[{"data":1,"prerenderedAt":24},["ShallowReactive",2],{"blog-article-ai-model-governance-as-an-application":3},{"id":4,"slug":5,"body":6,"html":7,"title":8,"description":9,"category":10,"tags":11,"author":17,"date":18,"year":19,"month":20,"quarter":21,"status":22,"featured":23},"2026\u002F07\u002Findustry-applications\u002Fai-model-governance-as-an-application","ai-model-governance-as-an-application","\nMost enterprises now have an AI policy. Far fewer have an AI governance **system**. The policy says every model must be inventoried, evaluated, approved and monitored. In practice, the inventory is a spreadsheet, the evaluations are in notebooks, approvals happen in email and monitoring depends on whoever built the model.\n\nThat works for five models. It fails at fifty, and it fails immediately when an auditor or supervisor asks for evidence.\n\n## The workflow behind “AI governance”\n\nThe **AI governance** family in the Atlas treats governance as an operational workflow with a system of record:\n\n1. **Register.** Every model and AI use case gets an owner, a purpose, a risk tier, its data sources and where it is deployed. That includes vendor models, LLM features and internal models.\n2. **Evaluate.** Structured evaluations against defined criteria: accuracy, robustness, bias and fairness, and for LLM features, groundedness and safety. Results are stored as evidence, not screenshots.\n3. **Approve.** Deployment requests route through the right reviewers, such as model risk, security, the business owner and compliance, based on the risk tier. Every decision is recorded.\n4. **Monitor.** Production behaviour is tracked against thresholds. Drift and incidents raise cases with owners.\n5. **Evidence.** Packs for internal audit, the board or supervisors are generated from the record.\n\n## Where AI helps inside the governance application\n\nIt sounds recursive, but it's useful:\n\n- **Summarization** of model documentation and evaluation results for reviewers\n- **Classification** of new use cases into risk tiers, as a suggestion for a human to confirm\n- **Evaluation assistance**, generating test cases and red-team prompts for LLM features\n- **Drafting** evidence-pack narratives from structured records\n\nEvery one of these is a draft for a human. The approval decision is never automated.\n\n## Who uses it\n\n- **Head of AI and the AI platform team:** keep the portfolio visible and deployable.\n- **Model risk managers:** run reviews with consistent criteria.\n- **Risk and compliance officers:** answer supervisors and auditors from one record.\n- **CIO, CDO and CDAO:** see where AI is used, by whom, and at what risk.\n\n## Integrations that matter\n\nModel registries and ML platforms, CI\u002FCD pipelines (so deployment approval is a real gate rather than a formality), the identity provider for reviewer roles, ticketing, and data catalogues for lineage.\n\n## Controls designed in\n\n- Segregation between model owner and approver\n- An immutable decision history\n- Required evidence before approval can proceed\n- Periodic re-review based on risk tier and staleness\n- Role-based access to sensitive evaluation data\n\n## Why it belongs in financial services first\n\nBanks and insurers already run model risk management for credit and pricing models. Generative AI has multiplied the number of “models” and blurred their edges. A governance application extends existing discipline to the new portfolio instead of creating a parallel process.\n\nThe same foundation applies across enterprise operations, government and any organization preparing for AI-specific regulation.\n\n## Starting point\n\nThe fastest start is to take one line of business's AI inventory and move it into the application, with the approval workflow switched on for new deployments only. The [Solution Definition Sprint](\u002Fservices\u002Fsolution-definition-sprint) scopes the delta: your risk tiers, reviewers, evaluation criteria and integrations.\n\nSee the [financial services](\u002Findustries\u002Ffinancial-services) page, search the [Atlas](\u002Fatlas), or [bring us your AI inventory](\u002Fcontact).\n","\u003Cp>Most enterprises now have an AI policy. Far fewer have an AI governance \u003Cstrong>system\u003C\u002Fstrong>. The policy says every model must be inventoried, evaluated, approved and monitored. In practice, the inventory is a spreadsheet, the evaluations are in notebooks, approvals happen in email and monitoring depends on whoever built the model.\u003C\u002Fp>\n\u003Cp>That works for five models. It fails at fifty, and it fails immediately when an auditor or supervisor asks for evidence.\u003C\u002Fp>\n\u003Ch2>The workflow behind “AI governance”\u003C\u002Fh2>\n\u003Cp>The \u003Cstrong>AI governance\u003C\u002Fstrong> family in the Atlas treats governance as an operational workflow with a system of record:\u003C\u002Fp>\n\u003Col>\n\u003Cli>\u003Cstrong>Register.\u003C\u002Fstrong> Every model and AI use case gets an owner, a purpose, a risk tier, its data sources and where it is deployed. That includes vendor models, LLM features and internal models.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Evaluate.\u003C\u002Fstrong> Structured evaluations against defined criteria: accuracy, robustness, bias and fairness, and for LLM features, groundedness and safety. Results are stored as evidence, not screenshots.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Approve.\u003C\u002Fstrong> Deployment requests route through the right reviewers, such as model risk, security, the business owner and compliance, based on the risk tier. Every decision is recorded.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Monitor.\u003C\u002Fstrong> Production behaviour is tracked against thresholds. Drift and incidents raise cases with owners.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Evidence.\u003C\u002Fstrong> Packs for internal audit, the board or supervisors are generated from the record.\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2>Where AI helps inside the governance application\u003C\u002Fh2>\n\u003Cp>It sounds recursive, but it's useful:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Summarization\u003C\u002Fstrong> of model documentation and evaluation results for reviewers\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Classification\u003C\u002Fstrong> of new use cases into risk tiers, as a suggestion for a human to confirm\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Evaluation assistance\u003C\u002Fstrong>, generating test cases and red-team prompts for LLM features\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Drafting\u003C\u002Fstrong> evidence-pack narratives from structured records\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Every one of these is a draft for a human. The approval decision is never automated.\u003C\u002Fp>\n\u003Ch2>Who uses it\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Head of AI and the AI platform team:\u003C\u002Fstrong> keep the portfolio visible and deployable.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Model risk managers:\u003C\u002Fstrong> run reviews with consistent criteria.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Risk and compliance officers:\u003C\u002Fstrong> answer supervisors and auditors from one record.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>CIO, CDO and CDAO:\u003C\u002Fstrong> see where AI is used, by whom, and at what risk.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>Integrations that matter\u003C\u002Fh2>\n\u003Cp>Model registries and ML platforms, CI\u002FCD pipelines (so deployment approval is a real gate rather than a formality), the identity provider for reviewer roles, ticketing, and data catalogues for lineage.\u003C\u002Fp>\n\u003Ch2>Controls designed in\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>Segregation between model owner and approver\u003C\u002Fli>\n\u003Cli>An immutable decision history\u003C\u002Fli>\n\u003Cli>Required evidence before approval can proceed\u003C\u002Fli>\n\u003Cli>Periodic re-review based on risk tier and staleness\u003C\u002Fli>\n\u003Cli>Role-based access to sensitive evaluation data\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>Why it belongs in financial services first\u003C\u002Fh2>\n\u003Cp>Banks and insurers already run model risk management for credit and pricing models. Generative AI has multiplied the number of “models” and blurred their edges. A governance application extends existing discipline to the new portfolio instead of creating a parallel process.\u003C\u002Fp>\n\u003Cp>The same foundation applies across enterprise operations, government and any organization preparing for AI-specific regulation.\u003C\u002Fp>\n\u003Ch2>Starting point\u003C\u002Fh2>\n\u003Cp>The fastest start is to take one line of business's AI inventory and move it into the application, with the approval workflow switched on for new deployments only. The \u003Ca href=\"\u002Fservices\u002Fsolution-definition-sprint\">Solution Definition Sprint\u003C\u002Fa> scopes the delta: your risk tiers, reviewers, evaluation criteria and integrations.\u003C\u002Fp>\n\u003Cp>See the \u003Ca href=\"\u002Findustries\u002Ffinancial-services\">financial services\u003C\u002Fa> page, search the \u003Ca href=\"\u002Fatlas\">Atlas\u003C\u002Fa>, or \u003Ca href=\"\u002Fcontact\">bring us your AI inventory\u003C\u002Fa>.\u003C\u002Fp>\n","AI model governance should be an application, not a policy document","Model inventory, evaluation, deployment approval and monitoring as one governed workflow, so AI governance produces evidence instead of meetings.","industry-applications",[12,13,14,15,16],"ai-governance","financial-services","evaluation","evidence","risk","fazezero-editorial","2026-07-02T00:00:00.000Z",2026,7,3,"published",false,1790080511918]