Staff Cyber Security Engineer – AI Data Protection

GE Healthcare GE Healthcare · Healthcare · Krakow, Lesser Poland, Poland +3 · Remote · Digital Technology / IT

Staff Cyber Security Engineer focused on Data Loss Prevention (DLP) for AI-related data risks within GE HealthCare. The role involves owning and operating AI DLP platforms, engineering DLP capabilities, assessing AI data protection risks, defining and implementing AI guardrails, and automating DLP workflows. It requires expertise in AI security tools, cloud security, and scripting languages like Python and PowerShell, with a focus on securing enterprise AI adoption and enabling responsible AI use.

What you'd actually do

  1. Own and operate the AI DLP platform, including administration, configuration, policy management, tuning, upgrades, deployments, and platform health.
  2. Engineer and deploy DLP and AI protection capabilities across endpoint, cloud, mobile, and network environments.
  3. Assess and manage AI-related data protection risks across Cyber, IT, and business teams.
  4. Define and implement AI guardrails to prevent data leakage, policy violations, and unsafe usage.
  5. Automate DLP workflows, triage, and integrations using Python, PowerShell, KQL, and Bash.

Skills

Required

  • Experience owning enterprise DLP or data protection platforms, including Microsoft Purview, Defender, Sentinel, or similar tools.
  • Hands-on knowledge of AI security tools, agentic AI frameworks, and AI monitoring platforms.
  • Proficiency in Python, PowerShell, Bash, KQL, SIEM analytics, and Azure automation.
  • Strong understanding of cloud security, AI/ML risk, privacy, compliance, identity, networking, endpoints, and infrastructure security.

Nice to have

  • Knowledge of CI/CD, DevSecOps, agile delivery, and relevant cyber security certifications is beneficial.

What the JD emphasized

  • AI-related data risks
  • AI DLP platform
  • AI security tools
  • AI guardrails
  • AI enablement
  • responsible AI adoption
  • AI/ML risk

Other signals

  • AI Data Protection
  • Data Loss Prevention (DLP) platforms
  • secure enterprise AI adoption
  • reduce data leakage risk
  • enable responsible AI use
  • AI guardrails
  • LLM, generative AI, and shadow AI risks