AI Security Specialist
Ascension · United States · Remote
Strong hiring record · median 26 days open
Hiring signal
Actively hiring
Posted 6 days ago · still listed on the employer's own board · How we score
All reasons
In its favour
- Posted 6 days ago
- Still listed on the employer's own board
- Someone has already applied through it
- Location: Remote
- Department: Security
- Schedule: Full-Time | Day Shift
#LI-Remote
- AI security operations: Evaluate, implement, and operate AI security tooling to monitor AI use across cloud and endpoint environments, investigate findings, and improve security visibility.
- AI security investigations: Support investigations of AI security findings and incidents—including over-permissioned agents, sensitive data (e.g., PHI) exposure, and AI application security issues—partnering with Incident Response, engineering, and business teams to assess impact and drive remediation.
- Secure AI development: Partner with AI engineering, Application Security, DevSecOps, infrastructure, and business teams throughout the AI SDLC to review architectures, validate findings, implement data security controls, and enable secure AI development.
- AI security governance and guardrails: Develop AI security guardrails, standards, technical documentation, and reusable frameworks. Assess AI applications against security frameworks (e.g., NIST AI RMF, OWASP Top 10 for LLMs) and ensure compliance with enterprise policies, governance requirements, and applicable regulations.
Education
- High School diploma equivalency with 2 years of cumulative experience OR Associate's degree/Bachelor's degree OR 4 years of applicable cumulative job specific experience required.
Additional Preferences
- Experience and background: 5+ years of cybersecurity experience with hands-on expertise in security investigations, incident response, application security, cloud security, or threat detection.
- AI and software development knowledge: Understanding of large language models (LLMs), AI agents, retrieval-augmented generation (RAG), AI application architectures, and common AI security risks. Familiarity with secure software development practices, AI development workflows, and CI/CD pipelines.
- Cloud and AI platform experience: Experience securing AI solutions in Google Cloud Platform (GCP) and/or Microsoft Azure, including platforms such as Vertex AI or Azure AI Foundry. Familiarity with AI-assisted development tools, cloud-native security controls, identity and access management, and CI/CD pipelines is preferred.
- Relevant certifications: CISSP or AI security-focused certifications are preferred.
Skills
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