Secure AI Developer Enablement

Overview

Designed and deployed security and data protection controls for developers using AI coding assistants and platforms. Implemented Guardrails.ai to enforce internal coding standards in AI outputs and integrated Presidio for automated detection/masking of PII in prompts and responses. Leveraged CodeQL and Semgrep for static analysis of AI-generated code, combined with dependency scanning to prevent insecure or unlicensed library use. Built policy-as-code guardrails and developer guidance to prevent IP leakage, privacy exposure, and insecure coding patterns.

Role

Director and lead security engineer responsible for architecture and rollout across engineering teams.

Impact

Enabled safe adoption of AI coding tools across engineering teams without compromising intellectual property, privacy, or compliance. Increased developer productivity by 30% while reducing risk of sensitive data exposure, unlicensed code inclusion, and deviation from corporate coding standards.

Technologies, Frameworks, and Artifacts

• GitHub Copilot governance
• Guardrails.ai
• Presidio data detection
• CodeQL and Semgrep
• Dependency scanning and policy-as-code