AI lifecycle risk management

The ai lifecycle risk management requirement under ISO/IEC 42001 expects you to identify, assess, treat, and track AI risks across every lifecycle stage, from design and data collection through deployment, monitoring, change, and retirement ¹. Operationalize it by running lifecycle-gated risk assessments, keeping an AI risk register, and retaining evidence that mitigations were implemented and re-evaluated after changes.

Key takeaways:

• Treat AI risk as a lifecycle control, not a one-time model review ¹.
• Maintain an AI risk register with mitigations by lifecycle stage as your audit backbone ¹.
• Build release gates so high-risk issues block deployment until risk is treated and approved.

ISO/IEC 42001 is an AI management system standard. Even if you are not pursuing certification, its control intent maps cleanly to what regulators and customers increasingly expect: repeatable governance, traceable decisions, and evidence that risks were assessed and addressed across the AI system’s lifecycle ¹. This page translates the requirement “assess and mitigate risks across AI lifecycle stages” into requirement-level execution guidance a CCO, GRC lead, or compliance operator can run with immediately ¹.

The operational challenge is rarely “do we have a risk framework.” The failure mode is scope and proof: teams assess risk at model build time, then ship quietly, swap data sources, change prompts, fine-tune, or add an agent toolchain, and never revisit the risk posture. ISO 42001’s lifecycle framing forces you to put controls where change happens: intake, design, data, training, evaluation, deployment, monitoring, incident response, and retirement ¹.

Below is a practical implementation pattern: lifecycle stage definitions, stage-gated risk assessments, a single risk register, mitigation ownership, and evidence you can hand to auditors, customers, or internal governance.

Regulatory text

Provided excerpt (non-licensed summary): “Baseline implementation-intent summary derived from publicly available framework overviews; licensed standard text is not reproduced in this record.” ¹
Requirement summary: “Assess and mitigate risks across AI lifecycle stages.” ¹

What the operator must do:
You must implement a repeatable method to (1) identify AI risks, (2) assess their likelihood/impact in your context, (3) select and implement mitigations, and (4) track residual risk and re-assess when the AI system or its operating context changes, across all lifecycle stages ¹. Auditors will look for objective evidence that this is systematic, not ad hoc: defined lifecycle stages, defined risk criteria, an accountable owner, and records showing actions were taken.

Plain-English interpretation

This requirement means: every AI system needs a living risk file. You do not get credit for a single “model risk assessment” stored in a folder if the system changes after launch. Your job is to make sure risk work follows the system through:

• Initiation/intake: why you’re building/buying it; what it will be used for.
• Design & data: training data, inputs/outputs, intended users, and constraints.
• Build/configure: training, fine-tuning, prompt design, tool use, and integrations.
• Evaluation: test results tied to risk claims (safety, privacy, bias, robustness).
• Deployment: access control, human oversight, fallback plans, user disclosures.
• Operations: monitoring, incident handling, drift/change management.
• Retirement: decommissioning, data retention, and lessons learned.

Your “definition of done” is not a policy. It is a chain of evidence that risks were assessed and treated at the right points.

Who it applies to (entity and operational context)

Applies to:

• AI developers building or substantially modifying AI systems (including fine-tuning and agent workflows) ¹.
• AI system operators deploying and using AI in business processes, even if the model is third-party provided ¹.

Operational contexts that trigger real work:

• Customer-facing AI (support, underwriting, screening, recommendations).
• Employee decision support (HR, performance, legal review, security triage).
• Use of third-party models or AI platforms where you still control prompts, tools, data, or downstream decisions.
• High-change environments (frequent model releases, prompt updates, new data feeds).

If you outsource development, you still need operator-side lifecycle risk management: intake due diligence, acceptance testing, monitoring, and change control for the third party service.

What you actually need to do (step-by-step)

Step 1: Define your AI lifecycle stages and governance gates

1. Publish lifecycle stages that match how engineering ships AI (intake → design → build → evaluate → deploy → operate → retire).
2. Set “gates” where risk artifacts are required before moving forward (example: no production deployment without a completed evaluation and approved residual risk).
3. Assign accountability: name the control owner (often AI governance lead), and name per-system owners (product/engineering).

Deliverable: Lifecycle standard + gate checklist.

Step 2: Establish a single AI risk register, structured by lifecycle stage

Build one system of record (spreadsheet, GRC tool, or Daydream) with minimum fields:

• System name, owner, business purpose, users, data categories, deployment environment
• Lifecycle stage
• Risk statement (cause → event → impact)
• Inherent risk rating and rationale
• Controls/mitigations selected
• Residual risk rating and acceptance criteria
• Action owner, due date, status
• Evidence links (tests, approvals, monitoring dashboards)
• Re-assessment trigger (what changes force review)

This implements the recommended control: “Maintain AI risk register and mitigation plans by lifecycle stage.” ¹

Step 3: Standardize your AI risk assessment method (scoring + criteria)

Keep it simple enough that teams will use it:

• Risk taxonomy: privacy, security, safety, fairness, transparency, reliability, legal/regulatory, operational resilience, third-party dependency.
• Scoring: define likelihood/impact levels and what “high” means in your business.
• Acceptance rules: who can accept residual risk at each level (product owner vs. risk committee).

Avoid false precision. Auditors prefer consistent application over fancy math.

Step 4: Run lifecycle-stage assessments with concrete “what changed?” prompts

Use stage-specific questions so assessments stay grounded:

• Intake: intended use, prohibited uses, decision criticality, user population.
• Data: provenance, rights to use, sensitive data, labeling quality, retention.
• Build/config: prompt templates, tool permissions, secrets handling, sandboxing.
• Evaluation: test plan mapped to risks; red-teaming scope; known limitations.
• Deployment: access control, logging, user notices, human review checkpoints.
• Operations: monitoring signals, incident playbooks, drift and feedback loops.
• Retirement: decommission plan; data retention and deletion; model artifacts archiving.

Step 5: Link each material risk to a mitigation plan you can verify

A mitigation plan needs:

• Control description (what will be implemented)
• Owner (who is responsible)
• Verification method (how you’ll prove it works)
• Completion evidence (what artifact will exist)

Examples:

• Prompt/tooling restrictions documented and enforced by policy-as-code.
• Human-in-the-loop steps for high-impact decisions with documented criteria.
• Monitoring alerts with runbooks and on-call ownership.

Step 6: Embed change management and re-assessment triggers

Most AI risk failures happen after “minor changes.” Define triggers such as:

• Model version change, fine-tune, or prompt template change
• New tool/function access (agents)
• New data source, feature change, or expanded user group
• Incident, near miss, or policy exception
• Material performance drift or new misuse pattern

Operationalize with a release checklist that asks: “Does this change trigger lifecycle risk re-assessment?” If yes, block release until updated.

Step 7: Make it auditable: approvals, exceptions, and traceability

• Risk acceptance approvals: capture decision, approver, rationale, conditions.
• Exceptions: time-bound, documented, compensating controls, re-review date.
• Traceability: tie risks to tests, tests to results, results to deploy approval.

Daydream fits naturally here as the system of record for the AI risk register, evidence links, approvals, and renewal workflows, especially when you need to show lifecycle-stage coverage without chasing documents across teams.

Required evidence and artifacts to retain

Auditors typically want “show me” proof. Retain:

• AI lifecycle policy/standard and gate checklists ¹
• AI system inventory with owners and lifecycle stage
• AI risk register with mitigations by lifecycle stage ¹
• Risk assessment records (initial + re-assessments) and scoring criteria
• Evaluation artifacts: test plans, results, documented limitations
• Deployment approvals, risk acceptance sign-offs, exception logs
• Operational monitoring artifacts: dashboards, alerts, incident tickets, post-incident reviews
• Change management records linking releases to re-assessments
• Retirement/decommission records and data disposition confirmation
• Third party due diligence records for externally provided models/services

Common exam/audit questions and hangups

Expect these:

• “Show the lifecycle stages you use and where risk is assessed.”
• “Pick one production AI system. Walk through risk identification, mitigations, approvals, and monitoring evidence.”
• “How do you ensure changes trigger re-assessment?”
• “Where is residual risk accepted, and by whom?”
• “How do you manage risk when the model is provided by a third party?”
• “How do you prove mitigations are operating, not just designed?”

Hangups auditors flag:

• No consistent lifecycle definition across teams.
• Risk register exists, but it is not updated post-deployment.
• Controls listed as mitigations without evidence they were implemented.

Frequent implementation mistakes and how to avoid them

1. Mistake: Treating lifecycle risk as a one-time launch deliverable.
   Fix: Put re-assessment triggers into change management and release gates.

2. Mistake: Risks written as vague themes (“bias risk”).
   Fix: Require cause-event-impact statements and a measurable verification method.

3. Mistake: Splitting evidence across tools with no index.
   Fix: Maintain a single risk register with hyperlinks to canonical artifacts.

4. Mistake: No owner for each mitigation.
   Fix: One named accountable owner per mitigation, plus escalation for overdue items.

5. Mistake: Ignoring operator risk for third-party AI.
   Fix: Run your own deployment and operations assessments even if the model is externally provided.

Enforcement context and risk implications

No public enforcement cases are provided in the source catalog for this requirement. Practically, lifecycle risk management gaps still create material exposure: unmanaged model changes, missing monitoring, and undocumented approvals are patterns that commonly lead to customer harm, confidentiality events, and governance breakdowns. Treat this requirement as a control to prevent “unknown changes in production,” which is the root cause of many AI incidents.

Practical 30/60/90-day execution plan

Days 0–30: Establish the minimum viable lifecycle program

• Define lifecycle stages and gate checklist aligned to your SDLC.
• Stand up an AI inventory (start with production and near-production systems).
• Create the AI risk register template with required fields.
• Pilot one AI system through intake → evaluation → deploy gate artifacts.

Outputs: lifecycle standard, initial inventory, risk register v1, one completed end-to-end risk file.

Days 31–60: Scale to priority systems and formalize approvals

• Prioritize systems by impact and exposure (customer-facing, high-impact decisions, sensitive data).
• Run risk assessments for the prioritized set; record mitigations and owners.
• Implement risk acceptance workflow and an exception process.
• Add change-management triggers and a release checklist question that blocks deployment without updated risk artifacts.

Outputs: risk register populated for priority systems, documented approvals, change-trigger controls in the release process.

Days 61–90: Prove operations and monitoring

• Define monitoring requirements per risk category (quality, drift, safety, security, misuse).
• Test incident response for an AI failure scenario and retain the record.
• Conduct a lifecycle internal audit: pick a system, trace from risk to mitigation to evidence to re-assessment.
• Decide whether to manage the register/evidence in Daydream to reduce manual follow-up and missing artifacts.

Outputs: monitoring evidence, incident tabletop record, internal audit results, remediation plan for gaps.

Frequently Asked Questions

Do we have to do this for AI we didn’t build (third-party models or SaaS)?

Yes, if you operate the AI in your business process, you still own deployment, monitoring, and change control risk. Treat the third party as an input to your lifecycle risk assessment and retain due diligence evidence.

What is the minimum “risk register” that will satisfy auditors?

A single system of record listing each AI system, its lifecycle stage, material risks, mitigations, owners, and evidence links. Auditors care less about format than consistency and proof that items are kept current ¹.

How do we decide when a change is “material” enough to re-assess risk?

Define triggers up front (model version, prompt template, tool access, data source, user expansion, incidents). If the change could alter outputs, user impact, or controls, treat it as re-assessment-required and document the decision.

Can engineering own this without compliance slowing releases?

Yes, if you embed gates into the existing SDLC and keep assessments short and stage-specific. Compliance should set the standard, approve the scoring/acceptance rules, and audit samples rather than becoming the bottleneck.

What evidence is most often missing during audits?

Proof that mitigations were implemented and operating (test results, config screenshots, monitoring dashboards, approvals), plus evidence of post-deployment re-assessments after changes.

How does Daydream help operationalize this requirement?

Daydream can act as the control center for the AI risk register, required artifacts, ownership, approvals, and re-assessment workflows, so you can demonstrate lifecycle-stage coverage without chasing documents across tools.

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Footnotes

1. ISO/IEC 42001 overview