Organizations face a constant influx of new regulations and evolving requirements, making it difficult to keep up and ensure compliance.

GRC activities often happen in departmental silos (legal, IT, HR, finance), leading to duplication of effort, inconsistent controls, and a fragmented view of overall risk. This makes it impossible to get a holistic picture of an organization's risk posture.

 Many firms, even large ones, still depend heavily on manual data collection, spreadsheet tracking, and manual reporting for GRC. This is prone to human error, time-consuming, and lacks scalability and real-time insights.

As supply chains and partnerships become more complex, managing the GRC posture of vendors and third parties is a significant challenge, introducing vulnerabilities and compliance gaps.

This is a huge and rapidly evolving area. Organizations are grappling with: Algorithmic Bias, Hallucinations, Data Privacy & Security, Model Drift,

Explainability (Black Box Problem). Managing these risks and regulatory compliance are top concerns when scaling Gen AI.

While organizations recognize the value of AI, a significant gap exists between recognizing its potential and effectively integrating it into existing GRC frameworks, citing concerns about data privacy, cybersecurity, and regulatory compliance.

These firms handle highly sensitive client data (financial, legal, personal). Protecting this data from breaches and ensuring compliance with data protection laws (e.g., attorney-client privilege, financial regulations) is paramount.

A GRC failure can severely damage a firm's reputation, leading to loss of clients and revenue. Proactive risk management is key to maintaining trust.