AI in GRC: Trends, Opportunities and Challenges for 2025

Introduction

Artificial Intelligence (AI) is rapidly becoming an indispensable co-pilot for risk and compliance leaders, enabling faster anomaly detection, streamlined compliance workflows, and more effective management of evolving regulatory requirements. The big question is no longer whether AI will be central to governance, risk, and compliance (GRC) but how organizations can harness its potential without sacrificing trust, accountability, or resilience.

Drawing on insights from MetricStream’s and the GRC Report’s 2025 GRC Practitioner Survey Report, this blog highlights the top AI priorities, opportunities, and challenges in the GRC space.

AI Emerges as a Strategic Priority for GRC in 2025

With regulatory change cycles accelerating, data volumes ballooning, and expectations around speed, accuracy, and accountability changing, GRC leaders understand that manual approaches aren’t built to scale with these dynamics. The survey results revealed key stats on how AI is quickly emerging as a strategic priority in 2025.

• 43.12% of GRC professionals are actively evaluating AI solutions to understand their fit and value
• 34.86% are planning for AI’s future potential, building roadmaps even before piloting specific use cases
• 13.76% have already integrated AI into their GRC frameworks, signaling a growing cohort of early adopters

AI, when implemented thoughtfully, simplifies GRC by becoming an enabler, making risk insights more immediate, compliance monitoring more adaptive, and governance decisions more data-driven. The survey results also demonstrate that GRC leaders are opting for a deliberate, phased approach. Rather than leaping straight into production, many organizations are piloting in low-risk areas, assessing data readiness and governance models before broad rollout.

Opportunities and Challenges of AI in GRC

The survey revealed 46.85% of GRC professionals identified AI adoption as both an opportunity and a challenge, an acknowledgment that while the learning curve is steep, the potential to transform decision-making and scalability is too significant to ignore.

Top 5 AI-Driven Opportunities in GRC

When asked to highlight the areas where active pilots or projects are being implemented, the respondents highlighted these five critical areas:

• Risk Monitoring and Reporting (48.24%)
  Risk monitoring is no longer reactive. With AI ingesting live feeds—from financial metrics to third-party data—risk teams get an uninterrupted pulse on emerging issues. When a red flag goes up anywhere in the system, risk teams are notified immediately.
• Automating Compliance Workflows (43.53%)
  Almost half of GRC teams have turned to AI to shoulder the heavy lifting of routine compliance tasks. By automating controls monitoring, regulatory reporting, and audit documentation, they’re reducing the hundreds of manual hours previously spent on these processes while ensuring consistency every single time.
• Strengthening Threat Detection & Incident Response (37.65%)
  Close behind, AI-driven security use cases are taking off. Advanced models now sift through network logs and user-behavior patterns in real time, spotting anomalies that human analysts might miss. The result? Far faster investigations and a dramatic reduction in “dwell time” for cyber threats.
• Harnessing Predictive Analytics for Risk Identification (35.29%)
  With predictive analytics for risk identification, organizations are able to highlight potential risk hotspots, such as supply-chain disruptions, before they escalate.
• Elevating Third-Party Risk Management (21.18%)
  AI is helping organizations manage risk in the extended enterprise. By automating the analysis of vendor data, ESG disclosures, and even real-time news feeds, AI gives teams a richer, continuously updated view of supplier health—long before a contract breach or reputational hit shows up in the headlines.

The above 5 use cases illustrate how AI is shifting GRC from a reactive, checklist-driven function to a proactive, insight-driven engine, empowering organizations to stay one step ahead of risk and compliance challenges.

Top AI Adoption Challenges in GRC

While the promise of AI in GRC is exciting, the survey also highlighted that GRC teams face significant obstacles before they can fully capitalize on these capabilities. Below are the six most-cited challenges, ranked by the share of organizations reporting them:

• Integration with existing systems and workflows (47.75%)
  Legacy platforms and bespoke data models often lack the APIs or architecture needed for seamless AI ingestion, forcing teams into costly workarounds or data re-engineering.
• Lack of skilled talent to manage AI systems (45.95%)
  Building AI is one thing—but operating it responsibly is another. Organizations report acute shortages of professionals who combine deep technical know-how with GRC domain expertise.
• Regulatory uncertainty around AI usage (43.24%)
  With guidelines on AI explainability, accountability, and ethical use still evolving, compliance teams find themselves navigating a constantly shifting rulebook.
• Potential risks—e.g., cyberattacks, data exposure (40.54%)
  Misconfigured models or unsecured pipelines can become new attack vectors, propagating errors at machine speed and exposing sensitive data far more widely than manual processes ever could.
• Data quality and availability (36.94%)
  Incomplete, inconsistent, or siloed datasets undermine model accuracy and erode stakeholder trust—often before pilots even get off the ground.
• Ethical and bias concerns in AI algorithms (36.04%)
  Unvetted training data can bake in discriminatory patterns, skewing risk scores or decision-support outputs in ways that may run counter to an organization’s compliance and inclusion goals.

Few technologies have entered the GRC conversation with the same mix of anticipation and scrutiny as artificial intelligence. At once seen as a lever for strategic acceleration and a source of deep complexity, AI is reshaping how organizations think about compliance, risk, and resilience. It offers the potential to reimagine traditional frameworks but also introduces new dimensions of risk that demand thoughtful governance and operational maturity. While AI’s strategic upside is becoming clearer, so too are the pain points.

What Do the Findings Mean for GRC Leaders?

As AI continues to make inroads into GRC, the true challenge for leaders lies in capitalizing on AI's advantages while exercising oversight for ethical implementation. GRC leaders seeking a strategic differentiator will have to move beyond surface-level adoption with point solutions and focus on crafting AI strategies anchored in the organization’s core values.

Here’s how GRC leaders can seize the AI opportunity:

• Crafting a clear, pragmatic AI strategy
  Leaders need a focused roadmap that places AI within context, aligned with organizational priorities and internal readiness.
• Investing in skills, not just tools
  Training should focus on both technical fluency and the judgment to question what the system gets wrong. Building AI literacy across GRC teams is fast becoming a strategic differentiator.
• Embedding ethical principles into development
  Ethics can’t be an afterthought in AI design. It needs to be part of the build process, especially when decisions could affect real-world compliance outcomes.
• Making risk, compliance, and IT speak the same language
  AI in GRC’s full value is realized when silos collapse. Integrated teams following a connected GRC strategy ensure risks are seen not in isolation but through a unified lens.

Final Thoughts

The opportunity of AI in GRC is real. Organizations that invest in the right strategies, tools, and talent today will be better positioned to lead in a future where intelligent, adaptive GRC is the norm.