A trio of moves by AuditBoard, Protiviti and Qualys underscore how AI is transforming the way organizations view and manage risk.
Traditional risk management was built for a slower world; one where quarterly audits, static spreadsheets, and manual workflows were enough to keep the machine running. In 2025, AI is becoming the command center.
AuditBoard and its consulting partner, Protiviti, recently rolled out two major integrations: generative AI agents and deep ERP integration (Yahoo Finance, 2025). The first link enables AI agents to draw on AuditBoard’s data to link risks, controls, and issues, auto-fill inputs, and draft mitigation language, transforming what used to be manual triage into a near-real-time workflow.
Simultaneously, the new ERP integration channel continuously pipes journal entries and account data into the platform, allowing control testing and evidence gathering to shift from periodic checks to live monitoring. According to AuditBoard’s chief growth officer Jim Sperduto, the aim is to “break down silos so risk, compliance, and audit insights drive smarter business decisions.”
At the same time, Qualys introduced its Enterprise TruRisk Management (ETM) platform and Risk Operations Center (ROC), built to unify cyber risk data, apply business context, and score risk in financial terms, supported by AI capabilities (SC World, 2025). The TruRisk 2.0 upgrade moves from proprietary QID identifiers to global CVE standards and real-time threat intelligence feeds, supplying asset inventory, vulnerability scoring, and remediation workflows from one central portal.
Rightly so, AI introduces three game-changing capabilities. First: velocity. AI models can analyze vast volumes of internal and external data (transaction records, communications patterns, vendor responses) far faster than any human team could. That means risk isn’t assessed monthly or annually; it’s actually living, evolving, and monitored. Second: connectivity. In legacy setups, audit, compliance, and operations operated in silos. Modern platforms link these streams to surface hidden dependencies; an internal control failure might now trigger a reputational ripple, supplier disruption, or a cascading compliance effect. Third: foresight. With scenario modeling and dynamic simulation built into the workflow, organizations no longer ask “What went wrong?” but “What could go wrong, and how do we respond before it does?”
Yet the upside is matched by complexity. One critical issue is model opacity. Many AI-driven decisions are made with little transparency, prompting regulators to warn of systemic vulnerabilities as similar models proliferate across industries. If a risk score flags your team for remediation, but the logic cannot be traced and explained, the very control you’ve built becomes a liability. Another challenge is data integrity and bias: AI learns from what you feed it. A skewed dataset, or one that fails to reflect changing conditions, can produce misleading outputs, replicating errors at scale. Governance frameworks such as the NIST AI Risk Management Framework emphasize four pillars (map, measure, manage, and govern) to make AI safer and more accountable.
Then there’s the emerging risk of the wrong sort of automation. By handing tasks to AI, such as evidence gathering, anomaly detection, and even vendor assessments, organizations may offload critical judgment without embedding robust human oversight. Worse, they may inadvertently broaden their attack surface. Generative AI systems can be manipulated, data poisoned, or used for adversarial attacks, creating outcomes that mimic risk rather than mitigate it.
Automation, integration, and foresight: they all sound like mere tools. But what they enable is readiness. When risk becomes live, business becomes responsive. And that responsiveness is a competitive advantage.