2026 is the year the regulatory perimeter snapped shut. DORA (Digital Operational Resilience Act) for financial services is in force. NIS2 (Network and Information Security Directive 2) covers most of the EU economy. The EU AI Act is enforcing on high-risk systems. Basel III / IV final rules are landing in major jurisdictions. BCBS 239 is no longer aspirational.
Most regulated enterprises have the same problem: the controls live in binders, the evidence is manual, and the regulator’s question gets a spreadsheet. That’s not sustainable in 2026.
ByteWave’s approach is to engineer a single risk + compliance control plane, with continuous controls monitoring, auto-evidence, and a regulator-ready dashboard. Same pattern we use for AML, fraud, GRC, cyber-risk, model risk and operational resilience.
DORA: ICT risk management, third-party risk register, incident reporting (4h / 72h / monthly), threat-led penetration testing, digital operational resilience testing. We deliver all five on a single platform.
NIS2: risk-management measures, incident handling, business continuity, supply-chain security, vulnerability handling, encryption, access control. Same platform, slightly different taxonomy.
EU AI Act: risk classification (unacceptable / high / limited / minimal), conformity assessment, technical documentation, post-market monitoring, human oversight, transparency. We map AI systems to risk classes, maintain technical documentation, instrument post-market monitoring, and document human oversight.
BCBS 239: risk data aggregation, accuracy, completeness, timeliness, adaptability. We deliver this on Snowflake / Databricks with lineage, governance, and the dashboards the supervisor will inspect.
The technical pattern is the same across all five. We use ServiceNow GRC / RSA Archer for the workflow, Snowflake or Databricks for the data, ServiceNow IRM or custom ML for AML / fraud, and a continuous controls monitoring layer that auto-collects evidence.
Auto-evidence is the key. The platform reaches into Snowflake / Databricks / SAP / ServiceNow / Workday / Dynamics / GitHub and pulls the evidence on a schedule. When the auditor asks ‘show me the change-management evidence for this quarter’, the dashboard shows it. No screenshots, no binders, no last-minute scramble.
Continuous controls monitoring. The same pattern that powers cyber-risk dashboards powers operational risk dashboards. Same for AML, fraud, vendor risk, model risk.
Regulator alignment. The risk platform ships with a regulator-ready view: per-jurisdiction, per-framework, per-control. The supervisor can be given read-only access to the dashboard (with proper identity and audit), and the evidence is always current.
Model risk management. With GenAI in production, model risk becomes a primary concern. MLflow + Model Registry + eval harness + drift detection + bias audits + human oversight. We deliver this on the same platform, not in a separate tool.
What does the engagement look like?
1. Risk-data assessment (4 weeks). Inventory of systems, data sources, controls, gaps. Map to DORA, NIS2, AI Act, BCBS 239, etc.
2. Target architecture (4 weeks). ServiceNow GRC + Snowflake + Databricks + custom ML. Signed off by CRO + CISO.
3. Phased build (6–12 months). Controls library, taxonomy, evidence collection, dashboards. Wave-based, with regulator alignment per wave.
4. Operate. We stay accountable for the platform’s health, with quarterly tabletop exercises and an annual regulator-ready review.
The outcome: a regulator who trusts the dashboard, a board that trusts the risk report, a CISO who trusts the evidence, and a CRO who can sleep at night.