How hidden risks in compliance can harm banks

The financial services industry operates in one of the most heavily regulated environments in the business world. With sensitive client data flowing through every transaction and communication, financial institutions face an increasingly complex web of compliance requirements that can make or break their operations. Traditional approaches to data governance simply aren't cutting it anymore.

When compliance meets reality — resilience is the real test. In my latest 2-minute video, I’ve shared a real use case on the AWS outage (October 20, 2025) — how a small automation bug disrupted thousands of organizations, including financial banks that had already completed all risk assessments and vendor checks. This event clearly showed that: Being compliant doesn’t always mean being resilient. Even trusted third parties can fail — and when they do, our ability to recover defines our strength. In the video, I talk about: How financial institutions were impacted Why traditional vendor assurance wasn’t enough What lessons align with DORA’s ICT third-party risk requirements How we, as GRC leaders, can strengthen resilience through continuous testing, dependency mapping, and Board-level visibility Our mission isn’t to eliminate risk — it’s to design systems and governance that can stand strong even when disruption happens. #GRC #GRCLeader #DORA #ThirdPartyRisk #OperationalResilience #FinancialBank #RegulatoryRequirement #CloudResilience #RiskManagement #Governance #Compliance #ResilienceLeadership #FinServ #Infosectrain #Barytech

Why Financial Institutions Need a Unified Data Vision? Many banks and insurers operate with fragmented data ecosystems, outdated governance frameworks, and growing regulatory pressures. The result? High compliance costs, inconsistent reporting, and limited business insight. A sustainable solution begins with a data strategy and governance roadmap that links data architecture, privacy controls, and business objectives into a single operating model. Financial institutions that map data ownership, define governance policies, and integrate automated compliance reporting gain both control and agility. The impact is measurable—reduced regulatory overhead, faster analytics delivery, and higher data trust across the enterprise. Data-driven resilience isn’t built overnight—it’s designed through strategic alignment and disciplined governance. Is your data strategy enabling smarter, compliant growth? #DataStrategy #DataGovernance #FinancialServices #BankingAnalytics #CDAO #CTO #CFO #DataLeadership #DigitalTransformation #DataAdvisory #Insurance

New DOJ “bulk data” requirements take effect October 6—and the stakes are especially high for global enterprises managing complex cross-border data flows, diverse vendor ecosystems, and high-volume information holdings. This timely analysis by Mike Summers and Tyler Thompson breaks down what the requirements mean in real terms—what to prioritize now, where risks typically arise, and how to align your data governance, vendor management, and compliance operations. If your organization handles large-scale data, this is a timely read to help reduce enforcement exposure and stay ahead of the curve.   To read the full article, click below. https://lnkd.in/eY4Dxae2 #DataCompliance #SensitiveData #DOJRule

Why data lineage is key to regulatory reporting Last year, the Bank of England renewed renewed its call for financial institutions to strengthen their data governance and reporting frameworks. By calling for stronger data governance and improved lineage, the regulator is reinforcing its expectation that banks must be able to trace, validate, and justify every figure they report. This emphasis on traceability is not only about compliance—it also reflects the growing recognition that data integrity underpins sound risk management and informed decision-making. ALMIS International, which offers an SaaS-based cloud platform to support data management, recently delved into whether it is time for financial institutions to address their financial data risk. Read the story here: https://lnkd.in/g9P_Q8di #FinTech #RegTech #Compliance Julie Duncan FCIM

# What we still under-test in Web3 risk & controls: Here’s a fresh take on how we can sharpen our testing of Web3 controls so they truly keep pace. 1. “Control effectiveness” gets real-time Traditional control testing often looks at quarterly snapshots. In Web3, things move in minutes: smart contract upgrades, governance votes, chain forks, DeFi liquidity shifts. ✅ Action: Include live monitoring hooks in your testing scope (e.g., event logs, protocol governance changes, chain anomalies) and simulate time-sensitive failure scenarios. 2. Smart contract logic = the new “business process” In Web2 you tested workflows + access controls. In Web3, the smart contract is the workflow. 🔍 You must embed code logic validation, e.g., are fallback functions secure? Are upgrade patterns safe? Are token approvals open-ended? Also test integration controls: multisigs, timelocks, governance-vote hooks. Don’t just test “can a user approve an amount” — test “who can upgrade the code, when, and how is that governance visible/audited?” 3. The perimeter has expanded and become fuzzy Wallets, bridges, oracles, off-chain signature systems — each is a control point and a risk vector. ⚠️ Consider: Your testing must map this “expanded perimeter” and include adversarial-thinking scenarios: attacker controls oracle → how many controls still hold? 4. Governance, not just tech In Web3 you’re not just dealing with code: you’re dealing with on-chain governance, token-holder votes, protocol forks. 🌐 Test design: What happens if token-holders approve a malicious proposal? Is there a pause or escalation control baked in? Are governance votes atomic and irrevocable? 5. Data-driven testing & anomaly detection Because of decentralization and on-chain transparency, you have rich data — transaction history, approvals, transfers, contract events. Use that in your control testing: 📊 Deploy analytics to spot: suspicious token approvals, sudden contract calls, abnormal governance vote patterns. In testing: simulate scenario where anomalous behavior just passes the control thresholds and see if your system flags it. 6. A control taxonomy for Web3 To make your testing systematic, define a taxonomy that includes: Access & identity (wallets, private keys, multisigs) Contract lifecycle (deployment, upgrade, self-destruct) Token economics & approvals (infinite approvals, reentrancy) Oracle & external data dependencies Governance & upgrade authority Bridge & cross-chain interoperability 7. Narrative + context = stakeholder buy-in When you share test findings, don’t just send a bug-list. Frame the story: 👉 If a malicious upgrade happens, bridge funds could leak — here’s the path, here’s the control that failed, here’s the real-world impact. Start testing not just what “should” happen, but what could happen when trust, code, and tokens are the controls. #Web3 #RiskManagement #ControlsTesting #BlockchainSecurity #Governance #DeFi #SmartContracts #Audit #Assurance

💡 C# Best Practices for Secure and Reliable Regulatory Reporting Systems In regulatory reporting, accuracy isn’t the only requirement — security and data integrity are just as critical. When working with frameworks like SFTR, EMIR, or MiFID II, your C# services often handle sensitive trade data, personally identifiable information, and regulatory identifiers. Here are a few best practices we’ve found essential for building secure and maintainable C# reporting systems in financial environments: --- 🔐 1️⃣ Secure Configuration Management Never embed credentials or endpoint URLs directly in your codebase. Use Azure Key Vault, AWS Secrets Manager, or environment-specific configuration files. In regulated systems, a hardcoded connection string is more than bad practice — it’s a potential compliance issue. --- ⚙️ 2️⃣ Strong Data Validation and Schema Enforcement When transforming or enriching data for XML or ISO 20022 reports, always validate against official schemas (XSD). Implement structured validation in your C# pipeline — not only during final XML generation. Early validation prevents downstream reconciliation issues and rejected reports. --- 🧩 3️⃣ Thread Safety and Parallel Processing Reporting pipelines often use parallel tasks for large volumes of trade data. Make sure shared collections and context objects are thread-safe and avoid race conditions in enrichment logic. A single concurrency bug can cause inconsistent figures across reporting runs. --- 🧮 4️⃣ Use Immutable Models for Data Transformations Immutable C# records (C# 9+) help ensure data consistency through transformation stages. When dealing with multiple enrichment layers (e.g. trade → collateral → counterparties), immutability reduces accidental mutation and increases audit traceability. --- 🔍 5️⃣ Logging with Privacy in Mind Comprehensive logging is essential, but be cautious: logs must never expose sensitive identifiers (LEIs, UTI, UPI). Implement structured logging with redaction and configurable verbosity levels per environment. --- At DataCraft Consulting, we help financial institutions optimize and secure their regulatory reporting systems — from SQL data pipelines to C# transformation layers. If you’d like to review your reporting architecture or explore performance and security improvements, visit 👉 https://datacraft.lv #CSharp #FinancialReporting #RegTech #SFTR #EMIR #MiFIDII #FinTech #DotNet #CyberSecurity #DataCraftConsulting

If you don't need it, delete it. Stop treating excess data as an asset. 🗑️ The risk of storing data "just in case" far outweighs any potential future benefit. This is the essence of data security and governance. Implement the EQLC Data Cleaning Cycle: 1. Review (Identify excess data). 2. Redact (Remove PII/sensitive details). 3. Restore/Archive (Secure the bare minimum). 4. Repeat (Schedule next cleanup). Clean your digital environment regularly. #DataGovernance #DataCleaningCycle #EQLC #EQLConsulting  Do you have a documented, scheduled process for deleting old customer data? (Yes/No/Working on it)

Why API Governance Matters More Than API Count Every institution is building APIs. Few are governing them. As ISO 20022 adoption expands, data governance and API governance are becoming inseparable. Every field, every consent, every audit trail must align across systems — or compliance risks multiply silently. Modern API management isn’t just about integration speed. It’s about control, visibility, and accountability: ✅ Who accesses what data ✅ When and why it’s accessed ✅ How securely it moves between systems At Nth Exception, we help banks design governance-first API architectures — ensuring ISO 20022 data remains structured, compliant, and regulator-ready across the entire transaction lifecycle. Because in payments, it’s not how many APIs you have that matters. It’s how well they’re governed. #ISO20022 #APIManagement #Governance #RegTech #CrossBorderPayments #Fintech #OpenBanking #BankingInnovation #NthException #DataSecurity

I reviewed this week the concept of 𝗔𝗖𝗜𝗗 𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀, and everything made sense at first: Atomicity? Easy, all or nothing. Consistency? Just don’t break the rules. Isolation? Leave the transaction alone. Durability? Make sure what’s done stays done. Then I dug deeper. Commits and rollbacks, constraints, locks, transaction logs… all clear. Until I hit 𝗜𝘀𝗼𝗹𝗮𝘁𝗶𝗼𝗻 𝗟𝗲𝘃𝗲𝗹𝘀. Those cryptic names like 𝘙𝘌𝘈𝘋 𝘊𝘖𝘔𝘔𝘐𝘛𝘛𝘌𝘋, 𝘙𝘌𝘗𝘌𝘈𝘛𝘈𝘉𝘓𝘌 𝘙𝘌𝘈𝘋, 𝘚𝘌𝘙𝘐𝘈𝘓𝘐𝘡𝘈𝘉𝘓𝘌. Why so many options? Isn’t fresh data always better? Not really. It depends on what you need the data for. It’s not about freshness, it’s about 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 𝗱𝘂𝗿𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸. Isolation levels decide how much the “world” is allowed to change while your transaction runs. I imagined running a financial report while other users are updating data at the same time.  • 𝗥𝗘𝗔𝗗 𝗨𝗡𝗖𝗢𝗠𝗠𝗜𝗧𝗧𝗘𝗗: my report could include numbers from transactions that might later fail, hence total chaos. Rarely used.  • 𝗥𝗘𝗔𝗗 𝗖𝗢𝗠𝗠𝗜𝗧𝗧𝗘𝗗: every query in the report (and a report can have A LOT of them) reads the latest committed data. But totals may shift halfway through, so the last queries of the report could have different data than the first ones. Fine for quick queries, not for long reports.  • 𝗥𝗘𝗣𝗘𝗔𝗧𝗔𝗕𝗟𝗘 𝗥𝗘𝗔𝗗: the database gives me a snapshot frozen at the start of the report. My report could take hours, but it stays consistent even if others commit changes. Great for long reports.  • 𝗦𝗘𝗥𝗜𝗔𝗟𝗜𝗭𝗔𝗕𝗟𝗘: the safest one. The system behaves as if my report ran completely alone. If another transaction could affect it, the database waits or fails with a serialization error. With this example I finally understood: Isolation isn’t just about blocking others from changing data. It’s about choosing the right level of consistency for the job, and 𝗸𝗲𝗲𝗽𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗹𝗱 𝘀𝘁𝗮𝗯𝗹𝗲 𝘂𝗻𝘁𝗶𝗹 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 𝗶𝘀 𝗱𝗼𝗻𝗲. You want to see your current account balance? Give me the latest committed data. You want to run a long report? Give me a snapshot of the world when I started. Of all ACID concepts, Isolation Levels was the tricky one for me, but with the right analogy, it finally clicked.