IBM watsonx Governance Lifecycle Advisor

Governance fails when everyone assumes someone else owns the risk. You map the roles that touch an AI system, assign clear responsibilities, and separate the people who build from the people who approve.

• Model owners, developers and business owners
• Validators, risk officers and compliance
• Data stewards and security teams
• Separation of duties
• Governance committees and escalation
• Writing a responsibility matrix

You build A responsibility matrix for the lifecycle of one AI use case.

Workflows turn policy into steps people actually follow. You configure stages, tasks, reviews and approvals, route work to the right roles, and keep an auditable record of every decision.

• Stages of an AI use-case workflow
• Tasks, reviews and approvals
• Routing by role and risk
• Notifications and deadlines
• Handling rejected and revised submissions
• Auditing workflow history

You build A configured approval workflow for a new AI use case.

Risk is judged the same way every time or not at all. You identify AI-specific risks, assess likelihood and impact, tier use cases, assign controls, and track inherent and residual risk to a decision.

• Taxonomies of AI risk
• Inherent and residual risk
• Risk questionnaires and tiering
• Controls and mitigations
• Risk appetite and thresholds
• Keeping assessments current
• Configuring a methodology in the platform

You build A risk assessment template that tiers three sample use cases.

You cannot govern what you cannot find. You decide what must be recorded about each model, prompt, dataset and vendor, find unmanaged AI already in use, and capture documentation that a reviewer can rely on.

• What belongs in an AI inventory
• Metadata for models, prompts and datasets
• Third-party and vendor models
• Finding AI no one has registered
• Factsheets as living documentation
• Keeping the inventory accurate

You build An inventory with complete records for five varied AI assets.

Rules differ by use case, sector and region. You read the main regulations and frameworks, map their obligations to specific use cases and controls, and keep evidence of compliance.

• Risk categories under the EU AI Act
• NIST AI Risk Management Framework
• Management system standards for AI
• Sector and privacy rules that overlap
• Mapping obligations to controls
• Recording evidence of compliance

You build A requirements map linking regulations to the controls for one high-risk use case.

A use case is the unit that governance approves. You link models to use cases, move them through proposal, development, validation, deployment and retirement, and record lineage so any result can be traced to its inputs.

• Defining and registering an AI use case
• Linking models and versions to a use case
• Lifecycle stages and gates
• Lineage of data, training and deployment
• Handling model changes and new versions
• Retiring a model properly

You build A registered use case with model versions and lineage traced across its lifecycle.

Approval should rest on evidence gathered before release. You define test data and metrics for quality, fairness and safety, set thresholds, evaluate predictive and generative models, and record the outcome.

• Choosing metrics and thresholds before testing
• Test data that represents real use
• Quality and accuracy metrics
• Fairness testing across groups
• Evaluating generative output for safety and faithfulness
• Recording results as approval evidence

You build A pre-release evaluation report with thresholds and a recommendation.

A model that was fine at launch drifts as the world changes. You set up monitors for quality, fairness and drift, alert on breached thresholds, and define what happens when one fails.

• Monitors for quality, fairness and drift
• Collecting payload and feedback data
• Thresholds and alert routing
• Investigating a breached monitor
• Triggering review, retraining or rollback
• Monitoring generative models in production

You build A monitoring configuration with alerts and a documented response plan.

People affected by decisions deserve reasons, and models need limits. You produce local and global explanations, know what each does and does not show, and apply guardrails for harmful content, personal data and misuse.

• Local and global explanations
• Feature attribution methods in outline
• Explaining a decision to an affected person
• Guardrails on prompts and responses
• Detecting personal data and harmful content
• Limits of explanations and guardrails

You build An explanation and guardrail design for a decision-support model.

Leaders, auditors and regulators each need a different view. You build reports that show what AI is running, how it performs and where risk sits, with the evidence behind each statement.

• What executives need to see
• What auditors and regulators ask for
• Dashboards across the portfolio
• Exporting documentation and evidence
• Reporting incidents and exceptions
• Keeping reports trustworthy

You build A reporting pack for executives and one for auditors.

Take a single use case through every governance step: roles, workflow, risk tier, inventory, legal mapping, evaluation and monitoring. The result is a file that would satisfy a skeptical reviewer.

• Registering the use case and its owners
• Assessing risk and mapping obligations
• Evaluating before approval
• Monitoring after release
• Assembling the governance file

You build A complete governance record for one AI use case, from proposal through monitoring.