Responsible AI Manager
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- Build accountable governance and measurable model-impact evidence
- Operationalize evaluation, red teaming and lifecycle risk controls
- Govern agentic/frontier risk and enterprise assurance at scale
Benchmark: Google + OpenAI + NIST/DeepMind (Sep 2026): operational AI governance, risk reviews/tiering, pre-launch testing, eval evidence, agentic/frontier risk, monitoring, incident controls + regulatory assuranc Mastery ladder: 2 job-market competencies per level; 2 substantive expert VIDEO bridges per competency (12 total).
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Enrichment
Build accountable governance and measurable model-impact evidence
AI governance frameworks, risk tiering + accountable ownership
Coverage: Responsible AI / Governance / Risk Management
Fairness, transparency + model-impact assessment
Coverage: Responsible AI / Fairness / Model Assessment
Operationalize evaluation, red teaming and lifecycle risk controls
Safety evaluation, red teaming + mitigation guardrails
Coverage: AI Red Teaming / AI Evals / Safety Testing
Lifecycle risk controls, monitoring + incident readiness
Coverage: AI Risk / Security / Privacy / Monitoring
Govern agentic/frontier risk and enterprise assurance at scale
Agentic/frontier risk, adversarial testing + reliable evals
Coverage: Agentic Safety / AI Red Teaming / Evals
Enterprise assurance, AI RMF profiles + executive governance
Coverage: AI Governance / Assurance / Executive Risk
Role-Readiness Proof / Capstone
Build an enterprise Responsible AI operating system for a high-impact generative/agentic use case: AI inventory and risk tier; owners/RACI; impact assessment and intended-use boundaries; fairness/robustness/privacy/security evidence; pre-launch eval and independent red-team plan; agent/tool threat model; mitigations and release gates; production monitoring, drift/incident/escalation controls; evidence register; regulatory/standards mapping; and executive launch decision dashboard.
Proof Rubric
- Make governance executable rather than aspirational: connect each material risk to an owner, metric, evaluation, control and decision threshold
- distinguish model, system and use-context failures
- document residual risk and evidence quality
- test adversarial/agentic failure modes
- define post-deployment monitoring and incident response
- make launch, restriction or rollback decisions auditable