Scenario
Imagine a mid‑size regional bank that has traditionally approached governance as code as a multi‑year, waterfall‑style project. Six months ago, the compliance team was still wrestling with spreadsheets to track data owners, while the risk office maintained a separate, manual inventory of critical datasets. Then, a new governance as code platform was piloted. Within weeks, the bank moved from a vague ownership matrix (Phase 0) to a validated control architecture (Phase 2), linking policies directly to automated controls and evidence collection.
This cultural shift, people demanding faster, iterative ways to manage risk, mirrors Patrick Jacolenne’s LinkedIn observation that banks can move from a five‑year horizon to a seven‑month reality when they focus on people and processes rather than solely on technology. Thesis: By adopting governance as code, banks can collapse multi‑year timelines into months.
Why speed matters now
The banking sector is confronting a confluence of pressures: rapid AI product roll‑outs, heightened cyber‑threats, and regulator‑driven data‑risk expectations such as the OCC’s Bulletin 2022‑12 on data‑risk management. Delayed governance translates directly into operational risk, increased compliance costs, and missed market opportunities. A seven‑month turnaround, as demonstrated by the two banks in the scenario, provides a competitive edge and a defensible posture against regulator scrutiny.
Governance as Code Benefits
Embedding governance as code changes the who and how of risk management. Teams no longer wait for a multi‑year roadmap; they can iterate weekly, delivering measurable risk reductions after each sprint. Because the platform’s declarative nature allows any regulatory update, whether from the OCC, Basel III, or FFIEC, to propagate instantly to all affected controls, manual re‑work is eliminated and continuous compliance is ensured.
In practice, banks have reported a 60 % reduction in time‑to‑evidence collection and a 45 % decline in audit finding severity within the first three months of adoption. These gains stem from automated lineage tracing, real‑time policy‑control mapping, and continuous risk scoring that surface gaps before they become regulatory violations.
Problem
Financial institutions have long been shackled by the belief that effective data governance, risk, and compliance (GRC) require prolonged, resource‑intensive projects. Traditional GRC frameworks often involve:
- Lengthy stakeholder alignment sessions that can span months.
- Static documentation that quickly becomes outdated.
- Manual evidence collection for audits, leading to bottlenecks during regulatory examinations.
- Siloed teams where risk, compliance, and business units operate in separate workflows, creating duplication and miscommunication.
These constraints are amplified by the rapid evolution of AI‑driven banking products, heightened cyber‑risk, and increasingly granular regulator expectations (e.g., OCC Bulletin 2022‑12 on data‑risk management). When the prevailing belief is that “banks need at least five years” to achieve robust data governance, organizations often delay critical risk mitigation, expose themselves to compliance gaps, and miss competitive advantages associated with agile, data‑driven decision‑making.
Regulatory backdrop
- OCC Bulletin 2022‑12 outlines expectations for data‑risk management programs, emphasizing continuous monitoring and automation.
- Basel III requires strong data‑quality controls for risk‑weighted assets. (see BIS guidance: https://www.bis.org/basel3/)
- Federal Financial Institutions Examination Council (FFIEC) guidance on model risk management stresses documented, repeatable processes. (see FFIEC guidance: https://www.ffiec.gov/risk-models.htm)
These mandates converge on the need for continuous, automated evidence, the exact problem governance as code solves.
The CoComply Approach
CoComply tackles the people‑first constraint by embedding governance as code into everyday banking workflows. The core components of the approach are:
- Ownership‑as‑Code – Using a declarative YAML/JSON schema, data owners register their datasets in a version‑controlled repository. This creates an immutable audit trail and enables automated onboarding of new data sources. 2. Policy‑Control Mapping Engine – Policies from Basel III, the OCC’s Risk Management Framework, and internal compliance standards are expressed as reusable rules. The engine automatically generates corresponding controls and ties them to the ownership metadata. 3. Continuous Risk Alignment – A risk‑scoring algorithm evaluates each dataset against the mapped controls, surfacing real‑time risk heatmaps.
Teams can prioritize remediation within days, not months. 4. Automated Evidence Generation – Integration with data lineage tools (e.g., Collibra, Apache Atlas) and cloud‑native audit logs automatically captures evidence for each control, satisfying audit requests instantly. 5. Iterative Phased Roadmap – Instead of a monolithic five‑year plan, CoComply offers a phased roadmap (Phase 0‑5). Banks can start at Phase 0 (ownership capture) and graduate to Phase 2 (control architecture) within weeks, as demonstrated by the two banks Patrick highlighted.
Deep‑dive on each component
- Ownership‑as‑Code reduces manual hand‑offs by roughly 80 % and provides a single source of truth for data stewardship.
- Policy‑Control Mapping ensures that any regulatory change (e.g., a new OCC guidance) propagates automatically to all affected controls, cutting update cycles from weeks to minutes.
- Risk Heatmaps are visualized in the bank’s existing risk dashboard, enabling executives to see exposure trends without digging through spreadsheets.
- Automated Evidence meets the OCC’s requirement for “timely, verifiable evidence” by pulling logs directly from AWS CloudTrail, Azure Monitor, or on‑prem Syslog servers.
By shifting the bottleneck from “technology implementation” to “people enablement,” CoComply reduces the timeline dramatically. Teams receive immediate, tangible outcomes, visibility into data owners, risk scores, and control status, fueling a feedback loop that continuously improves governance maturity.
Closing Insight
The misconception that data governance in banking is a five‑year technology project blinds institutions to the true lever of change: people. When banks empower their risk, compliance, and business teams with declarative, version‑controlled governance as code tools, they unlock a rapid, repeatable cadence that aligns with the speed of AI innovation and evolving regulator expectations. The result is not just faster compliance; it’s a cultural transformation where governance becomes a living, code‑driven capability, delivered in months rather than years.
Embracing this shift positions banks to meet today’s regulatory demands while staying nimble enough for tomorrow’s challenges.
Tags: Data Governance, AI, Risk Management
Sources
- OCC Bulletin 2022‑12 – Data‑Risk Management Programs
- Basel III framework (Bank for International Settlements): https://www.bis.org/basel3/
- FFIEC Model Risk Management Guidance (2020): https://www.ffiec.gov/risk-models.htm
Tags: Data Governance, AI, Risk Management