AI Governance & Risk Engineering Roadmap

Translate responsible-AI principles into decision rights and a usable front door. Inventory complete systems and use cases, then route proportionate governance through transparent contextual tiers.

• Explain NIST AI RMF Govern, Map, Measure, and Manage as iterative lifecycle functions
• Use the GenAI Profile to identify generative-system risks and actions relevant to context
• Define enterprise policy, standards, risk tolerance, prohibited uses, and escalation
• Create a federated RACI for business, product, engineering, data, security, privacy, review, incident, and risk acceptance
• Separate implementation, independent challenge, approval, exception, and audit responsibilities
• Inventory use cases, users, affected parties, data, models, vendors, agents, tools, autonomy, geography, and lifecycle
• Detect shadow AI through procurement, network, identity, code, survey, and business-process signals
• Tier by contextual impact, reversibility, decision influence, autonomy, sensitivity, exposure, scale, uncertainty, and control strength
• Define noncompensable escalation triggers and preserve reasoned human override
• Track ownership, review dates, material-change triggers, suspension, and retirement

Evidence outcome: governance charter, RACI, policy hierarchy, risk tolerance, inventory schema, three synthetic use cases, tiering rationale, and review-routing table.

Map the socio-technical context before selecting controls. Treat the model as one dependency among data, retrieval, tools, people, policy, vendors, interfaces, and operations.

• Document intended use, benefits, prohibited use, foreseeable misuse, assumptions, and alternatives
• Identify users, affected people, vulnerable groups, accessibility needs, and routes to recourse
• Map data flow, provenance, permission, transformations, quality, representation, retention, deletion, and telemetry
• Create model, system, data, evaluation, vendor, and operational documentation with version identifiers
• Assess fairness, privacy, security, safety, transparency, human oversight, reliability, and societal context
• Identify model, RAG, agent, tool, identity, supply-chain, output-consumer, and human-workflow threats
• Evaluate vendors for data terms, assurance, versions, changes, incidents, resilience, transparency support, rollback, and exit
• Use the ISO/IEC 42001 public overview only to understand management-system and continual-improvement concepts
• Map OECD values-based principles to original organizational control objectives
• Create dated regulatory worksheets from official sources and route applicability questions to qualified counsel

Evidence outcome: impact assessment, data-flow and trust-boundary diagrams, system card, data record, vendor review, risk register, stakeholder record, and non-legal regulatory worksheet.

Turn risk statements into reproducible tests. Evaluate the application in its context, inspect concentrated failures, and red-team complete workflows rather than only model prompts.

• Build versioned representative, edge, failure, misuse, abstention, and adversarial synthetic datasets
• Define task, groundedness, citation, tool, oversight, reliability, latency, and cost metrics
• Choose fairness slices and metrics from affected outcomes, base rates, and error consequences
• Test privacy minimization, tenant isolation, memorization or disclosure, retention, and deletion
• Apply OWASP LLM and GenAI risks to prompt injection, disclosure, supply chain, poisoning, output, agency, RAG, misinformation, and consumption
• Use MITRE ATLAS tactics and techniques to design threat-informed exercises and detections
• Use Google SAIF to connect AI threat discovery to security controls and operational defenses
• Test tool identity, schemas, resources, destinations, budgets, approval, idempotency, and emergency stop
• Assess whether human reviewers have information, time, authority, training, and accessible appeal routes
• Document uncertainty, severe failures, tradeoffs, limitations, and residual risk after mitigation

Evidence outcome: evaluation plan and report, subgroup analysis, privacy tests, threat model, authorized red-team report, remediations and retests, oversight usability study, and residual-risk update.

Connect policy and risk to implemented, tested controls. Gate releases using exact evidence, independent challenge, explicit exceptions, and practiced reversibility.

• Define control objectives with owners, implementation, expected result, test, threshold, and evidence expiry
• Link requirement or policy to risk, control, test, result, finding, exception, approval, release, and residual-risk decision
• Protect evidence integrity with versions, environment IDs, checksums, attribution, access, and retention
• Use noncompensable gates for severe privacy, security, safety, authorization, and oversight failures
• Require challenge and approval independence proportionate to the use-case tier
• Make exceptions narrow, time-bound, compensated, attributable, monitored, and automatically expiring
• Bind approval to exact model, data, prompt, retrieval, tool, policy, test, and release versions
• Stage releases through offline, shadow, canary, and limited rollout where appropriate
• Define kill-switch ownership, rollback versions, recovery criteria, and emergency-change review
• Record accepted residual risk, dissent, safer alternatives, and reasons not to deploy or to narrow scope

Evidence outcome: control-evidence graph, assurance case, release scorecard, findings and exceptions, independent review, signed decision record, staged rollout plan, kill-switch test, and rollback proof.

Govern what happens after approval. Detect changes and real outcomes, provide useful transparency and recourse, respond to incidents, and use evidence to improve or retire the system.

• Give users timely AI notice, purpose, material limitations, data information, source cues, human help, and feedback
• Provide accessible correction, complaint, escalation, and appeal paths where appropriate
• Monitor service health, cost, AI quality, subgroup behavior, control denials, drift, complaints, incidents, and outcomes
• Assign every metric an owner, threshold, segmentation, response playbook, and retention rule
• Detect material model, alias, data, index, prompt, filter, tool, identity, vendor, user, geography, autonomy, and purpose changes
• Preserve necessary evidence while containing AI incidents and coordinating product, security, privacy, vendor, communication, and counsel processes
• Run tabletops for prompt injection, cross-scope disclosure, poisoning, provider outage, control bypass, and harmful output
• Measure time to detect, contain, recover, correct, notify through approved process, and close lessons
• Report coverage, effectiveness, outcomes, exception age, incident trends, and residual risk above tolerance
• Reassess, suspend, narrow, roll back, improve, or retire based on current evidence

Evidence outcome: transparency note, monitoring catalog, dashboards, change records, incident tabletop, corrective actions, appeal metrics, management review, continual-improvement plan, and cleanup verification.