The AI Security Analyst is responsible for identifying, assessing, and mitigating security risks associated with artificial intelligence (AI), machine learning (ML), and data-driven systems. This role bridges traditional cybersecurity with emerging AI-specific threats, ensuring AI systems are secure, compliant, resilient, and trustworthy across their lifecycle.
The ideal candidate has a strong foundation in cybersecurity, understands AI/ML architectures and data pipelines, and can translate technical risk into actionable guidance for engineering, product development, and leadership teams.
Additional Job Description:
- Assess security risks across AI/ML models, training data, pipelines, APIs, and deployment environments
- Identify and mitigate AI-specific threats such as data poisoning, model inversion, prompt injection, model theft, and adversarial attacks
- Design, and implement security controls for AI systems, including access controls, monitoring, and anomaly detection
- Develop a systemic vulnerability monitoring process based on custom and third-party agents, and other AI models, tools, and applications used within Sensata’s architecture
- Conduct threat modeling and risk assessments for AI-enabled applications
- Support secure-by-design practices throughout the AI lifecycle (design, training, testing, deployment, and retirement)
- Contribute to AI security standards, policies, and guardrails aligned with organizational risk tolerance
- Monitor AI systems for misuse, abuse, and anomalous behavior
- Support incident response investigations involving AI systems or automated decision-making
- Develop playbooks for AI-related security incidents
Additional Experience/Qualifications:
- Bachelor’s degree in Cybersecurity, Computer Science, Engineering, or equivalent experience
- Strong experience in cybersecurity, application security, cloud security, or related fields
- Hands-on experience with AI/ML platforms, frameworks, or workflows (model training, inference, MLOps)
- Familiarity with AI threat models and attack techniques (adversarial ML, prompt injection, model leakage)
- Experience securing AI in cloud-native environments (containers, CI/CD, managed ML services)
- Knowledge of AI governance, model risk management, or responsible AI principles
- Relevant certifications
- 1+ years experience with: Ticketing systems (Such as Service Now), SIEM tools (such as Cylance, Falcon, or Carbon Black), DLP tools (such as Proofpoint), Vulnerability management Programs and tools which support them (such as Rapid 7, Falcon, or Tanium)
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