About The Position

Magnet Forensics is seeking a Data Protection, Purview, DLP & AI Security Consultant to improve organizational visibility and control over the movement of sensitive information while helping establish a scalable governance model for AI adoption. This role will focus on Microsoft Purview, Data Loss Prevention, Information Protection, Insider Risk, Data Classification, and emerging AI security controls. The successful candidate will deliver practical improvements that reduce risk while improving business usability. This is a 3-4 month contract role.

Requirements

  • 5+ years of Data Protection or Information Security experience
  • Strong Microsoft Purview experience
  • Data Loss Prevention expertise
  • Information Protection expertise
  • Data Classification experience
  • Defender for Cloud Apps experience
  • Insider Risk Management experience

Nice To Haves

  • AI governance experience
  • Copilot security experience
  • Regulatory compliance experience
  • Data Security Posture Management experience
  • Privacy program experience

Responsibilities

  • Assess and improve existing DLP policies, policy effectiveness, false positive rates, alert quality, and business user experience.
  • Develop recommendations and implement improvements across Microsoft 365, endpoints, cloud applications, and collaboration platforms.
  • Improve sensitivity labeling strategy, label adoption, information protection controls, data classification coverage, and data ownership visibility.
  • Create sustainable governance processes for maintaining data classification.
  • Develop baseline controls and visibility for Microsoft Copilot, ChatGPT, Claude, and other emerging generative AI technologies.
  • Establish risk management recommendations, governance controls, data handling guidance, monitoring recommendations, and executive reporting.
  • Develop reporting around data movement, DLP incidents, risky user activity, insider risk indicators, AI-related exposure, and sensitive data repositories.

Benefits

  • learning and development
  • hybrid-flexible approach
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