
AI Security Manager
Role Purpose
Operationalize AI system security for products that embed ML or generative‑AI capabilities. Build controls spanning model, data, prompt, and supply-chain risk while integrating with existing Secure SDLC/DevSecOps practices.
Key Outcomes
Publish an AI Security Control Baseline (model inventory, data lineage, red‑teaming, evaluations, guardrails).
Integrate AI checks into CI/CD: dataset governance, model/package SBOM, safety/toxicity tests, prompt policies.
Run AI misuse/abuse testing (jailbreaks, prompt injection, data exfiltration) with repeatable playbooks.
Establish Responsible AI review with Privacy/Legal and GRC; document residual risk.
Responsibilities
Build and maintain model and dataset inventories; define criticality tiers and protections.
Drive AI red‑team exercises and security evaluations; partner with Product and Data Science teams.
Define secrets, keys, and model endpoint hardening (authorization, rate limiting, content filters).
Monitor supply chain for models, libraries, and embeddings; enforce signing and provenance.
Produce executive‑readable risk narratives and dashboards.
Required Qualifications
6–8+ years in security with 2–3 in AI/ML security or closely related fields.
Familiarity with LLM risks, RAG, and model hosting patterns (e.g., Azure OpenAI).
Proficiency in Python or scripting for test harnesses and CI/CD integrations.
Experience with privacy and data protection in EMEA (GDPR, data minimization).
Preferred Qualifications
Experience in AI red teaming and safety evaluations; exposure to Azure AI or GCP Vertex.
Certifications: CCSP, CISSP, Azure AI Engineer (or equivalent).
Key Performance Indicators (KPIs)
Coverage of AI features by the control baseline (%).
Red‑team findings trend and closure rate.
Model/data inventory completeness.
Safety test coverage and regression stability.
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