AI-enabled Compliance: Balancing Potential and Risk
AI in compliance is becoming an integral part of many organisations’ daily operations, ranging from automated contract review to advanced anomaly detection. Regulators acknowledge that while AI can significantly enhance compliance functions by improving detection capabilities and automating resource-intensive tasks, it can also amplify risks. Organisations that adopt AI-driven compliance tools for continuous monitoring, fraud detection, and predictive analytics must deploy these tools responsibly and effectively.
The US Department of Justice (DOJ) has clarified that businesses using AI to achieve their objectives are also expected to meet their compliance requirements. This guidance highlights the dual role of AI as both a compliance enhancer and a potential risk amplifier. For compliance officers, the challenge lies in balancing innovation with accountability, transparency, and a commitment to ethical design.
By proactively addressing key AI risk areas—bias, misuse, and data privacy—compliance programmes can mitigate potential pitfalls. Strong governance frameworks, continuous monitoring, and regular training are essential to ensure that AI-enabled compliance tools add value to a company’s compliance function. As technology evolves, compliance teams must adapt their risk assessments, oversight mechanisms, and internal controls accordingly.
AI-related Risks
To responsibly deploy AI resources, compliance leaders should consider and plan to mitigate three key risk areas.
1. Bias and Discrimination
AI tools rely on defined datasets for training. If these datasets are skewed—due to historical inequities, incomplete data, human error, or flawed assumptions—algorithms can perpetuate or exacerbate bias. For instance, an AI-powered internal risk monitoring tool may flag an employee with a flexible work arrangement for family health issues as having suspicious logins. If not handled properly, this could expose the business to a discrimination claim. Compliance leaders must routinely test and audit AI outputs to ensure that design and training processes account for fairness and ethics, aligning with the company’s values.
2. Fraudulent and Unlawful Uses
Bad actors, whether internal or external, can exploit AI to facilitate sophisticated fraud schemes. Advanced algorithms may assist in evading sanctions, laundering money, or deciphering a company’s internal controls. Insiders could use AI to enable schemes such as insider trading, embezzlement, or billing-related fraud. Regulators will expect compliance programmes to demonstrate robust oversight of AI-enabled processes, making AI systems monitoring a central priority for compliance teams.
3. Data Privacy and Security
AI systems thrive on data, often containing personal, financial, proprietary, or other sensitive information. The handling of such sensitive data raises data privacy, cybersecurity, and reputational risks. Regulations like the EU’s and U.K.’s General Data Protection Regulations and the California Consumer Privacy Act impose strict rules for data handling and individual privacy protection. AI-enabled compliance programmes must ensure robust management of sensitive data, both in storage and during processing.
Integration and Governance Strategies
Integrating AI into Compliance
When utilized correctly, AI can revolutionize compliance activities, enabling real-time transaction monitoring, predictive analytics for high-risk deals, and advanced analytics for third-party due diligence. AI excels at automating tedious tasks, such as screening extensive vendor datasets, allowing compliance teams to focus on more strategic objectives.
Decision-makers should resist deploying AI for its own sake or simply to keep pace with business leaders’ trends. A thoughtful, bottom-up implementation plan that aligns with specific compliance objectives is essential.
Establishing Governance Frameworks
AI tools require a robust governance framework for success. Cross-functional groups must oversee and develop governance structures to guide AI strategy, model development, and performance metrics. These structures should outline:
- Auditability: How will the tracking of AI algorithms’ decision-making processes be conducted?
- Ethical Safeguards: What methods will be employed to test and analyze outcomes for consistency and bias?
- Accountability: How will the organization respond if an issue arises? Who is responsible for the functioning of AI compliance tools? Can the compliance team disable AI tools that are concerning?
A strong governance framework will help answer critical questions posed by the DOJ guidance during potential corporate compliance evaluations, including:
- Is the management of AI-related risks integrated into broader enterprise risk management strategies?
- What is the company’s governance approach regarding the use of new technologies like AI in its compliance programme?
- How does the company mitigate any unintended consequences from technology usage?
- What controls are in place to monitor AI’s trustworthiness and compliance with applicable laws?
- What baseline of human decision-making is used to evaluate AI?
- How is accountability over AI usage monitored and enforced?
- How does the company train employees on emerging technologies like AI?
Transparency and Explainability
Regulators and stakeholders—many of whom lack AI expertise—will demand explanations for AI-driven decisions. “Black box” models, where data scientists struggle to explain how a model arrived at a conclusion, may face scrutiny during investigations.
Compliance leaders must balance the sophisticated capabilities of AI with the need for transparency. Simpler, more interpretable models can enhance compliance without sacrificing accountability.
Managing Risk and Adapting
Dynamic Risk Assessments
AI evolves rapidly by design. A well-tuned model today could become a risk vector tomorrow if underlying datasets or business processes change. Compliance teams must integrate AI risk assessments into existing enterprise risk management processes to quickly identify and mitigate vulnerabilities.
Training and Awareness
All stakeholders, including compliance officers, in-house counsel, finance team members, and information security teams, require a foundational understanding of AI’s capabilities and limitations. A superficial overview is insufficient.
Team members, including executives, must know which systems rely on AI and possess the technical fluency to identify red flags and escalate concerns appropriately. Board members and C-suite leaders must appreciate both AI’s value and risks, balancing resources allocated for risk management with those for business value realization.
Keeping Pace with Regulations
As AI matures, regulations will evolve. Although regulations often lag behind technological advancements, global regulators are beginning to implement AI-specific legislation. New rules will dictate how AI systems must be designed, monitored, or disclosed.
Multinational companies need to monitor regulatory changes across the global enforcement landscape and update their compliance programmes accordingly. The current regulatory emphasis on privacy, transparency, and auditability is likely to persist, pushing forward-thinking organizations to build or acquire AI tools that can adapt to future regulatory shifts.
Embracing AI Responsibly
AI will increasingly become central to compliance programmes over the next five years, offering deeper insights and faster response times. By aligning AI with legal and regulatory standards now, organizations can harness its potential while safeguarding against emerging risks.
The hype surrounding new technologies can cloud judgment. Rather than racing to adopt AI merely to avoid falling behind, professionals must approach adoption sensibly. By remaining vigilant, flexible, and informed, compliance leaders can successfully integrate AI tools while fostering a culture of integrity and trust—today and into the future.
