The evolution of AI requires compliance leaders to be forward-thinking and proactively engage with the growing regulatory landscape to mitigate risks and maximize opportunities for innovation.
The current state of AI in the compliance landscape
Artificial Intelligence has rapidly moved from theoretical to transformational, profoundly changing how businesses operate across industries. While the benefits of AI, particularly Generative AI (genAI), are monumental, these technologies introduce a new range of risks. Compliance professionals are contending with an evolving regulatory landscape, where staying ahead requires a proactive and informed approach.
Adoption of AI
AI, and genAI specifically, are increasingly driving strategic business decisions. According to the 2024 NAVEX State of Risk and Compliance Report, 56% of organizations plan to use genAI within the next 12 months. As adoption spreads, there is pressure to integrate robust compliance risks, like data breaches, biased outputs, and regulatory violations.
“By early 2024, 72% of companies reported adopting AI, with significant improvements in supply chain and inventory management, as well as notable revenue increases in marketing and sales.” ( McKinsey & Company)
Regulatory pressure
The growing implementation of AI is driving the need for specific regulations. Compliance professionals face a global landscape of emerging laws like the EU Artificial Intelligence Act and New York City’s AI bias audit requirements . These regulatory efforts, including frameworks like the Artificial Intelligence and Data Act (AIDA) in Canada and sector-specific guidelines in the U.K. and Brazil, mitigate AI risks by enforcing transparency, accountability, and ethical standards.
The EU AI Act, expected to be enforced by 2026, will be the first large-scale AI governance framework focusing on uses with the highest risks. Non-compliance could lead to fines of up to €35 million or 7% of global revenue. This level of regulatory scrutiny highlights the importance of organizations developing proactive governance frameworks to stay ahead of compliance requirements .
Critical challenges in AI governance
As AI reshapes business operations, compliance professionals face several critical governance challenges. These challenges include managing data privacy, mitigating algorithmic bias, and addressing third-party risks while ensuring ethical and transparent AI use. The key to overcoming these hurdles lies in establishing a governance framework that accounts for the risks AI introduces.
Data privacy and cybersecurity
AI-powered systems handle vast amounts of sensitive data, making them attractive cyberattack targets. New vulnerabilities arise when AI is used to analyze and manage data, particularly when cybercriminals leverage AI to exploit those weaknesses. Organizations must prioritize strict data protection measures, including encryption and secure data storage, to prevent breaches and unauthorized access.
Bias and ethical risks
One of the most significant risks of AI is algorithmic bias, where AI systems unintentionally produce unfair or discriminatory outcomes. AI systems are only as good as the data they learn from. Poor quality or biased data can lead to flawed decision-making, especially in more high-stake areas like hiring and law enforcement.
For example, New York City’s AI bias requirements mandate audits for AI tools used in hiring to prevent discrimination, underscoring the need for ethical governance of AI systems. This issue is compounded by a lack of transparency in how AI systems arrive at decisions, often called the “black box” problem. To mitigate this, organizations must prioritize transparency and ensure AI technologies are audited and validated for fairness.
Third-party risks
AI often relies on third-party vendors for development, data and implementation. However, third-party relationships introduce significant risks, as the algorithms and data used by external partners may harbor unseen vulnerabilities or non-compliance with regulatory standards. This emphasizes the importance of third-party risk management in AI governance to vet AI vendors and monitor their compliance with ethical standards.
For example, the rise of ESG reporting highlights the need for AI systems to adhere to environmental, social and governance standards, particularly when third-party partners are involved. If there are gaps in compliance or unethical practices by third parties, your organization could be exposed to reputational damage and legal consequences.
Legal and compliance risks
Legal risks related to AI include intellectual property infringement, data misuse and non-compliance with emerging AI regulations. This evolving regulatory landscape, including the Artificial Intelligence Liability Directive currently being discussed in the EU, highlights the growing pressure on companies to ensure their systems comply with existing laws, like copyright and trade secret protections, with new AI-specific regulations.
As organizations integrate AI into their operations, proactive addressing of these risks through comprehensive legal reviews, compliance audits, and board-level reporting is crucial.
Emerging AI regulatory trends to watch in 2025
As AI becomes more embedded in business operations, regulatory bodies worldwide are accelerating efforts to develop comprehensive AI governance frameworks. Compliance professionals must stay ahead of these evolving trends to ensure their organizations remain compliant and prepared.
Let’s explore some of the regulatory trends anticipated to shape the global AI compliance landscape in 2025 and beyond.
Global regulations
AI regulation is set to expand significantly globally, focusing on data privacy, ethical AI usage, and risk mitigation. The European Union’s Artificial Intelligence Act (AI Act), expected to take full effect by 2026, will likely serve as a global benchmark for AI governance. The AI Act introduces a risk-based approach, categorizing AI systems based on their potential impact on fundamental rights and safety. High-risk AI applications, like those in law enforcement and employment, will be subject to stricter compliance standards .
Other regions are following the EU’s lead. In Canada, AIDA establishes strict standards for automated decision-making systems the federal government uses. China is drafting a holistic AI framework emphasizing data security and regulation of AI use. Although there is no overarching AI-specific regulation at the federal level in the United States, sector-specific laws and voluntary guidelines, including the Biden Administration’s AI “pillars” for responsible AI, influence AI governance.
Industry-specific regulations
Certain industries, like healthcare, finance and employment, are already seeing more targeted AI governance requirements. The healthcare sector faces increased scrutiny over AI applications used in diagnostics and patient care, and AI use in clinical settings poses unique ethical and regulatory challenges. Financial institutions are under pressure to ensure AI systems used in credit scoring and fraud detection adhere to standards for transparency and fairness.
How organizations can prepare for AI governance
To effectively manage AI risk, organizations should build a comprehensive governance framework that aligns with global and industry-specific regulations. This framework should address key areas such as AI usage policies, ethical considerations and compliance oversight.
Building a governance framework
A robust governance framework ensures AI is used ethically and complies with evolving regulations. This includes establishing clear policies on AI deployment, forming oversight committees, and creating mechanisms to monitor AI activities across departments. Notably, only 18% of organizations have an enterprise-wide council authorized to make decisions on responsible AI governance, highlighting the urgent need for structured oversight according to a 2024 McKinsey Report.
Automation and data management
Automation can be vital in streamlining compliance processes, particularly in managing large volumes of AI-driven data. Effective data management practices, such as AI-specific data mapping and real-time reporting, will be crucial for tracking and auditing AI activities, ensuring regulatory compliance while minimizing risks.
Ongoing training and awareness
Continuous education on AI risks and compliance is key to staying ahead of regulatory changes. Organizations should regularly train their teams on the ethical use of AI, new regulations, and best practices to mitigate bias and other AI-related risks. This proactive approach ensures that employees can navigate the rapidly evolving AI landscape.
The future of AI compliance – predictions for 2025 and beyond
In 2025 (and beyond), AI governance will be a critical business imperative. As regulations tighten and the influence of AI grows, companies must establish robust frameworks to mitigate risks and seize opportunities. Proactive compliance is key to unlocking AI’s full potential while avoiding legal pitfalls. Organizations that prioritize AI governance will gain a competitive edge and drive sustainable growth.
2025 Top 10 Trends in Risk and Compliance
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