Speaks the controls your risk team already knows.
AutoDevOps produces a customer-owned, pre-production evidence trail for AI-assisted code changes — normalized agent events, policy decisions, human approvals, intent-fidelity evidence, provenance queries, PR merge evidence, and tamper-evident evidence packages. Here is how that evidence maps to the control narratives a regulated-finance team evaluates.
What AutoDevOps actually produces
The mappings below are built on concrete artifacts your auditor can open and verify — inside your own cloud. AutoDevOps produces the evidence; it does not certify you against any framework.
Where the evidence lines up
Each mapping is partial by design: the evidence exists today, while customer-cloud validation and auditor review are still required. We name both.
SR 11-7 style model-risk governanceSR 11-7
AI-assisted engineering changes need documented intent, assumptions, review, and exceptions before release — and risk signals monitored over time, not only at one approval point.
Evidence AutoDevOps produces
Still requires you & your auditor
Customer-specific model-inventory linkage, an independent validation workflow, and completed model-risk owner signoff in your environment.
FFIEC / bank technology riskFFIEC
Pre-production changes need identity, approval, evidence of review, and traceability to the agent that performed them — with sensitive actions tied to an actor, policy, and outcome.
Evidence AutoDevOps produces
Still requires you & your auditor
Customer IAM integration, live identity mapping, and validated BYOC deployment in your environment.
DORA ICT change, resilience & third-party riskDORA
AI-assisted development tools should produce evidence of controlled changes, resilient approval paths, and a clear view of reliance on external coding agents.
Evidence AutoDevOps produces
Still requires you & your auditor
Full BYOC deployment automation, completed customer-environment validation, live connector receipts, and resilience runbooks.
EU AI Act governance conceptsEU AI Act
High-impact AI-assisted workflows need human oversight, logging, transparency, and risk management.
Evidence AutoDevOps produces
Still requires you & your auditor
Customer legal classification, role-specific human-oversight procedures, and retention-policy alignment.
ISO 42001 AI management systemISO 42001
An AI management system needs defined roles, risk controls, monitoring, documentation, and traceable records.
Evidence AutoDevOps produces
Still requires you & your auditor
Formal customer AI management-system procedures, completed control-owner matrices, and an approved trust-score use policy.
SOC 2 / ISO 27001 style change managementSOC 2
Changes should be authorized, tested, traceable, and reviewable.
Evidence AutoDevOps produces
Still requires you & your auditor
Customer CI and ticketing integration, and completed live deployed rollout receipts.
This page shows how the current artifacts can support a regulated-finance control narrative, with customer-cloud validation and auditor review still required. It is not legal advice, regulatory advice, an audit opinion, or a claim that AutoDevOps satisfies any framework by itself. Your customer, auditor, model-risk team, and counsel review the final mapping in your own environment.
See the evidence on a real workflow.
A live walkthrough deployed into your own cloud — policy, approvals, audit, and a tamper-evident evidence package your security and model-risk teams can verify themselves.