How to Become a AI Risk & Controls Automation Specialist

Build a FastAPI microservice that intercepts LLM requests and responses, running them through a configurable pipeline of safety checks (PII detection, toxicity classification, keyword blocklists, prompt injection detection). Expose a REST API for integration with any LLM application and include a simple admin dashboard for policy management.

Develop a Python toolkit that systematically tests LLM applications against a curated library of adversarial prompts - including jailbreaks, prompt injections, data extraction attempts, and role-playing exploits. Automate the generation of test cases using mutation techniques and produce structured vulnerability reports with severity ratings.

Build a centralized AI model inventory system where teams register models with metadata (data sources, intended use, risk tier, evaluation results). Integrate OPA/Rego policies that automatically gate model promotions based on evaluation scores, documentation completeness, and approval status. Include a dashboard showing compliance posture across the organization.

Create an end-to-end evaluation framework for RAG (Retrieval-Augmented Generation) applications that automatically tests faithfulness, hallucination rates, context relevance, and PII leakage across diverse query types. Use DeepEval or custom metrics, integrate with CI/CD, and generate compliance-ready evaluation reports.

Build a platform that monitors AI system health metrics in real time (via Prometheus/Grafana), automatically classifies anomalies by severity, triggers containment actions (model rollback, feature disable, user notification), and generates structured incident reports for post-mortem analysis. Include playbooks for common AI-specific incident types.

Develop a toolkit that maps EU AI Act requirements for high-risk AI systems to automated compliance checks. Generate conformity assessment evidence packages, produce risk assessment templates pre-filled with system data, maintain a requirements traceability matrix, and integrate with existing CI/CD and model governance pipelines.

Design and implement a monitoring system for multi-agent LLM architectures that tracks inter-agent message flows, validates tool calls against permission policies, detects privilege escalation attempts, and provides a visual trace of agent reasoning chains for security review. Include anomaly detection for unusual agent behavior patterns.

Build a GitHub Action that automatically scans repositories for AI safety issues: hardcoded API keys, unvalidated model inputs, missing safety evaluations, insecure model deserialization (pickle files), and outdated ML dependencies with known vulnerabilities. Publish as an open-source tool with configurable severity thresholds.