Description
- Robust runtime protection for AI systems in production
- Reduced exposure to AI misuse, data leakage, and agent abuse
- Clear alignment to NIST AI RMF, security-by-design, and regulatory runtime expectations
- Defensible governance posture for clients, auditors, and regulators
Responsibilities
- Define and own enterprise AI governance controls focused on runtime security, monitoring, and enforcement for GenAI, LLM, RAG, and Agentic AI systems in production.
- Establish technical standards for runtime threat detection and prevention, covering prompt injection, agent manipulation, inference abuse, data leakage, hallucination exploitation, and unauthorized model access.
- Ensure AI runtime architectures incorporate guardrails, policy enforcement points, and telemetry collection across APIs, orchestration layers, model gateways, and inference pipelines.
- Oversee implementation of continuous monitoring and observability mechanisms, including behavioral monitoring, data and concept drift detection, usage anomalies, and output risk indicators.
- Institutionalize governance requirements for runtime risk response, including alerting thresholds, automated containment, escalation workflows, and integration with enterprise cyber and incident response processes.
- Partner with platform, security, and MLOps/LLMOps teams to embed runtime controls into CI/CD pipelines, model deployment workflows, and API management layers without impacting delivery velocity.
- Define governance expectations for secure AI operation at scale, including access control, rate limiting, logging, explainability at runtime, and auditable control evidence.
- Lead technical governance for high‑risk and regulated AI deployments, ensuring runtime behavior complies with internal policies, client contractual commitments, and global regulatory expectations (e.g., NIST AI RMF).
- Act as the technical advisor for AI runtime risk decisions, advising executive stakeholders and clients on production re
📌 Assistant Vice President (Noida)
🏢 EXL
📍 Noida