Practical checklists and governance patterns to manage AI privacy, model risk, explainability, and human oversight.
An actionable, fillable pre-deployment and periodic review checklist to evaluate data provenance, privacy, model risk, explainability, human oversight, monitoring, vendor risk, retention, and incident readiness. Includes a simple risk-tier rubric, owner and review fields, and a notes section so teams can save a documented decision and follow up on mitigations.
Practical guardrails, retrieval and prompt patterns, provenance practices, human-in-the-loop handoffs, evaluation metrics, privacy controls, and a brief rollout runbook for building retrieval-augmented assistants that support organizational knowledge work.
A practical, actionable guide for designing retrieval‑augmented, role‑aware assistants that surface institutional knowledge safely. Includes architecture patterns, prompt templates, provenance and explainability approaches, guardrails to reduce hallucination and data leakage, update/retirement workflows, monitoring metrics, escalation rules, and two concrete example flows (employee onboarding Q&A and incident triage).
A pragmatic, step-by-step assessment template teams can use before building, buying, or deploying AI or automated decision tools. Includes clear sections, suggested questions, evidence to collect, a lightweight scoring rubric, sign-off criteria, and post-deployment monitoring guidance that organizations can adapt.
An actionable, savable checklist teams can use to assess readiness before introducing AI-assisted tools into workflows. Includes fields to capture approvals, effort estimates, risk scoring, ownership, and notes to support prioritization and follow-up.
A practical, reusable matrix-style checklist teams can use to evaluate privacy, bias, explainability, data minimization, security, human oversight, vendor risk, and operational safety before advancing an assistant or automation from pilot to production. Includes concrete evidence examples, rating guidance, sample mitigations, and suggested production thresholds.
An actionable, evidence-oriented checklist that teams can use to assess AI projects for privacy, fairness, explainability, monitoring, human oversight, vendor risk, and incident readiness. The form captures owners, evidence, priorities, and recommended next steps so teams can operate, track, and improve responsibly.
A practical, action-oriented checklist to evaluate privacy, consent, retention, access, bias, provenance, and operational risks when datasets are repurposed for organizational learning, analytics, or AI. Includes guidance on how to use the checklist, suggested mitigations, and a simple action template for assignment and follow-up.
A practical, reusable template to document a model's purpose, scope, inputs, outputs, data lineage, architecture, performance, known limitations and failure modes, observed biases, interpretability techniques, guardrails, monitoring and remediation plans, human-in-the-loop checkpoints, vendor and privacy notes, and audit history. Includes a completed example for a simple summarization model used in knowledge search.
A short script and flowchart for handling ambiguous or risky assistant responses—when to surface sources, ask for human review, or route to legal/privacy teams.
A practical dashboard specification and playbook to observe agent usage, detect reliability and safety issues early, run rolling human audits, measure provenance coverage, record sensitive-data attempts, and trigger remediation workflows with clear ownership.