See how AI operating layers are transforming control testing, assurance execution and risk management in banking.
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As the industry advances into the "Banking 4.0" era, AI in Banking is shifting from a set of experimental features to the fundamental operating substrate of the enterprise. Despite heavy technology spending, 95% of enterprise AI pilots fail to deliver measurable financial impact, leaving many institutions stuck in "pilot purgatory" while regulatory complexity and fraud patterns accelerate.
To bridge this gap, banks are moving toward an AI Operating Layer for Control Assurance— a governed execution model designed to transform Risk Management in Banking from a manual, periodic burden into a proactive engine of resilience.
The Execution Gap in Legacy GRC
Traditional GRC (Governance, Risk, and Compliance) models were built on assumptions that no longer hold true: that systems change slowly and that controls can be effectively sampled periodically. Today, banks are real-time digital platforms generating billions of security-relevant signals daily from API traffic, identity events, and cloud telemetry.
Manual assurance processes cannot keep pace with this scale. When control testing relies on spreadsheet-heavy workflows and point-in-time audits, it creates a "misleading perception of control" where compliance artifacts appear strong, but real-time risk exposure remains opaque. This means institutional knowledge is often tied to individual experience rather than structured, scalable systems.
The Shift from Assurance Reviews to Assurance Execution
Traditional control assurance is built around reviews. Evidence is collected, controls are assessed, findings are validated, and conclusions are documented through a series of manual activities. While effective in the past, this review-centric model is becoming increasingly difficult to scale as control environments grow more complex.
An AI operating layer enables a shift toward assurance execution, where activities such as walkthroughs, evidence acquisition, control testing, risk gap identification, and reporting operate as part of a connected workflow with traceability embedded throughout.
The Shift from Assurance Reviews to Assurance Execution
Walkthrough
Establish testing scope and understand the control environment
Sample Acquisition
Collect evidence and supporting documentation
Test of Design (TOD)
Assess whether controls are appropriately designed
Test of Effectiveness (TOE)
Validate whether controls are operating as intended
Risk Gap Identification
Identify control weaknesses and potential exposure areas
Leadership Reporting
Generate evidence-backed findings and assurance insights
The Multi-Agent Architecture Behind Scalable Assurance
Modern control assurance requires more than a single chatbot; it requires a multi-agent architecture. An effective AI operating layer utilizes specialized agents—such as Testing Agents, Evidence Evaluation Agents, and Audit Narrators—to execute distinct functions simultaneously without manual coordination.
Unlike public AI tools, a dedicated operating layer for banks must be domain-trained for regulated environments like SOX, ICFR, and credit risk workflows. This ensures that AI understands the specific nuances of banking regulations, reducing the 90-95% false-positive rates often seen in legacy transaction monitoring systems.
Governance by Design: The The Control Assurance Command Center
The primary concern is not just powerful AI, but safe, enterprise-ready AI. An AI Operating Layer is designed to operate within the bank’s controlled environment (AWS, Azure, or on-prem) to ensure data privacy and compliance.
Key pillars of this secure architecture include:
- Zero Data Copy Posture: Documents and evidence are never stored or retained outside the enterprise environment
- Audit-Grade Traceability: Every AI decision and output is captured in a tamperproof audit trail, allowing for the complete reconstruction of activity for regulators.
- Human-in-the-Loop Accountability: The AI does not replace judgment; it escalates ambiguous evidence for human validation. Reviewers retain final authority over approvals and overrides.
This level of rigor is supported by certifications such as ISO 27001 and SOC 2 Type II, ensuring the platform aligns with the NIST AI Risk Management Framework and GDPR requirements.
The Next Evolution of Control Assurance
As control environments become more complex and regulatory expectations continue to rise, banks need more than systems that manage workflows and repositories. They need the ability to execute assurance activities with greater consistency, traceability, and scale.
This is why many institutions are looking beyond traditional GRC platforms and adopting an AI operating layer for control assurance—one that can support control testing, evidence evaluation, risk gap identification, and reporting within a governed operating model.
ANA was built for this shift, helping banks transform assurance from a periodic, reviewer-dependent process into a more scalable and continuously ready capability.
Resources
Go deeper on the thinking, the architecture, and the operating model behind ANA.
ANA at
a Glance
A complete overview of ANA's architecture, lifecycle stages, governance posture, and deployment model.
Download PDFThe AI Operating Layer for Control Assurance
A practitioner's view of how agentic AI changes the operating model for risk and control functions.
Read white paperRCSA Modernisation at a Mid-Size US Bank
How a regional bank modernised its RCSA cycle and expanded control coverage with ANA.
Read case studyWhy Control Testing Was Never Designed to Scale
A perspective on the structural reasons assurance functions struggle to keep pace.
Read blogDirect answers to the questions that surface most often in conversations with CROs, Internal Audit, and Compliance leaders.
