Moving beyond the checklist: GRC practices and technology must evolve for modern AI governance

Until recently, the conversation around governance, risk, and compliance (GRC) for artificial intelligence has focused primarily on process and workflow. Organizations in regulated industries, especially insurance and financial services, have made large investments in top business platforms [like ServiceNow] for high-level use cases and IT service risk. They might even use platforms [like OneTrust] to make privacy assessments and compliance workflows easier. Meanwhile, in ‌technical organizations, data governance and MLOps platforms ensure data lineage and management of the core model development pipeline.

At a high level, it might seem like there are enough people, processes, and technology to ensure that every AI asset is inventoried and every policy is mapped for oversight. But the moment a regulator asks, "Show me the objective proof that your claims model is fair and robust today.” or the C-suite asks for proof that AI investments are working; the entire ecosystem reveals cumbersome integrations and fragmented workflow steps across all systems as a critical vulnerability.

GRC solutions are historically designed to follow checklists and to check IT systems regularly. They are not designed to control the dynamic, unpredictable nature of AI. This creates a dangerous “assurance gap” where the speed of AI and innovation far outpaces the speed of control in established GRC systems.In this article, we’ll help you evaluate the state of your current GRC technology, people, and practices for AI readiness. We’ll also help you consider opportunities for GRC to be a champion of AI as a strategic part of your company’s growth.

Governing AI requires unique treatments and specializations

Even the most advanced tech stacks can still lack key specializations in important areas. This can create unmanaged liability when supporting and scaling AI.

1. Data-to-Fairness Risk: While best-in-class data platforms excel at governing static data (data quality, lineage, and sensitivity classification), they cannot quantify the downstream effects of that data. A model built on compliant data can still produce biased outcomes. The gap is the inability to run the quantitative, outcome-based fairness tests required to prove algorithmic equity before and after deployment.
2. Independent Validation Gap: MLOps platforms are a powerful system of record for model developers (Line 1 Defense). They record model versions and performance. However, regulatory best practices, particularly in model risk management, demand that validation (Line 2 Defense) be independent and objective. Audits require proof of validation tests run by a system separate from the model's creator. Relying solely on the developer’s MLOps logs creates a conflict of interest.
3. GRC and IT management platforms use subjective risk scoring to measure model risk. They use high-level use case characteristics and subjective attestation, like manager questionnaires. This gives your risk score an organizational view, but little technical substance. When asked to defend a high-risk model, the GRC team provides a signed document, not verifiable, real-time performance data from the model itself. The subjective score fails to stand up to regulatory scrutiny.
4. Emergent Behavioral Risk: The rise of autonomous, multi-step Agentic AI—often built on Generative AI—breaks every traditional monitoring system. With AI agents performing multiple steps and making autonomous decisions, continuous validation is critical. This is where real-time delegation to an AI governance tool is needed to continuously monitor how the system works and maintain it against the goals it was designed for.

People and process friction: The challenge for established GRC programs

Even as you aspire to fill critical gaps in your GRC technology stack for robust AI governance and assurance, there are critical people and process challenges to consider.

The overwhelming adoption of AI is forcing GRC teams to confront deeply ingrained operational practices and assumptions. The challenge is not only adopting technology. Teams must also evolve from a culture of compliance to an enabler of responsible and sustainable new growth:

Invest in a purpose-built AI model assurance engine

To overcome technical and systemic challenges, GRC leaders must advocate for a purpose-built AI governance platform as an investment that complements, rather than competes with, existing enterprise tools. By using model governance as an assurance engine in the stack, it acts as the technical auditor to close the “assurance gap”. This gives the C-suite confidence and gives clear, real-time proof to stakeholders.

Core capabilities of an assurance engine:

1. Pre-Deployment Proof: Provides automated, quantitative validation capabilities, such as stress testing, robustness checks, and fairness simulations, that models must pass before they are released from the MLOps environment. This reduces manual testing hours and slashes the time a model spends waiting for approval.
2. Continuous, Live Monitoring: Creates real-time rules for all models and agents. It records every production decision (full transaction history) to check for drift, bias, or sudden performance problems. This serves as an early warning system that protects the enterprise from catastrophic model failure.
3. Policy-to-Evidence Mapping: The platform is natively designed to translate complex regulations (e.g., the EU AI Act, NAIC Model Bulletins) into clear, technical controls and automatically gathers the evidence from the MLOps pipeline needed to satisfy them.

By prioritizing solutions with these capabilities, you can build a future-proof system that instills confidence at the executive level and offers clear, real-time evidence to all stakeholders.

Integration strategy as a force multiplier

Look for an AI governance platform that helps your business invest in AI. This will create a closed-loop governance and assurance system at all stages of the model development process.

• It ingests model and data lineage metadata from MLOps and data governance services.
• It generates objective, verifiable technical risk scores.
• It sends that data back to Privacy and GRC platforms to base the risk assessment on data. It also triggers remediation tickets and closes the loop on compliance controls.

Focusing on these strategic integration points within your existing platforms can also mitigate the risk of a steep tech and process adoption curve for stakeholders as you iterate on AI use cases for sustainable investment.

GRC leaders are positioned to be an enabler of AI business value

Investing in AI governance and assurance is not about adding more tools. It is about realizing that AI requires a new level of GRC responsibility and accountability. For that, you must have the right tools for the right jobs. You must also shift from managing processes to managing validated and objective proof that AI is working as intended and at the appropriate level of investment.

Leaders must evaluate AI governance and assurance vendors [such as Monitaur] and pilot them with a high-risk use case. Choose a platform that shows the deepest technical fidelity and the strongest native integration with your existing GRC and MLOps platforms.

The time to act is now. Your competitors are deploying AI for a competitive advantage; your organization must ensure that it is done responsibly, scalably, and demonstrably. Make the case for an investment that turns AI governance and model risk management into a strategic enabler of long-term business value.

Citations

• Top 5 Governance Considerations for Agentic AI
• MLOps isn’t necessarily model governance
• AI governance for foundation models and generative AI
• 3 key steps to build a scalable AI governance framework (Gartner, Jan 2025)
• MLOps system design has a governance problem
• The Essential Guide to AI governance (OCEG™, 2024)
• Case study: How a leading insurer leveraged AI governance automation to accelerate growth