AI in compliance is here. It's embedded in many organisations’ daily operations, from automated contract review to advanced anomaly detection. Regulators know that while AI can supercharge compliance functions by enhancing detection capabilities and automating resource-intensive tasks, it can also amplify risk. Organisations adopting AI-driven compliance tools for continuous monitoring, fraud detection and predictive analytics must deploy these tools responsibly and effectively.
The US Department of Justic (DOJ) has clarified that, to the extent that a business uses AI to achieve its business objectives, it expects that business to achieve its compliance requirements. DOJ’s guidance also underscores AI’s dual role as a compliance enhancer and a potential risk amplifier. For compliance officers, the path forward involves balancing innovation with accountability, transparency and a commitment to ethical design.
By proactively addressing AI’s key risk areas – bias, misuse and data privacy and cybersecurity vulnerabilities – compliance programmes can mitigate pitfalls. Strong governance frameworks, continuous monitoring, and regular training ensure that AI-enabled compliance tools add value to a company’s compliance function. As technology evolves, compliance teams’ risk assessments, oversight mechanisms, and internal controls must adapt to keep pace.
AI-related risks
To responsibly and effectively deploy AI resources, compliance leaders should consider and plan to mitigate three key risk areas.
- Bias and discrimination AI tools rely on defined datasets for training. If those datasets are skewed – due to historical inequities, incomplete data, human error or flawed assumptions – algorithms can perpetuate or exacerbate bias. An AI-powered internal risk monitoring tool might flag an employee with a flexible work arrangement to accommodate a family health issue as having suspicious logins. Unless 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, and that they align with the company’s values.
- Fraudulent and unlawful uses
Bad actors – internal or external – can use AI to facilitate sophisticated fraud schemes. Advanced algorithms can help bad actors evade sanctions, launder money or decipher a company’s internal controls. Insiders could use AI to enable or facilitate schemes like insider trading, embezzlement or billing-related fraud. Because regulators who investigate these types of misconduct will expect compliance programmes to demonstrate robust oversight of AI-enabled processes, AI systems monitoring is a central priority for compliance teams. - Data privacy and security
AI systems thrive on data, and AI systems most useful to compliance professionals will likely contain personal, financial, proprietary or other sensitive business and third-party information. And where sensitive data goes, data privacy, cybersecurity and reputational risks follow.
Moreover, regulations like the EU’s and U.K.’s General Data Protection Regulations and the California Consumer Privacy Act impose strict rules for handling data and protecting individual privacy. AI-enabled compliance programmes must account for the treatment of sensitive data – both at rest in their systems of records and when used by the AI tools and the compliance team.
Integration and governance strategies
Integrating AI into compliance
When used well, AI revolutionises compliance activities. Real-time transaction monitoring, predictive analytics for high-risk deals, and advanced analytics for third-party due diligence are all in practice today. AI excels at automating tedious tasks – like screening huge vendor datasets – freeing compliance teams to focus on more strategic tasks.
For this reason, decision-makers should resist deploying an AI solution for AI’s sake or to keep up with business leaders’ wish to follow the trend. Instead, they should insist on thoughtful, bottom-up implementation plan that aligns with specific compliance objectives.
Establishing governance frameworks
AI tools cannot succeed without a robust governance framework. Cross-functional groups must oversee and build governance structures to guide AI strategy, model development and performance metrics. These governance structures should define:
- Auditability: How will you track the way AI algorithms arrive at certain conclusions?
- Ethical safeguards: How will you test and analyse whether outcomes are consistent and free from bias?
- Accountability: How will the organisation respond if something goes wrong? Who is responsible for how AI compliance tools function? Can the compliance team turn off AI tools that raise concerns?
In the event of a potential corporate compliance programme evaluation, a strong governance framework will answer questions that the recent US Department of Justice (DOJ) guidance directs prosecutors to ask, including each of the following:
- Is management of risks related to the use of AI and other new technologies integrated into broader enterprise risk management (ERM) strategies?
- What is the company’s approach to governance regarding the use of new technologies, such as AI, in its commercial business and compliance programme?
- How is the company curbing any potential negative or unintended consequences resulting from the use of technologies – both in its commercial business and its compliance programme?
- How is the company mitigating the potential for deliberate or reckless misuse of technologies, including by company insiders?
- To the extent that the company uses AI and similar technologies in its business or as part of its compliance program, are controls in place to monitor and ensure its trustworthiness, reliability, and use in compliance with applicable law and the company’s code of conduct?
- What baseline of human decision-making is used to assess AI?
- How is accountability over the use of AI monitored and enforced?
- How does the company train its employees on the use of emerging technologies such as AI?
Transparency and explainability
Regulators and stakeholders—many of whom are not AI experts – will ask for explanations of AI-driven decisions. “Black box” models – where data scientists and AI experts struggle to explain how a model reached a conclusion – will not fare well under scrutiny during an external investigation.
Balancing the power of sophisticated AI capabilities and the need for transparency demands the attention of compliance leaders. Simpler, more interpretable models will often enhance compliance without sacrificing accountability.
Managing risk and adapting
Dynamic risk assessments
By design, AI evolves rapidly. Today’s well-tuned model could be tomorrow’s added risk vector if underlying datasets or business processes change. Compliance teams must include AI risk assessments in their existing ERM processes. These assessments will quickly identify and mitigate new vulnerabilities – like shifts in data sources or biased outputs.
Training and awareness
Compliance officers, in-house counsel, finance team members and information security teams need a foundational understanding of AI’s capabilities and limitations. A high-level overview is not enough.
Team members – including executives – must know which systems rely on AI. They must have enough technical fluency to spot red flags and know how to escalate their concerns appropriately within the organisation. Board members and C-suite leaders must appreciate the value and risks of AI and balance the resources allocated to manage risk with the resources allocated to realize business value.
Keeping pace with regulations
As AI matures, regulations change in its wake. Though regulations struggle to keep pace with AI, global regulators are already implementing AI-specific legislation. New rules will shape how AI systems must be designed, monitored or disclosed.
Multinational companies must track changes across the global enforcement ecosystem and update their compliance programs accordingly. Regulators’ current emphasis on privacy, transparency, and auditability will not likely change. Consequently, forward-thinking organisations can build or buy AI tools that can support future regulatory shifts that require greater, or different, disclosures or protections.
Embracing AI responsibly
AI will move more to the forefront of compliance programmes over the next five years. It will offer deeper insights and foster faster response times. Acting now to align AI with legal and regulatory standards will position organisations to harness this technology’s potential while safeguarding them from emerging risks.
The hype that often surrounds new tech can cloud judgement. Rather than racing to not be left behind, professionals must manage these adoption steps sensibly. By staying vigilant, flexible and informed, compliance leaders can integrate AI tools while fostering a culture of integrity and trust – today and into the future.