AI Agent Abuse Prevention Engineer

Zendesk Zendesk · Enterprise · San Francisco, CA +2

This role focuses on preventing abuse and ensuring security for AI agents within a SaaS customer support platform. Responsibilities include threat modeling, designing mitigation controls, building detection systems, and leading cross-functional security initiatives related to AI agents.

What you'd actually do

  1. Lead threat modeling for AI agent features, integrations, and APIs (prompt injection, jailbreaks, data leakage, automated workflow abuse).
  2. Design and prototype mitigation controls (e.g., input/output sanitization, provenance tracing, policy gates, token/session lifetimes, capability scoping, sandboxing).
  3. Build anomaly detection rules, telemetry, and behavioral analytics to surface anomalous agent activity and abuse patterns.
  4. Act as a subject-matter expert for investigations involving AI agents; define containment, eradication, and customer communications playbooks.
  5. Collaborate with Legal/Privacy for compliance, Product for roadmap trade-offs, and Customer Success for mitigation support.

Skills

Required

  • 10+ years of professional experience in cybersecurity, software engineering, or ML security
  • Deep understanding of application/API security, OAuth/token lifecycle, session management, and modern auth patterns.
  • Practical experience with LLMs/agents: understanding of prompt engineering risks, injection attacks, and mitigation approaches for model-based systems.
  • Strong track record leading cross-functional technical initiatives and influencing product decisions.
  • Excellent communication skills
  • Experience at a SaaS company with a customer support platform.

Nice to have

  • Experience with incident response and forensic investigations involving data exfiltration or API abuse.
  • Prior role building agent safety, trust & safety, or ML security programs.
  • Background in privacy, compliance frameworks (SOC2, GDPR), or experience working with Legal/Compliance.
  • Advanced degree in CS, Security, or related field and/or relevant certifications (OSCP, CISSP, etc.).

What the JD emphasized

  • AI agent abuse prevention
  • secure agent architectures
  • mitigation controls
  • prompt injection
  • jailbreaks
  • data leakage
  • automated workflow abuse
  • input/output sanitization
  • provenance tracing
  • policy gates
  • token/session lifetimes
  • capability scoping
  • sandboxing
  • anomaly detection
  • abuse patterns
  • incident response
  • forensics
  • agent safety/security

Other signals

  • AI agent abuse prevention
  • design secure agent architectures
  • mitigation controls
  • anomaly detection for AI agents