AI Security Analyst

Description

• Partner with legal, compliance, and privacy teams on AI risk management and regulatory requirements
• Embed security controls into AI/ML models, LLM-based applications, and agentic workflows across the SDLC
• Support and apply AI security standards aligned with NIST, ISO, and emerging AI governance frameworks
• Identify and mitigate AI-specific threats such as prompt injection, data poisoning, model abuse, and insecure agents
• Conduct security reviews of Generative AI, traditional ML models, and supporting data pipelines
• Assist in evaluating and operating AI security and observability tools for model monitoring and data protection
• Contribute to internal guidance and training on secure AI development practices

Requirements

We are seeking a Junior to Mid-Level IT Security Analyst with hands-on exposure to AI, Generative AI, and Agentic AI security. This role supports a Product Security organization focused on implementing security guardrails for AI-enabled applications. You will work closely with Data, AI, and Product teams to secure machine learning pipelines, large language models, and agent-based systems in a cloud-native enterprise environment.

Preferred / Nice-to-Have Skills

IT Security Analyst II – AI & Emerging Technology Security (Contract)

• Experience with AI security platforms such as Zenity or HiddenLayer
• Familiarity with Microsoft security and AI governance tooling, including:
• Familiarity with core security domains such as authentication, encryption, network security, and IAM
• Scripting or automation experience for security testing and validation
• 2+ years of experience in cybersecurity, with exposure to AI/ML environments
• Azure AI Content Safety
• Experience with AI model governance or AI security programs
• Strong communication and collaboration skills within agile or SAFe-based teams
• Microsoft Defender for Cloud
• Bachelor’s degree in Computer Science, Information Security, or related discipline
• Experience with cloud-native security concepts and threat modeling
• Understanding of AI/ML concepts including model training, inference, data labeling, and adversarial attacks
• Security or cloud certifications (e.g., CCSK, CEH, or AI-focused credentials)
• Microsoft Purview (data governance / DLP)
• Microsoft Defender for Cloud Apps
• Exposure to Power Platform, Power BI, or low-code environments, especially implementing DLP or data governance controls

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance