AI Security Architect
New
6
66degreesAI Transformation
United StatesContractSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years of experience in information security with at least 3 years in security architecture roles
- Required Skills
- Machine LearningGenerative AI
Requirements
- Bachelor's degree in Computer Science, Cybersecurity, Information Systems, or related field; Master's degree preferred
- 8+ years of experience in information security with at least 3 years in security architecture roles
- Demonstrated experience designing security controls for AI/ML systems, including LLMs, generative AI platforms, or machine learning pipelines
- Deep understanding of AI security risks including prompt injection, model inversion, data poisoning, adversarial attacks, and privacy concerns in machine learning
- Strong knowledge of enterprise security frameworks and standards such as NIST CSF, ISO 27001, SOC 2, and data protection regulations including GDPR, CCPA
- Experience with cloud security architectures, particularly in AWS, Azure, or GCP environments
- Familiarity with AI development tools such as OpenAI API, Azure AI Services, AWS SageMaker, or Google Vertex AI
- Relevant security certifications such as CISSP, CCSP, CISM, or specialized AI security certifications
- Experience in the insurance or financial services industry
Responsibilities
- Design and implement comprehensive AI security architecture, including governance frameworks, data protection controls, model security standards, and usage policies for enterprise AI systems
- Collaborate with the AI business deployment team to establish security requirements and controls for AI applications, ensuring alignment between business objectives and security standards
- Develop and enforce AI usage policies and security guardrails for employees, including guidelines for acceptable use of generative AI tools, prompt injection security, and data sharing restrictions
- Establish security standards and best practices for AI/ML development teams, covering model training security, data pipeline protection, API security, and secure model deployment
- Conduct AI-specific threat modeling and risk assessments, identifying vulnerabilities such as prompt injection, data poisoning, model theft, adversarial attacks, and privacy leakage
- Define data classification and handling requirements for AI systems, ensuring sensitive customer information and personally identifiable information are appropriately protected in training datasets and model outputs
- Evaluate and recommend AI security tools and technologies, including data loss prevention for AI interactions, AI gateway solutions, model monitoring platforms, and security testing frameworks
- Partner with compliance and legal teams to ensure AI implementations meet regulatory requirements including insurance industry regulations, data privacy laws, and emerging AI-specific legislation
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