps, or MCP server portals.
• Development experience on Cloudflare Workers (JavaScript or TypeScript).
• Experience building or securing AI agents and MCP servers.
• Cloud network security experience in AWS, Azure, or Google Cloud.
• Detection engineering experience with Cortex XSIAM, Splunk, Microsoft Sentinel, or a similar platform.
• Familiarity with the NIST AI Risk Management Framework and its Generative AI Profile (NIST AI 600-1), or with ISO/IEC 42001.

What success looks like

• First 90 days: A current inventory of AI applications, agents, and MCP servers in use, with policy applied to sanctioned and unsanctioned tools. DLP for AI prompts running in monitor mode against member data patterns. Cloudflare configuration managed as code.
• First 6 months: Runtime protections and audit logging in place for every production HealthEquity AI application. AI DLP enforcing where detection accuracy supports it. AI-related detections live in the SIEM. Palo Alto Networks changes flowing through the same review and automation pipeline.
• First 12 months: An approved reference architecture for AI agents and MCP connections, enforced through Cloudflare and Palo Alto Networks controls. Adversarial testing built into the release process for AI applications. A measurable reduction in firewall rule debt.

#LI-Remote

This is a remote position.

The actual compensation offer is determined based on job-related knowledge, education, skills, experience, and work location. This position will be eligible for performance-based incentives as part of the total compensation package, in addition to a full range of benefits including:

• Medical, dental, and vision
• HSA contribution and match
• Dependent care FSA match
• Uncapped paid time off
• Paid parental leave
• 401(k) match
• Personal and healthcare financial literacy programs
• Ongoing education & tuition assistance
• Gym and fitness reimbursement
• Wellness program incentives

Onboarding & Travel

This is a remote role, with an in-person onboarding training component. New team members must participate in Trailhead, HealthEquity’s immersive onboarding experience Trailhead is designed to foster meaningful connections, support your integration into the organization, and equip you with a strong understanding of our business. Trailhead participation is a key expectation of this role. Trailhead is held onsite at our headquarters once per quarter. HealthEquity covers all required travel and accommodations. 

This role may begin with a virtual, self-paced onboarding experience, followed by a mandatory onsite Trailhead session at a later date.