AI Governance Guide
AI Risk Assessment Guide for Enterprise Workflows
Assess AI risk by workflow, data sensitivity, user impact, autonomy, vendor posture, model behavior, monitoring, and evidence before approving AI software or agents.
Use this as a planning and buyer research structure, not legal advice. Confirm legal, regulatory, contractual, and industry-specific requirements with qualified legal, compliance, and security owners.
Discovery questions
Clarify governance scope before approval
Workflow impact
The same model can be low risk for drafting and high risk for decisions that affect customers, employees, finance, or legal outcomes.
What decision, recommendation, or action will the AI influence?
Data exposure
Risk depends on customer data, employee data, contracts, source code, financial records, health data, and confidential business information.
Which sensitive data can the AI see, store, infer, or expose?
Autonomy level
A draft assistant, analyst copilot, workflow agent, and fully automated action need different approval and rollback controls.
Can the AI only suggest, or can it trigger actions in production systems?
Failure visibility
Assessment should identify whether errors are easy to detect, expensive to fix, or hidden until after customer or regulatory impact.
How quickly can a reviewer detect and correct a bad output?
Control areas
Compare risk controls by evidence
Input and output controls
Define allowed inputs, prohibited inputs, output review rules, logging, and redaction expectations.
What will prevent restricted data from entering prompts or outputs?
Testing evidence
Use historical examples, edge cases, adversarial prompts, quality thresholds, and review samples before launch.
What test set proves the workflow is ready for the intended users?
Human review and escalation
High-risk outputs need named reviewers, escalation criteria, override rules, and evidence capture.
Who reviews exceptions, and what triggers escalation?
Monitoring and incidents
Risk assessment should define what signals are monitored after launch and how incidents are handled.
Which metrics, complaints, failures, or anomalies reopen the risk review?
Decision steps
- 1Assess one workflow at a time; do not approve broad AI use with one generic vendor review.
- 2Score data sensitivity, autonomy, user impact, external exposure, and detectability of failure.
- 3Define required controls before the pilot begins.
- 4Use pilot evidence to confirm or downgrade risk assumptions before production.
- 5Set review triggers for tool changes, data changes, model changes, incidents, and expansion.
Evidence artifacts
- Workflow risk assessment covering impact, data, autonomy, vendor, model behavior, and controls.
- Pilot test set with success, failure, exception, and reviewer-effort results.
- Approval record naming owner, risk tier, required controls, and launch conditions.
- Monitoring plan for quality, cost, security events, user complaints, and exceptions.
- Exception and incident log for outputs that require correction, escalation, or rollback.
Operating models
Choose the right governance depth
Use case risk score
Teams approving many AI experiments quickly.
Standard questionnaire, data class, autonomy level, reviewer requirement, and risk tier.
Watch out: A score without owner judgment can hide critical workflow context.
Control-based assessment
Security, GRC, and compliance teams that need repeatable review.
Controls, owners, test evidence, exceptions, acceptance criteria, and review dates.
Watch out: Controls should map to practical user behavior, not only policy text.
Pilot-gated approval
High-value workflows where real evidence matters more than vendor claims.
Pilot results, failure samples, reviewer notes, cost data, and user feedback.
Watch out: A successful demo is not the same as a controlled pilot.
Related governance guides
FAQ
How do you perform an AI risk assessment?
Assess the workflow, data sensitivity, user impact, autonomy level, vendor posture, model behavior, failure detection, human oversight, testing evidence, monitoring, and incident response before approval.
Should AI risk assessment happen before or after a pilot?
Run an initial assessment before the pilot to define controls, then update it after the pilot with real accuracy, exception, cost, reviewer, and user evidence.
Related buyer paths
Turn governance work into a buying packet
AI Governance Readiness Checker
Score governance readiness across use cases, data rules, vendor review, human oversight, monitoring, policy, and incident response.
AI Software Buyer Guides
Compare AI software categories after governance owners, data risk, and workflow controls are clear.
AI Buying Templates
Use RFP, scorecard, security questionnaire, POC, business case, and governance policy templates.
AI Buying Checklists
Run vendor due diligence, security review, implementation readiness, and governance readiness checks.
AI Cost Guides
Estimate governance, monitoring, implementation, reviewer, audit, and support cost before approval.
AI ROI Guides
Connect governance cost to risk reduction, faster approval, controlled rollout, and renewal evidence.
AI Services Buyer Guides
Evaluate consultants, implementation partners, and enterprise AI advisors when governance work needs outside help.
AI Security Questionnaire Template
Ask vendors about data use, model training, access controls, audit logs, and compliance evidence.
AI Vendor Due Diligence Checklist
Collect risk evidence before buying or expanding an AI vendor.