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Every new rule lands as requirements. Prove every one — on demand.
Regulators have moved from guidance to enforcement, and the question never changes: show me. Modern Requirements gives financial institutions one governed requirements thread inside Azure DevOps — from regulatory obligation to implemented control to test evidence — so the answer to the examiner, the auditor, and the second line is a live record, not a reconstruction.
Modern Requirements4DevOps · Copilot4DevOps · Agents4DevOps — deployed in your tenancy or on-premises with Azure DevOps Server, inside your data-residency boundary.
✓ Obligation → requirement → control → test → evidence: one thread, provable on demand.
Coverage computed from live link data — the exam answer, before the exam.
The challenge
Enforcement has started. The evidence hasn't caught up.
Financial institutions run the most regulated software estate in the private sector — and the record connecting regulation to requirement to implemented control is, at most institutions, a mapping spreadsheet, a policy PDF, and a change log that meets them only during exam prep.
of firms passed all DORA register data-quality checks
In the ESAs' dry run of nearly 1,000 firms. Incomplete registers became the leading cause of supervisory letters in the first enforcement cycle.
EU AI Act high-risk obligations apply — 2026
Credit scoring is named high-risk. Risk management, data governance, documentation, logging, and human oversight — covering existing production models, not just new ones.
to report a major ICT incident under DORA
The clock is short, and the report needs facts: which services, which systems, which controls. A record you can query answers in hours. A reconstruction doesn't.
of global turnover — the AI Act ceiling for high-risk failures
Up to €15M or 3% for Articles 9–17 non-compliance — separate from, and stacking with, DORA and GDPR penalties. ICT weaknesses can also drive Pillar 2 capital add-ons.
Every new rule lands as requirements across a dozen systems
DORA, the AI Act, PCI DSS 4, NYDFS amendments, operational resilience regimes — each arrives as obligations that must become requirements, allocated across platforms, implemented, tested, and evidenced. When that translation happens in spreadsheets, every regulation restarts the same manual project — and no one can say with confidence where the last one actually landed.
Examiners expect written evidence, not verbal assurances
Three lines of defense, SOX ITGC testing, internal audit cycles, supervisory exams — all sampling the same question: show the change, the approval, the test, the release. At most institutions each request triggers a war room, because the evidence exists but the links between the pieces don't. The finding isn't that the control failed. It's that nobody could prove it worked.
SR 11-7 covers your LLMs. The AI Act covers your production models.
Regulators have made it explicit: model risk management applies to all models, including AI tools and third-party services — and institutions that can't produce a model inventory with risk assessments on request are receiving findings. Now the AI Act layers conformity documentation, logging, and human-oversight evidence on top. Model governance run in documents was straining before. It doesn't survive this.
Core modernization, payments migration — with the ledger running
Core banking replacement, ISO 20022 migration, platform re-architecture — the industry's largest change programs run against systems that move money every second. When requirements live in one tool, code in another, and tests in a third, the impact analysis behind every release is a best guess — and the deviations that slip through are the ones the incident report finds.
Everything on this page runs on three Modern Requirements products
All native to Azure DevOps — cloud in your tenancy or Azure DevOps Server on-premises, inside your data-residency boundary. Start with the foundation; add the layers as your program needs them.
Modern Requirements4DevOps
The governed requirements record: obligation-to-control Smart Docs, live traceability, release baselines, change control with segregation of duties, review management with e-signatures.
See the foundation → The AI layerCopilot4DevOps
Governed AI on that record: decompose obligations into requirements, score quality, assess change impact — with BYO-LLM and data-residency options that clear a bank's review.
See the AI layer → The agents layerAgents4DevOps
Agents that run the routine work — evidence patrols across changes and controls, coverage checks before release, exam package assembly — under policy controls, with human sign-off.
See the agents layer →Next: the ICT risk loop, the regulatory calendar, and the clock running on both.
Next: The lifecycle →The lifecycle
Compliance in finance isn't a project. It's a loop under supervision.
Two lifecycles govern a financial institution's software: the ICT risk loop the regulators now supervise continuously, and the regulatory calendar that adds a new obligation every cycle. Both keep asking the same question — show me the obligation, the control, and the evidence — and both get their answer from the same governed record.
ICT risk
management
Identify critical & important functions
Derive requirements & tolerances
Implement under change control
Test resilience (TLPT)
Detect, respond & report (24h)
Learn, remediate & oversee third parties
The loop the supervisor now sits inside
DORA turned operational resilience from a periodic exercise into continuous supervision — registers cross-checked automatically, incident clocks measured in hours, TLPT findings tracked to closure, and ICT weaknesses feeding directly into SREP and capital. The loop never closes, and the regulator watches it turn.
In Modern Requirements, every station is a governed work item: the critical function mapped to the requirements that protect it, the impact tolerance to the test that exercised it, and the remediation to the evidence that closed it. When the 24-hour incident clock starts, the question "which services, which systems, which controls" is a query — not a conference call.
The regulatory calendar, 2025 → 2027
Every card below starts or advances a compliance clock — and the nearest one is days away. A record you can query is the difference between staging the new requirements and starting the program over.
DORA applies
Full applicability for virtually every regulated EU financial entity
Critical providers designated
First CTPP list — 19 providers under direct EU oversight
Register of Information
2026 ROI cycle submitted — dry-run pass rate was 6.5%; supervisors are cross-checking
EU AI Act high-risk
Credit scoring obligations live — covering existing production models
TLPT cycle
Threat-led penetration testing at least every 3 years for designated entities
SREP supervision
ICT risk weaknesses can drive Pillar 2 capital add-ons
Why the record matters more than any single rule: enforcement has moved from paperwork to proof — registers cross-checked automatically, exam findings issued for inventories that can't be produced on request, and the AI Act about to add conformity evidence on top. Institutions that keep obligations, requirements, and evidence on one live thread don't re-platform when a rule lands; they run a gap query, stage the new requirements, and let the same workflows carry them to the deadline.
Next: the five governed workflows that run financial software delivery day to day.
Next: Workflows →Governed workflows
Five workflows that run regulated financial delivery
Each workflow is enforced inside Azure DevOps. Every state has an owner, every transition has a rule, and every approval is logged with an immutable audit trail. They ship as the banking & finance configuration of Modern Requirements4DevOps.
Regulatory change → requirements
From published rule to allocated requirement with nothing lost in translation. Obligations are captured as structured items with their source clause, decomposed into requirements with trace intact, and allocated across the platforms they touch — so "where did that regulation land?" has a computed answer, permanently.
Obligation captured
Rule clauses as items with source and applicability
Requirements derived
Decomposed, quality-scored before review
Allocated
Across systems and teams, interfaces noted
Validated
Right requirement, right obligation — e-signed
Implemented
Commits and configs linked with IDs
Baselined
Obligation coverage computed live, gap-queryable
Change & release governance
The change trail your auditors sample, built by construction. Every change links to the requirements it modifies, impact is computed from live links, approval is segregated from authorship and e-signed, and the release ships with its evidence attached — so the sample request is a filtered view, not a war room.
Change raised
Linked to the items it proposes to modify
Impact computed
Suspect links across requirements, controls, tests
Approved
Segregation of duties enforced; e-signed with rationale
Implemented
Commits linked; flagged tests re-run
Evidence attached
Test results and sign-offs on the change record
Released & baselined
Complete change record; difference report generated
Model & AI governance
Model risk management extended to the AI estate — as a governed thread instead of a document set. Every model and AI system is an inventory item with derived requirements for data governance, oversight, and performance; validation and monitoring evidence links at execution depth; and the AI Act's documentation set generates from the record.
Inventoried
Model / AI system registered with risk classification
Requirements derived
Data governance, oversight, accuracy — per Arts. 9–15
Independently validated
Evidence linked; effective challenge documented
Approved for use
Deployment decision e-signed with conditions
Monitored
Performance and drift tracked; logging evidence current
Revalidated
The loop re-enters with full history on one thread
Operational resilience testing
Impact tolerances as first-class requirements, exercised on schedule. Critical functions map to the requirements that protect them, scenario and threat-led tests execute against those requirements at step depth, and findings become remediation requirements that ride the same governed rails to closure.
Functions mapped
Critical & important functions as traceable items
Tolerances set
Impact tolerances derived as testable requirements
Scenarios exercised
TLPT and scenario tests executed, results linked
Findings logged
Gaps as remediation requirements with owners
Remediated
Fixes implemented under change control
Re-tested & closed
Closure evidence on the thread — supervisor-ready
Audit & exam evidence
The workflow that turns exam prep from a war room into a filtered view. Obligations trace to controls, controls to implementations, implementations to evidence — continuously — so internal audit tests against live data, the second line attests over a queryable record, and the examiner's sample assembles in minutes.
Control mapped
Obligation → control → owning system
Implemented
Requirement and change records linked
Evidence verified
Test results and approvals attached at depth
Attested
Review over live links, e-signed
Independently tested
Samples pulled as queries; findings tracked
Exam-ready
Request packages generated from live state
Next: every framework a financial institution answers to, mapped to capability.
Next: Compliance →Compliance
Every framework a financial institution answers to, mapped to capability
From operational resilience to model risk to change control, Modern Requirements maps the artifacts in your Azure DevOps environment to the frameworks below — obligation by obligation.
| Framework | What it governs | How it's covered inside Azure DevOps |
|---|---|---|
| DORA (EU 2022/2554) | ICT risk management, incident reporting, resilience testing, third-party oversight — enforced since Jan 2025 | Critical functions, tolerances, and controls as traced requirements with test and remediation evidence — incident facts and register support answerable as queries |
| EU AI Act | High-risk AI (incl. credit scoring) — risk management, data governance, documentation, logging, human oversight from Aug 2026 | AI systems as inventory items with Arts. 9–15 requirements traced to validation, oversight, and monitoring evidence; documentation generated from the record |
| SR 11-7 · OCC 2011-12 | Model risk management — inventory, validation, effective challenge, all models incl. AI | Model inventory as governed work items: derived requirements, independent validation evidence, approval and revalidation history on one thread |
| SOX ITGC | IT general controls — change management, access, segregation of duties | Segregated, e-signed approvals enforced in the workflow; any quarter's change population reproducible as a query with evidence attached |
| PCI DSS 4.x | Cardholder data security — incl. future-dated requirements now live | Security requirements traced to implementation and verification evidence, assessed change-by-change instead of annually reconstructed |
| NYDFS Part 500 | Cybersecurity requirements for NY-regulated financial services | Control obligations as traced requirements with evidence behind every certification the senior officer signs |
| FCA · PRA operational resilience | Important business services, impact tolerances, scenario testing | Tolerances as testable requirements exercised on schedule, with findings remediated on governed rails |
| FFIEC guidance | US examination expectations — incl. AI governance scope | The written evidence examiners expect, produced by construction: inventories, assessments, and change records as live views |
| ISO 27001 | Information security management systems | Security requirements and controls traced through the same change and evidence rails as functional work |
| ISO/IEC/IEEE 15288 · 29148 | System life cycle processes and requirements engineering | Requirement items carry 29148-aligned attributes, quality-scored against INCOSE characteristics before review |
Every mapping is bidirectional. Pick an obligation, see which artifacts satisfy it. Pick an artifact, see which obligations it serves. When the examiner, the external auditor, or the second line asks "show me," the answer is a filtered view of live data — not a binder someone maintains.
Next: the foundation, the AI layer, and the agents layer.
Next: The platform →The foundation · Modern Requirements4DevOps
One thread, from obligation to exam-ready evidence
Modern Requirements4DevOps makes every artifact below a real Azure DevOps work item or a Modern Requirements virtual work item. The links are live. Nothing is exported, synced, or reconstructed — the document is a view of the data, not a copy of it.
Obligation-to-control documents built from work items
Author the documents a regulated institution runs on — obligation registers, control narratives, functional specs, model documentation — as structured documents where every clause is a traceable, versioned work item. Word and Excel imports bring legacy mappings in with hierarchy intact.
Configuration control that matches how you release
Freeze any work-item set into an immutable baseline at every release and attestation point, and compare any two with a difference report — the change story between exams, written by the system. Variant management keeps jurisdictional configurations side by side.
Traceability into test execution, not just test plans
Horizontal and vertical matrices generated on demand from live link data — obligation down to the test step that verifies its control. Virtual work items extend the trace into execution depth: Test Point, Test Run, Test Result, Test Step Run. Control coverage is computed, not claimed.
Attestations and approvals where the work lives
Formal review cycles with item-by-item navigation, threaded findings, and e-signatures bound to work-item versions — who approved what, at which revision, with segregation of duties enforced. Smart Report turns the same live data into control, trace, and exam reports in minutes.
Built inside Azure DevOps. Deployed inside your boundary.
Many requirements tools connect to Azure DevOps. Connection means synchronization — and synchronization is where threads break, baselines fork, and audit findings are born.
External RM tool, synced to Azure DevOps
- Two systems of record; sync jobs decide which one is true today
- The thread crosses a tool boundary — matrices exported and rebuilt per exam
- A new vendor surface to clear through third-party risk review — and onto the register
- Engineering works in one tool, requirements live in another — drift is structural
- Evidence assembled per request: a war room, every time
Modern Requirements, native in Azure DevOps
- One system of record — requirements are work items, links are link data
- The thread spans obligation → requirement → code → test execution in one graph
- Lives inside your boundary — your tenancy or Azure DevOps Server on-prem, inheriting your identity and access model
- Engineers never leave their environment — the thread maintains itself as work happens
- Evidence is continuous: exam and attestation packages generate from live state in minutes
The AI layer · Copilot4DevOps
AI that clears your third-party risk review, not just your demo
A bank doesn't get to use AI that ships regulated data to an uncontrolled endpoint — and under SR 11-7, the AI tool itself belongs in your model inventory. Copilot4DevOps puts AI-assisted requirements work inside Azure DevOps, with deployment and data-residency options built for financial institutions, so every generated artifact lands in the managed lifecycle with authorship, review, and trace links intact.
Decompose
Turn a regulatory clause or business requirement into complete, well-formed requirements — structured, linked to their parent, ready to allocate, in a single click. The analyst reviews and accepts; the decomposition arrives with its trace already built.
Analyze quality
Every requirement scored against INCOSE requirement-quality characteristics — ambiguities flagged with concrete rewrites before review. An ambiguous requirement caught at authoring costs minutes; caught in production, it costs an incident report.
Change impact
Real-time impact assessment over your actual link graph — the full ripple of a change across requirements, controls, and tests before the approval is requested. The CAB meets with the analysis done, not to commission it.
Diagramming
Generate flow and process diagrams directly from requirements to validate shared understanding fast — linked to live work items and always current, from payment flows to approval chains.
The agents layer · Agents4DevOps
The routine work of the thread, executed by agents. Analysts approve.
Agents4DevOps runs the repetitive, high-volume work a regulated institution generates — evidence patrols across changes and controls, coverage checks before release, exam package assembly — under policy controls you define, with a human decision at every consequential step.
Evidence patrols
Agents patrol the thread continuously: production changes without segregated approval, controls drifting from their evidence, model revalidations aging past schedule, remediation items awaiting re-test — surfaced and packaged weeks before the audit sample lands, not after.
Exam & attestation package assembly
One prompt assembles the exam sample, the SOX testing population, the model inventory extract, or the AI Act documentation set — obligations, controls, change records, and signed approvals pulled from live data, validated, and staged for human review.
Obligation coverage patrols
New obligations without derived requirements, requirements without allocated owners, impact tolerances without a current test — flagged and drafted into tracking items long before the gap becomes a supervisory letter.
Plain-English commands that act
Tell it "show every production change this quarter without linked approval and test evidence, and draft the remediation items" — and the agent queries the live thread, creates the items, and stages the output for review.
A capacity multiplier for your best people, not a replacement for them. Agents execute under policy controls you define; analysts review, decide, and sign. Every agent action runs through governed accounts and the same audit trail as everything else in the system.
Next: six roles working inside one governed thread.
Next: Who it's for →Who it's for
One governed thread. Six roles working inside it.
Every workflow on this page has named owners because that's how a regulated institution actually runs — the first line builds, the second line challenges, the third line tests. Here's what each role does inside the same system, instead of in six disconnected tools.
Compliance as a by-product of shipping
Engineers stay in Azure DevOps — requirements sit beside the boards, repos, and pipelines they already use. Commits carry requirement IDs, tests auto-link, and the change record their release generates is the same one the auditor samples. No parallel paperwork track.
Challenge over a live record
The second line owns the obligation map and the attestations. Every obligation traces to requirements, controls, and evidence; gap queries replace annual mapping refreshes; and the attestation is signed over a record that can be inspected, not a summary that has to be believed.
Sampling as a query, not a request cycle
Audit owns independent testing. Change populations pull as queries with approvals and evidence attached, findings track to closure as governed work items with re-test evidence, and fieldwork starts on day one instead of after three weeks of evidence requests.
An inventory that answers on request
MRM owns the inventory, the validations, and — since the AI Act — the conformity evidence. Every model and AI system is a governed item with derived requirements, independent validation linked, and revalidation schedules that age visibly instead of silently.
Requirements that survive contact with delivery
BAs own the translation from business intent and regulatory obligation to buildable requirements. Copilot drafts and scores the tedious parts, decompositions arrive with trace intact, and "why does this requirement exist?" is answered by the link to the obligation — permanently.
Release decisions on computed coverage
QA owns the evidence behind every release. Tests execute at step depth against the requirements they verify, a change flags every affected test the moment it lands, and the release review runs on coverage that's computed — then locks the baseline the next exam will reference.
Next: the 90-day rollout and the questions strong buyers ask.
Next: Getting started →Implementation
A use-case-driven rollout, not a platform project
We don't turn on the full platform on day one. The rollout starts with one obligation thread on one platform, traced end to end, so the team experiences a real win in the first sprint — and by day 90, holds an exam-ready evidence package.
One thread, end to end
- Deployed into your Azure DevOps — your tenancy or Server, inside your boundary
- One obligation traced to implemented control and test evidence
- Smart Doc structure mirrored from your control narratives
- First trace matrix in the CTO's and CCO's hands
Requirement set moved in
- Active obligation mapping imported from Word/Excel as structured items
- Legacy IDs preserved as attributes
- Change and review workflows live with segregated e-signatures
- First Copilot4DevOps quality scores on the backlog
Role-based training, four tracks
- BA & compliance — Smart Docs, decomposition, obligation trace
- Engineering — code linking, change workflow
- QA & release — execution depth, coverage, baselines
- Audit & MRM — sample queries, inventory views
First evidence package
- Exam- or audit-style package assembled by Agents4DevOps
- Coverage dashboard live at the current baseline
- Internal mock sample run against the package
- Package exported for your next real audit or exam
What "exam-ready" means, measurably
Full bidirectional trace on at least one obligation family · the active mapping migrated with change workflow live · and an evidence package generated from live data that passes an internal mock sample.
The questions strong buyers ask
What a decision maker will want answered before talking to anyone
The questions CTOs, CROs, and heads of audit actually ask when they evaluate a system that will hold their obligation thread. Straight answers, including where the honest answer is "that stays in your GRC."
Data residency and sovereignty — can this run inside our boundary?
The architecture is designed for it: Modern Requirements runs inside your Azure DevOps environment — your tenancy in your chosen region, or Azure DevOps Server on-premises — inheriting your identity model, permissions, and network boundary rather than adding a new SaaS surface to your third-party register. For the AI layer, Copilot4DevOps supports bring-your-own-data, bring-your-own-LLM, and on-prem AI configurations, so AI assistance doesn't mean regulated data leaving your boundary. [CONFIRM with product: supported regions, Azure DevOps Server versions, and current on-prem AI deployment options]
Does this make us DORA / AI Act compliant?
No tool makes you compliant, and you should distrust any that claims to. What Modern Requirements does: it makes your compliance demonstrable — every obligation traced to the requirements that implement it, the changes that delivered them under segregated control, and the test evidence behind them. Your compliance program, registers, and regulator relationships stay yours; the requirements thread is where the engineering evidence behind them lives. With the AI Act's high-risk obligations landing on existing production models, a queryable record is also your hedge: new obligations become a gap query and staged requirements, not a restarted program.
Does this replace our GRC platform (Archer / ServiceNow / MetricStream)?
No — and the boundary matters. Your GRC owns the enterprise risk register, policies, issues, and attestation workflows at the governance level. Modern Requirements owns the engineering requirements thread: how an obligation became requirements, how those requirements became code and configuration under change control, and the execution-depth test evidence that they work — living where the engineering happens. The two meet at the control: GRC says which controls exist and who owns them; the thread proves they're implemented and tested. Institutions that try to run requirement-level traceability inside a GRC are usually the ones whose exam prep still ends in a war room.
How does this help with SR 11-7 and the AI Act specifically?
By making the model estate part of the governed thread. Each model or AI system is an inventory item with risk classification; its data-governance, oversight, accuracy, and logging obligations are derived as traceable requirements; independent validation and monitoring evidence link at execution depth; and approvals and revalidations carry e-signatures with full history. When the examiner asks for the inventory and risk assessments — or the AI Act asks for technical documentation — the answer generates from the record. The finding pattern regulators keep citing is an inventory that can't be produced on request; this makes it a view.
Half our estate is on Jira. Does this force a migration?
No. Modern Requirements includes Jira synchronization, so mixed estates — Azure DevOps for some platforms, Jira for others — can keep one requirements thread with links spanning both. The governed record lives in Azure DevOps; teams working in Jira keep their workflow, and trace and evidence stay whole. Consolidation can come later, on its own business case, not as a precondition. [CONFIRM with product: current Jira sync scope and any GRC/ServiceNow integration options]
We're mid core-modernization. Is this the wrong time?
It's the best time — modernization is when the requirements record gets rebuilt whether you plan it or not. Standing the thread up on the modernization program means the new platform is born with obligation-to-evidence traceability instead of inheriting the old spreadsheet estate, and the migration's own change risk rides governed rails with computed impact. Most institutions start exactly here: one program, one thread, expanding as platforms cut over.
What do examiners and auditors actually receive?
Documents — because exams still run on documents. Smart Report produces control narratives, trace matrices, change populations with approvals and evidence, coverage and gap reports, baseline difference reports, and approval histories with signature records, formatted for your reviewer. The difference from the status quo isn't the format the examiner sees — it's that the document is a report of the thread's state, so it never disagrees with the system it came from.
Next: go deeper — guides, whitepapers, and demos on everything this page covers.
Next: Resources →Resources
Go deeper on regulated financial delivery
Blogs and guides on financial-services compliance in Azure DevOps — DORA operational resilience and requirements management across banking, finance and insurance.
DORA Requirements for Financial Institutions
Read the blog →BlogWhy Requirements Management is the Backbone of Insurance Product Innovation
Read the blog →BlogFrom Chaos to Clarity: Tackling Insurance Change Requests
Read the blog →GuideEnsure DORA Compliance with AI-Powered Requirements
Read the guide →Ready? Scope a banking & finance working session with our team.
Book a working session →Talk to us
See it on an estate shaped like yours
A working session with our financial services team — in a demo environment preloaded with an obligation register, change governance workflow, model inventory, and exam evidence packages. Your frameworks, your vocabulary, your boundary constraints.
- Walk one thread end to end: obligation → requirement → segregated change → test evidence
- Run a live change impact assessment and watch the suspect links land across controls and tests
- Watch an exam sample package assemble from live work-item data
Prefer to look before you talk? Watch the five-minute technical demo — Smart Docs authoring, live traceability into test execution, and e-signature review workflows, screen-recorded in the live product. Watch the demo — no form →
Book a banking & finance working session
Tell us about your estate. We'll come prepared with your frameworks, not a generic tour.
