Principal Security Architect-GenAI Security &Readi

Principal Security Architect – GenAI Security & Readiness (Data Protection Team)

Top Skills:

• Hands-on experience with GenAI frameworks (LangChain, LangGraph, Langfuse), LLMs (GPT-4o, Claude, Llama 3), and vector DBs
• Understanding of Cyber Security Framework
• IAM & Data Protection understanding
• Microsoft Security; ** Microsoft Copilot; ** Jira/Confluence
• Fluent in Agile;
• Project & Stakeholder Mgmt.
• Demonstrated ability to build strong relationships;
• Critical thinking;
• Attention to detail/detail oriented/organized;
• Self-starter – able to get things done – take the ball and run with it.
• Experience working with senior level leaders – executive presence; Must have excellent communication skills - written and verbal; team player - work well with others – collaborative, able to challenge respectfully.
• CISSP/ CISM/CCSP – preferred but not required.

Position Summary

We are seeking a visionary and technically adept Principal Security Architect to lead GenAI security and readiness initiatives within the Data Protection team. This role will be instrumental in shaping enterprise-wide strategies for securing generative AI platforms, ensuring compliance, and enhancing data protection capabilities across cloud and hybrid environments. The ideal candidate will bring deep expertise in AI/ML systems, cloud-native architectures, and cybersecurity frameworks, with a strong focus on operational readiness and risk mitigation.

Key Responsibilities

• GenAI Security Strategy & Architecture
• Define and implement security architecture for GenAI platforms including LLMs, RAG pipelines, agentic workflows, and inference services.
• Lead threat modeling and risk assessments for GenAI components such as LangChain, LangGraph, Langfuse, and vector databases (e.g., OpenSearch, Milvus).
• Establish governance and guardrails for prompt engineering, model evaluation (RAGAs, G-Eval), and responsible AI practices.

Data Protection & Compliance

• Collaborate with the Data Protection team to integrate GenAI security controls into existing DLP, encryption, and classification frameworks.
• Ensure alignment with regulatory standards (e.g., NIST CSF, ISO 27001, CIS) and support audit readiness for GenAI deployments.
• Evaluate and enforce IAM, data residency, and privacy controls across AWS, Azure, and hybrid environments.

Operational Readiness & Monitoring

• Build observability and monitoring frameworks for GenAI systems using tools like Prometheus, Grafana, ELK, and Langfuse.
• Develop automated pipelines for model deployment, evaluation, and rollback using Kubernetes, Helm, and CI/CD tools (e.g., ArgoCD, CircleCI).
• Lead incident response planning and tabletop exercises for GenAI-related security events.

Cross-Functional Enablement

• Partner with engineering, product, and business units to embed security into GenAI use cases such as chatbots, knowledge assistants, and document summarization.
• Deliver training and awareness programs on GenAI security risks and best practices.
• Represent the Data Protection team in enterprise AI steering committees and working groups.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Cybersecurity, or related field.
• 10+ years of experience in cybersecurity, with 1+ years in AI/ML or GenAI security.
• Hands-on experience with GenAI frameworks (LangChain, LangGraph, Langfuse), LLMs (GPT-4o, Claude, Llama 3), and vector DBs.
• Proficiency in Python, Kubernetes, AWS (Bedrock, Sagemaker), Azure Databricks, and MLOps tools (MLflow, Argo workflows).
• Strong understanding of data protection principles, encryption, and cloud security.

Preferred Qualifications

• Master’s degree in Cybersecurity, AI/ML, or related discipline.
• Certifications: CISSP, CCSP, AWS Machine Learning Specialty, or equivalent.
• Experience in highly regulated industries (e.g., financial services, insurance).
• Familiarity with data governance tools (e.g., Collibra, Alation) and GenAI evaluation frameworks (RAGAs, Guardrails).
• Exposure to agentic AI design patterns and multi-agent orchestration.

Success Measures

• Deployment of secure and compliant GenAI platforms within 6–12 months.
• Reduction in GenAI-related data exposure and model misuse incidents.
• Positive feedback from stakeholders on security enablement and collaboration.
• Demonstrated leadership in GenAI security architecture and readiness planning