Engineering an Enterprise AI Security Platform Across Apps, Employees, and Agent

Engineered token-level cost analysis, prompt-efficiency assessment, and model-selection logic to identify inefficient usage patterns and reduce avoidable AI expenditure.

Developed trace-level monitoring and AI evaluation methods to assess model responses, latency, token consumption, cost, factual accuracy, and hallucination patterns.

Engineered AI red teaming and adversarial testing workflows to assess LLMs, RAG pipelines, and AI agents against prompt injection, jailbreaks, system-prompt extraction, and other AI-specific attack scenarios.

Engineered contextual data redaction and session-level value restoration to protect personal, medical, and financial information while preserving conversational context.

Developed AI tool discovery and endpoint monitoring, delivered through a lightweight browser extension and desktop agent, to identify unapproved applications, surface workforce AI usage patterns, and enforce policies before data loss.

Engineered prompt and response inspection, specialized threat detection, and policy-driven enforcement to address prompt injection, jailbreaks, toxic content, and sensitive data exposure.

Developed identity-linked authorization for AI agent workflows and connected tools, applying granular discovery, read, write, and execute permissions to restrict unauthorized agent actions.

Engineered audit logging to record detections, policy decisions, enforcement actions, authorization events, and AI security activities, supporting investigations and internal reviews through AI compliance and audit automation.

Engineered pipelines that structure threat signals, scanner results, and security events to identify recurring attack patterns, investigate suspicious activity, and inform improvements to AI security policies.