[{"data":1,"prerenderedAt":24},["ShallowReactive",2],{"blog-article-operational-risk-on-live-data":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\u002Foperational-risk-on-live-data","operational-risk-on-live-data","\nOperational risk functions are often stuck in a cycle: collect risk and control self-assessments in spreadsheets, consolidate them, report quarterly, repeat. By the time a report reaches the risk committee, the data is weeks old and the links between incidents, risks and controls have been lost along the way.\n\n## The connected model\n\nThe **risk management** family in the Atlas connects the objects risk teams already work with:\n\n- **Risk register:** risks by process, product and entity, with inherent and residual ratings.\n- **Controls:** mapped to risks, with owners and testing results.\n- **Key risk indicators:** thresholds and trends fed from source systems, not typed in.\n- **Incidents and loss events:** captured, classified, investigated and linked to the risks they reveal.\n- **Issues and actions:** remediation with owners, dates and verification.\n- **Assessments:** risk and control self-assessments run as workflows rather than spreadsheets.\n\nWhen these live in one application, questions like “which controls failed before this incident?” or “which risks have deteriorating KRIs and overdue actions?” become queries instead of projects.\n\n## Where AI helps\n\n- **Incident classification:** suggest a taxonomy category, root cause and the linked risks from the incident narrative.\n- **Pattern detection:** surface clusters of similar incidents across business units.\n- **Anomaly detection on KRIs:** flag unusual movements before they breach thresholds.\n- **Summarization:** draft committee papers from the underlying records, clearly marked as drafts.\n- **Assessment support:** pre-fill self-assessment answers from last cycle's evidence for owners to confirm or correct.\n\nRatings and risk acceptance stay with people. The application records when AI suggestions were used and whether they were accepted.\n\n## Who uses it\n\nRisk officers and operational risk teams, business-line risk champions, control owners, internal audit and executive management.\n\n## Integrations\n\nSource systems for KRI data, incident intake from ITSM and security tools, HR for ownership, finance for loss data, and the identity provider for role-based access to sensitive incidents.\n\n## Controls designed in\n\n- Four-eyes review of risk ratings\n- Evidence required for closing actions\n- Restricted visibility for sensitive investigations\n- A complete audit trail of rating changes\n\n## Why now\n\nSupervisors increasingly expect operational resilience: important business services mapped, impact tolerances set and scenarios tested. That is hard to evidence from spreadsheets. A connected risk application makes the mapping explicit and keeps it current.\n\n## First scope\n\nStart with incidents and KRIs for one business line, since that's where live data changes the conversation fastest, then extend to assessments. We'd scope it in a [Solution Definition Sprint](\u002Fservices\u002Fsolution-definition-sprint).\n\nSee [financial services](\u002Findustries\u002Ffinancial-services), explore the [Atlas](\u002Fatlas), or [bring us your risk workflow](\u002Fcontact).\n","\u003Cp>Operational risk functions are often stuck in a cycle: collect risk and control self-assessments in spreadsheets, consolidate them, report quarterly, repeat. By the time a report reaches the risk committee, the data is weeks old and the links between incidents, risks and controls have been lost along the way.\u003C\u002Fp>\n\u003Ch2>The connected model\u003C\u002Fh2>\n\u003Cp>The \u003Cstrong>risk management\u003C\u002Fstrong> family in the Atlas connects the objects risk teams already work with:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>\u003Cstrong>Risk register:\u003C\u002Fstrong> risks by process, product and entity, with inherent and residual ratings.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Controls:\u003C\u002Fstrong> mapped to risks, with owners and testing results.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Key risk indicators:\u003C\u002Fstrong> thresholds and trends fed from source systems, not typed in.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Incidents and loss events:\u003C\u002Fstrong> captured, classified, investigated and linked to the risks they reveal.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Issues and actions:\u003C\u002Fstrong> remediation with owners, dates and verification.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Assessments:\u003C\u002Fstrong> risk and control self-assessments run as workflows rather than spreadsheets.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>When these live in one application, questions like “which controls failed before this incident?” or “which risks have deteriorating KRIs and overdue actions?” become queries instead of projects.\u003C\u002Fp>\n\u003Ch2>Where AI helps\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Incident classification:\u003C\u002Fstrong> suggest a taxonomy category, root cause and the linked risks from the incident narrative.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Pattern detection:\u003C\u002Fstrong> surface clusters of similar incidents across business units.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Anomaly detection on KRIs:\u003C\u002Fstrong> flag unusual movements before they breach thresholds.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Summarization:\u003C\u002Fstrong> draft committee papers from the underlying records, clearly marked as drafts.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Assessment support:\u003C\u002Fstrong> pre-fill self-assessment answers from last cycle's evidence for owners to confirm or correct.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Ratings and risk acceptance stay with people. The application records when AI suggestions were used and whether they were accepted.\u003C\u002Fp>\n\u003Ch2>Who uses it\u003C\u002Fh2>\n\u003Cp>Risk officers and operational risk teams, business-line risk champions, control owners, internal audit and executive management.\u003C\u002Fp>\n\u003Ch2>Integrations\u003C\u002Fh2>\n\u003Cp>Source systems for KRI data, incident intake from ITSM and security tools, HR for ownership, finance for loss data, and the identity provider for role-based access to sensitive incidents.\u003C\u002Fp>\n\u003Ch2>Controls designed in\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>Four-eyes review of risk ratings\u003C\u002Fli>\n\u003Cli>Evidence required for closing actions\u003C\u002Fli>\n\u003Cli>Restricted visibility for sensitive investigations\u003C\u002Fli>\n\u003Cli>A complete audit trail of rating changes\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>Why now\u003C\u002Fh2>\n\u003Cp>Supervisors increasingly expect operational resilience: important business services mapped, impact tolerances set and scenarios tested. That is hard to evidence from spreadsheets. A connected risk application makes the mapping explicit and keeps it current.\u003C\u002Fp>\n\u003Ch2>First scope\u003C\u002Fh2>\n\u003Cp>Start with incidents and KRIs for one business line, since that's where live data changes the conversation fastest, then extend to assessments. We'd scope it in a \u003Ca href=\"\u002Fservices\u002Fsolution-definition-sprint\">Solution Definition Sprint\u003C\u002Fa>.\u003C\u002Fp>\n\u003Cp>See \u003Ca href=\"\u002Findustries\u002Ffinancial-services\">financial services\u003C\u002Fa>, explore the \u003Ca href=\"\u002Fatlas\">Atlas\u003C\u002Fa>, or \u003Ca href=\"\u002Fcontact\">bring us your risk workflow\u003C\u002Fa>.\u003C\u002Fp>\n","Operational risk management that runs on live data, not quarterly spreadsheets","Risk registers, KRIs, incidents and control testing as one connected application, with AI that helps risk teams see patterns earlier.","industry-applications",[12,13,14,15,16],"risk","financial-services","enterprise-operations","governance","evidence","fazezero-editorial","2026-07-30T00:00:00.000Z",2026,7,3,"published",false,1790080511943]