Almost every organization today uses artificial intelligence (AI) to improve efficiency and hone their competitive edge. In fact, McKinsey reports that 88% of organizations regularly use AI, with one-third saying they have begun scaling their AI programs. Yet despite this enterprise-wide adoption, many governance, risk and compliance (GRC) professionals still view AI risks primarily through a cybersecurity lens, focusing narrowly on addressing IT-related AI threats. While artificial intelligence has introduced a new class of cybersecurity threats, those risks represent only one aspect of AI in enterprise risk management.

The Limits of a Cybersecurity-Only Mindset

It makes sense that AI-generated cyber threats grab the most attention. After all, 78% of Chief Information Security Officers (CISOs) report that AI-powered threats are already having a significant impact on their organization. The stakes are also high. IBM’s 2025 report estimates the average cost of a single cyber incident at $4.4 million.

But there is more to managing enterprise AI risks. Beyond security threats, AI technologies introduce risks that traditional cybersecurity measures often miss:

  • Legal exposures
  • Ethical concerns
  • Operational vulnerabilities
  • Compliance gaps
  • Workforce challenges

Your AI risk management framework should take a multidisciplinary approach to identify and mitigate broader operational and governance exposure.

Related Article: Shadow AI Risks: Why Your Employees Are Putting Your Company at Risk

AI creates complex legal issues around ownership and copyright. Training data often includes protected material, and reproducing it during model training may constitute infringement without proper licensing. For example, Disney and Universal sued AI firm Midjourney for making “innumerable” copies of their character to train its image generator without authorization.

AI-generated works are not eligible for copyright protection unless there’s a significant human contribution. So your organization cannot protect content independently produced by generative AI. Competitors or third parties could legally reuse your AI-generated output without consequences.

Contractual and Confidentiality Risks

You might also face contractual exposure if you use a third-party AI tool that doesn’t meet regulatory or contractual obligations. Employees might input sensitive or proprietary information into a public AI tool, which could be stored or reused in future model training, leading to intellectual property loss or confidentiality breaches.

Combating AI-related legal risks requires proactive management. Involve the legal, compliance and other departments in your AI risk assessment to close gaps early and reduce the likelihood of costly disputes.

Ethical and Accuracy Risks in Automated Decision-Making

According to Signal AI’s report, 85% of business leaders say AI-driven decision-making could add up to $4.26 trillion to the U.S. economy annually. Yet the same potential that makes AI a powerful driver of growth also carries significant risks if mismanaged.

AI Bias

AI-driven decisions can amplify bias. Consider an AI recruiting tool trained on past hiring data from an organization that historically hired more men than women for technical roles. Even if gender isn’t explicitly part of the data, the AI can learn patterns that favor male candidates. It can rank them higher or recommend them more often. 

If not addressed, using this AI will amplify existing biases, potentially leading to unfair hiring practices or discrimination that can damage an organization’s reputation.

Hallucination 

AI algorithms sometimes perceive patterns or objects that don’t exist, resulting in nonsensical or inaccurate outcomes. In a 2024 study, popular large language models (LLMs) had high hallucination rates:

  • GPT-3.5: 39.6%
  • GPT-4: 28.6%
  • Google Bard (Gemini): 91.4%

For instance, legal AI models might include references to non-existent laws or case precedents that sound credible but are false and misleading. And if your organization relies on hallucinated output to make decisions, you risk regulatory and operational errors that might lead to reputational damage or financial losses.

Your GRC program should prioritize validation and auditability controls that support accuracy, fairness and accountability across all automated decision-making processes.

Operational and Compliance Blind Spots

AI can introduce interdependence and operational risks that might go unnoticed until a failure occurs. Surveys show 78% of organizations use third-party AI tools, and more than half rely exclusively on them. This dependency is concerning given that 55% of AI failures come from third-party tools.

If a vendor’s platform goes offline or its AI systems produce erroneous output, business-critical processes can grind to a halt or produce flawed outcomes. Overreliance on automation without sufficient human oversight risks cascading operational errors.

What’s more, emerging regulations, including the EU AI Act, U.S. Executive Orders and NIST AI Risk Management Framework, now require organizations to maintain compliance across all systems. Embed AI enterprise risk management into your GRC to manage both operational dependencies and regulatory demands.

Workforce and Organizational Culture Risks

Only 55% of employees trust their organization to implement AI responsibly. If you introduce AI without transparency or clear accountability, employee confidence can decline. Workers may fear replacement or feel disconnected from decisions driven by black-box AI algorithms.

In addition, relying too much on AI tools can erode human judgment and critical thinking. This weakens oversight and increases the chances of poor or noncompliant decisions slipping through. 

Instead of treating AI as the sole authoritative source of information, encourage a company culture where employees can question AI and understand its limitations. You’ll build trust and strengthen accountability across the organization.

How AI Can Support Enterprise Risk Management

Enterprise risk management may feel like constantly playing catch-up in the age of AI. The good news, however, is that AI can also be your greatest tool to stay ahead of emerging risks. Though it introduces complexity in risk management frameworks, AI when properly implemented can bring precision and foresight to your GRC strategy.

1. Early Anomaly Detection

AI can continuously scan large datasets to flag anomalies before they escalate into major issues. GRC professionals can use it to monitor models for:

  • Compliance breaches
  • Bias drifts
  • Security control weaknesses
  • Data quality issues
  • Operational disruptions

2. Automated Compliance Monitoring

As AI introduces new regulatory standards, you can deploy models to track evolving rules and automatically map them to your organization’s use of AI. This automation reduces the risk of noncompliance with emerging regulatory standards and identifies where vendors or internal systems may fall short of new standards.

3. Predictive Risk Modeling

Machine learning can analyze real-time performance data to predict operational disruption or potential failures. These insights can help you act before risks materialize.

4. Enhance Decision Support

Using AI can turn risk data into actionable insights to prioritize critical issues faster. To that end, you can make more informed decisions while maintaining auditability and transparency.

5. Augment Human Oversight

AI should support, not replace, human judgment. Using AI to handle heavy data analysis frees skilled employees to focus on more complex issues of interpretation and governance, keeping accountability firmly in human hands.

Build Enterprise-Wide, Multidisciplinary AI Governance With a Unified GRC Tool

Enterprise AI risk management requires collaboration across legal, compliance, HR, IT and operations for AI to work safely within your organization. A centralized GRC tool such as Onspring helps you coordinate risk management across departments.

With Onspring, you can:

  • Maintain a complete AI-use case inventory and oversight to manage risk at every stage of the model
  • Set ethical AI policies to enforce standards that reflect your organization’s values
  • Monitor models for anomalies to detect issues before they escalate
  • Track vendor and data due diligence to verify third-party AI providers and reduce operational or contractual risks
  • Centralize reporting and accountability to give stakeholders real-time visibility and clarify responsibilities in managing AI risks

By shifting from controlling AI to building resilience around AI, your organization can keep up with the evolving AI risks. Watch the Onspring on-demand webinar “Future of AI Risk Management On-Demand” to learn how leading businesses are rethinking AI risk beyond cybersecurity.