Rethinking governance, risk and compliance (GRC) in the age of AI: Practical shifts for real-time risk and compliance

Governance, risk and compliance (GRC) has traditionally been the safety net of organizations. It ensures that policies are followed, risks are logged and compliance reports are delivered on time. In an AI-driven world, that’s no longer enough.

As organizations rapidly adopt AI tools across departments, the risk landscape is transforming. What was once a predictable, policy-based process is now a real-time, dynamic challenge that demands greater speed, visibility and strategic foresight.

This analysis examines how AI is not just changing what GRC teams do, but fundamentally reshaping the way they operate.

Let’s start with the obvious: the pace of change has outgrown static frameworks.

• Siloed and manual processes: Traditional GRC relies heavily on spreadsheets, emails and disconnected systems. This fragmented approach leads to duplicated efforts, missed risks and slower response times.
• Inability to scale with complexity: Regulatory changes, cyberthreats and global operations are evolving too quickly for legacy GRC systems to manage. These tools cannot provide the real-time visibility or scalability required in today’s complex environment.
• Lack of real-time insight and automation: Without automation and real-time data, organizations are left reacting to risks after the fact. Decision-making slows and opportunities to prevent issues are missed.

Today’s risk environment is complex. AI-driven GRC transforms outdated systems into smarter, faster and more predictive risk and compliance operations.

1. GRC is becoming real time

AI enables continuous monitoring of risks and controls, shifting GRC from periodic audits to always-on oversight, including:

• Real-time detection of policy violations
• Instant alerts for control failures
• Automated updates to risk registers as conditions change

Instead of reviewing access control violations quarterly, AI flags anomalies such as privilege escalations or unusual login activity in real time.

2. AI enables predictive risk management

Machine learning models can identify early indicators of emerging risks, even before a compliance issue occurs. They provide further protection through these methods:

• Predictive models can flag high-risk third-party vendors
• Behavioral analytics can spot potential internal control violations
• Natural language processing (NLP) tools can interpret new regulations and automatically map them to relevant controls

This shift turns risk management from reactive to proactive. For instance, predictive models can detect an unusual spike in privileged account activity signaling a potential insider threat before it escalates.

3. Automation is eliminating manual compliance work

Gathering evidence for audits or updating policy registers manually is no longer necessary.

AI-powered systems can now:

• Automatically scan documents for compliance gaps
• Generate tailored audit reports for different regulatory bodies
• Dynamically map regulation changes to internal controls and policies

The result is significant time savings and reduced human error—still one of the top compliance risks today. For example, AI can scan thousands of policy documents to pinpoint non-compliant clauses before an external audit.

As AI assumes more manual work, GRC professionals must evolve their roles. Here’s what the future requires:

• Strategic thinking: The focus shifts from box-ticking to assessing business vulnerabilities and managing exposure in a volatile environment.
• Tech literacy: While GRC leaders don’t need to know how to code, understanding how AI systems work (and fail) is essential for oversight and governance.
• Cross-functional collaboration: GRC teams must collaborate hand-in-hand with legal, IT, security, product and AI teams to embed governance principles into system design and deployment.

While AI is transforming GRC, it is also creating entirely new governance challenges.

The questions that the GRC must now address include:

• Who is accountable when an AI system makes a wrong decision?
• Can AI-driven decisions be explained to auditors and regulators?
• How do we identify and mitigate bias in AI models?

To address these challenges, organizations must develop AI-specific governance frameworks alongside traditional GRC practices. This approach includes:

• Model risk management policies
• Ethical AI use principles
• Transparency and auditability standards
• Governance across the AI lifecycle

AI is not just another enterprise tool; it is a force multiplier. But without proper governance, it can quickly become a liability.

GRC teams that embrace AI—both as a risk and a tool—are better equipped to move faster, operate smarter and enable innovation rather than slow it down.

The future of GRC is not about control for control’s sake, it is about becoming a strategic partner in scaling trustworthy AI across the enterprise.