Playbook: Compliance & Legal — Auditing AI Systems

Practical templates, checklists, and rubrics to help compliance, legal, and risk teams evaluate AI systems, document decisions, and recommend remediations that are grounded in use‑case context.

Why audit AI systems now?

AI is being embedded into everyday work—from chat assistants used by small businesses to predictive models in hospitals and factories. Audits help teams move beyond assumptions and demonstrate that an AI system has been evaluated for legal, regulatory, privacy, safety, and fairness concerns that matter to your organization. Good audits reduce surprises, support clearer deployment decisions, and create a record for internal governance and external scrutiny.

What you'll learn and accomplish

This playbook helps you run focused, repeatable audits. You will learn how to:

• Map the regulatory and contractual requirements relevant to a specific AI use case.
• Use checklists and scoring rubrics to evaluate data quality, model behavior, explainability, and operational controls.
• Document findings in a concise audit report and recommend practical remediation steps and acceptance criteria.

Who benefits

Primary beneficiaries include compliance officers, in‑house counsel, risk managers, privacy teams, and auditors at organizations of any size that are deploying or procuring AI. Practical examples:

• Healthcare compliance teams validating a triage model for clinical safety and data minimization.
• Manufacturing operations and safety managers checking predictive maintenance models for unexpected failure modes and human override processes.
• Nonprofits and social services teams auditing screening models for bias and transparency before public deployment.
• Small service firms assessing a customer‑facing assistant for privacy, disclosure, and escalation rules.

What's in this playbook

The resource collection includes a Legal & Regulatory Mapping Checklist, audit templates, scoring rubrics for common risk domains (privacy, fairness, safety, robustness), and reporting formats that help you present findings to stakeholders. Each template is designed to be copied and tailored to your organization’s policies, risk appetite, and applicable laws.

How to use it in practice

Start with a short scoping session to define the AI system’s purpose, users, and data sources. Run the mapping checklist to identify which laws and contracts apply. Use the relevant rubrics to score risks, record evidence, and draft a report that ties recommended mitigations to deployment decisions. Invite cross‑functional stakeholders—engineering, product, privacy, and business owners—into the audit huddle so issues are validated and remediations are actionable.

Platform affordances and good adoption patterns

The playbook is intentionally modular so teams can copy templates into their own Hunger Engine, tailor checklists to local context, and save completed audits. Consider turning templates into interactive audits (saved forms and JSON records) to build organizational memory and track remediation over time. Use audits as living artifacts: revisit them after model updates, data drift, or changes in regulation.