AuditBoard and Protiviti roll out AI-driven risk and audit capabilities for enterprises
AuditBoard, working with Protiviti, has introduced two AI-driven solutions to streamline risk and audit management. The focus: faster workflows, higher-quality data, and stronger visibility across governance, risk, and compliance functions.
Announced on October 21, 2025, the updates build on AuditBoard's risk platform and are aimed at leaders who want less manual work and more actionable insight.
What's new
- Generative AI Integrations: Create AI agents that interact with AuditBoard. They connect related risks, controls, and issues for better search, analyze historical data to draft context-aware text for mitigation, and auto-fill fields to shrink admin time.
- Deep ERP Integration: Pipe journal entries and financial account data directly into AuditBoard for ongoing control monitoring and easier evidence collection. Teams can also trigger automated workflows within AuditBoard Analytics for continuous or point-in-time testing.
Why this matters for management
Manual evidence gathering, fragmented data, and slow control testing clog decision-making. These integrations aim to cut cycle time, reduce human error, and keep your audit, risk, and compliance teams focused on higher-value analysis.
Andrew Struthers-Kennedy, who leads Protiviti's Internal Audit & Financial Advisory services globally, said the collaboration helps organizations shift from reactive compliance to proactive risk intelligence by connecting data, context, and action-accelerating maturity and unlocking strategic value.
AuditBoard's chief growth officer, Jim Sperduto, noted that the connected risk approach is about breaking down silos so risk, compliance, and audit insights inform daily business decisions. The partnership is intended to capture higher-quality data at the source and increase visibility to risk in core operations.
How this could look in your environment
- Connect ERP systems: Map journal entries and account hierarchies to AuditBoard. Set schedules for automated pulls.
- Define the risk/control taxonomy: Standardize naming for risks, controls, and issues so AI agents can link records accurately.
- Configure AI agents: Use historical findings, policies, and prior narratives to guide text generation and auto-fill rules.
- Automate testing in Analytics: Set continuous monitoring for key controls and point-in-time checks for audit periods.
- Route exceptions: Build workflows for triage, remediation, and sign-off so issues don't stall.
Governance to get right from day one
- Data access and privacy: Limit AI agent permissions to the minimum necessary. Log all activity.
- Model oversight: Review AI-generated narratives and mappings before they go live. Keep a human in the loop for high-impact changes.
- Change control: Version prompts, rules, and workflows. Document who approved what and when.
- Quality checks: Spot-check auto-filled fields and AI-linked records until accuracy is consistently high.
Metrics to track
- Time to collect evidence and complete control tests
- Percentage of fields auto-populated with acceptable accuracy
- Exception detection rate and false positive rate
- Cycle time to close issues and implement remediation
- Control coverage (continuous vs. periodic)
- User adoption and satisfaction across risk, audit, and finance
Implementation pointers
- Start with a pilot on one ERP data flow and a handful of high-priority controls.
- Build a cross-functional squad: internal audit, controllership, IT, data, and security.
- Document policies for AI-generated text, evidence acceptance, and exceptions.
- Communicate early wins to speed adoption across business units.
Where to learn more
See platform details from AuditBoard and advisory perspectives from Protiviti.
If you're upskilling your team on practical AI workflows for risk and audit, explore role-based training options at Complete AI Training.
Your membership also unlocks:
