Artificial intelligence (AI) is advancing at an unprecedented pace in investment, innovation, and deployment. Yet governance, regulation, and control mechanisms have not progressed commensurately. AI’s speed, scale, and growing autonomy clearly outpace existing controls, posing risks that require structured and formal oversight. Regulators understand the imbalance, and Governance, Risk & Compliance (GRC) software forerunners recognize an opportunity. With regulation emerging and AI governance solutions gaining traction, the GRC software landscape is poised for rapid transformation.
Traditional GRC software providers have long supported enterprises with policy management, risk assessments, audit reporting, third-party risk oversight, mapping controls to frameworks, etc. Though foundationally necessary, traditional GRC practices and solutions have insufficiently adapted to the complex and fast-evolving AI-risk landscape.
AI systems operate unpredictably, scale rapidly, and pose risks such as bias, hallucination, data misuse, and model drift—challenges that current governance measures cannot fully address. Agentic AI, systems that plan and act toward goals with limited human oversight, amplifies operational risk when connected to external systems. Examples include environments where AI agents have the autonomy to initiate actions across enterprise software (e.g., triggering financial transactions via ERP systems), interact with IoT devices (e.g., adjusting industrial equipment or medical wearables), or coordinate across cloud-based APIs and robotic systems. Regulators and investors increasingly recognize that this revolutionary and nuanced technology has ushered in a fundamentally new risk environment, one that current oversight frameworks are ill-equipped to manage.
Policy makers are beginning to act. The EU AI Act was enacted in 2024, with phased application beginning in 2025. Bans on “unacceptable risk” systems became enforceable on February 2025, with broader high-risk requirements phased in over time and full compliance expected for large-scale EU IT systems by the end of 2030. These milestones demand auditable, demonstrable model documentation, transparency, and post-market monitoring. ISO/IEC 42001 establishes an auditable AI management system, allowing enterprises to certify AI governance processes, akin to ISO 27001 for cybersecurity. This standard turns AI governance from best practice into a certifiable control system.
In the U.S., AI regulation is accelerating, though the path remains less defined. In April 2025, the Federal Trade Commission (FTC) issued new guidance on the use of AI in consumer finance and advertising, emphasizing standards for fairness, explainability, and transparent model management. The Department of Justice (DOJ) has also increased AI-related enforcement actions; for example, probing algorithmic bias in lending and employment screening. Meanwhile, the National Institute of Standards and Technology (NIST) updated its AI Risk Management Framework, providing clear, prescriptive steps for organizations to identify, assess, and control AI risks. Collectively, such regulatory developments signal a shift toward more stringent oversight and elevated AI-compliance expectations.
The resulting opportunity is enormous: Forrester Research predicts spending on AI governance tools will surge, exceeding $15 billion, representing 7% of total enterprise AI software expenditures by 2030. GRC software providers are beginning to respond to the opportunity. OneTrust’s Spring 2025 release introduced expanded AI governance capabilities that streamline model inventory, assessment, and ongoing oversight. Products include: the AI Governance Program Center, AI Systems Inventory, and AI Model/System Cards. The ServiceNow AI Platform integrates model telemetry and policy workflows, shifting buying criteria toward platforms that can bring AI lifecycle governance into existing processes rather than adding a standalone tool. IBM’s OpenPages now connects with watsonx.governance, enabling regulated firms to demonstrate end-to-end traceability from model documentation to audit findings. The above examples mark the nascent stage of scalable AI governance platforms that support embedded capabilities within existing GRC and developing AI frameworks.
As AI systems grow more autonomous and commercially embedded, the governance gap will widen unless enterprises and regulators evolve in tandem. Agentic systems, real-time decisioning, and autonomous operations demand oversight that is not static or peripheral, but rather dynamic, embedded, and AI-native. The regulatory landscape will be increasingly active, global, and prescriptive. Enterprises that treat AI governance as a strategic imperative will be better positioned to earn trust, mitigate risk, and compete in a market where accountability is currency. This creates a clear opening for vendors capable of delivering the next generation of GRC platforms and solutions built for continuous oversight, explainability, and traceability across the AI lifecycle. What we’re seeing now is just the beginning.
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