Head of AI Security

Gong Gong · Enterprise · Tel Aviv, Israel · Information Security

Head of AI Security responsible for the end-to-end AI security strategy, including infrastructure, data pipelines, LLMs, RAG systems, and autonomous AI agents. The role involves designing secure-by-design frameworks, AI threat modeling, vulnerability management, AI governance, and building security tooling and automation for AI environments. Key responsibilities include protecting AI systems against emerging threats, preventing data leakage, securing complex AI architectures, and embedding security into the ML lifecycle.

What you'd actually do

  1. Gong’s end-to-end AI security strategy across AI/ML infrastructure, data pipelines, LLMs, RAG systems, and autonomous AI agents
  2. Secure-by-design frameworks and security architecture for AI-powered products and platforms
  3. AI threat modeling, adversarial testing, red teaming, and vulnerability management programs
  4. AI governance, internal security policies, and alignment with emerging global AI regulations and standards
  5. Security tooling, automation frameworks, and continuous monitoring capabilities for AI environments

Skills

Required

  • 7+ years in cybersecurity
  • 2+ years explicitly dedicated to securing data science environments, ML pipelines, or AI-native applications
  • Exceptional communication skills
  • Proven track record of leading cross-functional initiatives
  • Translating highly complex technical AI risks into actionable business strategies for executives
  • Deep understanding of deep learning architectures, LLM mechanics, vector databases, and multi-agent orchestration frameworks
  • Strong familiarity with MITRE ATLAS
  • Strong familiarity with OWASP GenAI Security Project

Nice to have

  • Experience with adopting security AI agents

What the JD emphasized

  • securing our proprietary AI technologies
  • multi-agent systems
  • RAG architectures
  • AI/ML infrastructure
  • data pipelines
  • LLMs
  • RAG systems
  • autonomous AI agents
  • AI threat modeling
  • adversarial testing
  • red teaming
  • vulnerability management
  • AI governance
  • global AI regulations
  • AI environments
  • prompt injection
  • model inversion
  • data poisoning
  • training data leakage
  • sensitive customer and enterprise data
  • LLMs
  • RAG pipelines
  • multi-agent systems
  • ML lifecycle
  • adversarial testing
  • AI governance
  • compliance
  • regulatory requirements
  • secure and trustworthy enterprise AI
  • AI innovation to scale safely and responsibly
  • highly sensitive conversational and business data
  • resilient AI systems
  • emerging threat vectors
  • security-first AI engineering culture
  • AI security, governance, and MLSecOps
  • hypergrowth AI company
  • explicitly dedicated to securing data science environments, ML pipelines, or AI-native applications
  • leading cross-functional initiatives
  • translating highly complex technical AI risks into actionable business strategies for executives
  • deep learning architectures
  • LLM mechanics
  • vector databases
  • multi-agent orchestration frameworks
  • MITRE ATLAS
  • OWASP GenAI Security Project
  • adopting security AI agents

Other signals

  • AI security strategy
  • AI threat modeling
  • AI governance
  • Securing LLMs, RAG, and multi-agent systems