AI in compliance explained: what compliance professionals need to know

Artificial intelligence (AI) refers to technologies that can perform tasks that typically require human intelligence, such as analysing information, identifying patterns and supporting decision making. In a compliance context, AI helps with analysis, detection, prioritisation, monitoring and workflow activity across a range of functions.

However, AI does not remove the need for accountability, professional judgement or effective controls. For compliance professionals, AI compliance has two dimensions: using AI within the compliance function itself, and helping organisations identify, assess and manage AI-related compliance risks. 

This guide is designed for compliance, financial crime, risk, governance and related professionals who need to understand how AI is affecting governance, regulation and compliance. As requirements vary by jurisdiction, sector and use case, organisations should consider the rules and guidance applicable to their circumstances. 

What does AI in compliance mean?

AI in compliance refers to the use of computer systems that can analyse information, identify patterns, generate content or support decisions within compliance-related activities. In practice, AI may assist with monitoring, risk assessment, investigations, reporting and other compliance processes, but it does not replace professional judgement or accountability.

Artificial intelligence is a broad category that includes technologies such as machine learning and generative AI. Machine learning systems identify patterns in data and make predictions or classifications, while generative AI creates new content, such as text, summaries or responses, based on prompts.

Agentic AI refers to AI systems that can plan and carry out multi-step tasks with a degree of autonomy. This raises additional questions about oversight and control.

It is important not to confuse AI with compliance automation or RegTech. Not every automated compliance system uses AI, and not every RegTech solution relies on AI. Automation may simply follow predefined rules, whereas AI can adapt its outputs based on data and probabilistic models.

Artificial intelligence, automation and RegTech: key terms

The term "AI compliance" can also refer to governing AI itself. As a result, compliance professionals may both use AI in their own work and oversee an organisation's compliance with AI-related laws, regulations, policies and controls. In most cases, AI should be viewed as a decision-support tool rather than an autonomous decision maker. Human oversight remains essential.

Why AI matters to compliance professionals and organisations

As AI becomes more widely embedded within organisational processes, compliance professionals need a working understanding of how it is used, where it influences decisions, and the risks it may introduce. This is not simply a technology issue. AI can affect governance arrangements, controls, accountability, record-keeping and regulatory obligations across multiple business functions.

The compliance function has an important role in advising, challenging and monitoring AI-related activities. This may include providing input on the adoption of AI tools, reviewing compliance frameworks, assessing risk and helping ensure that systems are used in a way that aligns with organisational policies, specific regulations and external requirements.

AI helps organisations manage growing scale and complexity by supporting activities such as monitoring, analysis and information review. However, these opportunities sit alongside new or changed risks, including issues relating to data quality, bias, transparency, accountability and oversight.

As organisations make decisions about AI deployment, compliance input may be required alongside senior management, legal, internal audit and technology teams. Effective governance often depends on collaboration between these disciplines rather than decisions being made in isolation.

For compliance professionals, understanding AI supports informed professional challenge. The objective is not to become technical specialists in every aspect of AI, but to ask appropriate questions, evaluate evidence and help ensure that governance, controls and decision making remain effective in an evolving operating environment.

How AI can be used in compliance activities

AI can support a wide range of compliance activities by helping professionals review, organise and analyse large volumes of information. Common examples include compliance monitoring, where AI systems identify unusual patterns for further review, or sanctions screening, where names and entities are compared against relevant lists.

AI may also assist with know your customer (KYC) processes by analysing customer and entity information from multiple sources and highlighting information that may require attention. In risk-focused activities, AI can support detection, assessment and risk intelligence by helping teams identify trends, relationships and potential areas of concern.

AI use cases

Other applications include analysing regulatory updates and other regulatory content, reviewing lengthy documents and supporting regulatory change management by tracking changes that may affect policies, controls or obligations. AI can also contribute to reporting processes and workflow automation, helping teams prioritise work and manage growing volumes of information.

Some organisations use AI-powered knowledge tools to help staff locate relevant policies, procedures and guidance more efficiently. Generative AI may also be used as a drafting or research aid, for example when creating summaries, first drafts or background research. However, outputs should be subject to appropriate review, oversight and organisational controls.

For professionals interested in specific applications, including AML and sanctions compliance, individual use cases can vary significantly depending on organisational requirements, processes and risk appetite.

Potential benefits, important limitations and challenges

AI can support compliance teams in processing large volumes of information, identifying patterns, prioritising work and assisting with timely risk analysis. In some well-defined and repeatable tasks, it may improve consistency and help professionals focus their attention on higher-risk areas that require deeper review. These capabilities can contribute to greater efficiency in environments where regulatory requirements, data volumes and operational complexity continue to grow.

However, AI also has important limitations. Performance depends heavily on system design, data quality and the suitability of the information used. Some AI tools can generate unreliable or fabricated outputs, while others may have limited contextual understanding of specific organisational, legal or regulatory circumstances.

AI systems can also reflect or amplify bias, and some outputs may be difficult to explain or justify. In addition, performance can change over time, creating challenges for monitoring and governance. There is also a risk that users place too much trust in automated outputs without sufficient scrutiny.

Balancing innovation with human oversight

Used well, AI can support innovation within the compliance function.  For these reasons, AI should generally be viewed as a tool that supports, rather than replaces, professional decision making. Human oversight remains essential when assessing findings, interpreting context and determining appropriate actions. While AI can assist with analysis, reliability cannot be assumed and professional judgement remains critical, particularly in complex, sensitive or high-impact decisions.

What are the main risks of AI in compliance?

AI can introduce a range of risks that compliance professionals should understand and assess as part of effective risk management. One concern is inaccurate outputs, including AI hallucination, where AI systems generate information that appears credible but is incorrect. Bias is one of the most significant ethical risks, as it may affect outcomes if underlying data or design choices produce unfair or skewed results.

Some AI systems present challenges around explainability, making it difficult to understand how a particular output or recommendation was produced. There may also be concerns relating to data privacy, inappropriate data use, confidentiality and security, particularly where sensitive information is processed or shared.

Model risk and third-party risk management

Additional considerations include third-party risk where organisations rely on external providers, as well as concentration risk if multiple critical processes depend on a limited number of vendors. AI performance can also change over time through model drift, creating ongoing model risk and the need for continued monitoring.

Governance-related risks include insufficient documentation, unclear accountability for decisions and automation bias, where users place excessive trust in system outputs. There is also the possibility of a regulatory or legal mismatch if AI-enabled processes do not align with applicable requirements, which could expose the organisation to enforcement action.

When AI becomes the target: security threats and criminal misuse

AI does not only create risk through error. Systems can be deliberately attacked, and criminals are using the same technology to commit and conceal financial crime.

Adversarial attacks target how a model learns or decides. Data poisoning corrupts training data, while model evasion involves structuring activity, such as transactions, to stay below a detection model's thresholds. Language-level attacks such as prompt injection and jailbreaking manipulate generative AI tools into ignoring their safeguards, for example through instructions hidden in a document the tool is asked to review.

AI-enabled crime uses AI as the weapon. Deepfake audio and video, synthetic identities and automated fraud can undermine customer due diligence and payment controls that rely on seeing or hearing a customer. Shadow AI adds a further risk: when staff use AI tools without approval, confidential data can leave the organisation and gaps appear in audit trails.

Why traditional cybersecurity is not enough

Traditional cybersecurity and security solutions protect networks and code but are not designed to govern how a model interprets its inputs. Effective risk mitigation requires AI-specific controls, such as an AI inventory, adversarial testing, input and output monitoring and clear acceptable-use rules.

Risk and compliance

The importance of each risk varies according to the context. Higher-risk use cases often require more detailed assessment and specialist input from compliance, legal, risk, technology and security professionals.

AI governance, accountability and human oversight

Responsible AI use requires clear accountability, regardless of where ownership sits within the organisation. This typically includes documented ownership, defined roles and decision rights, approved uses and a clear understanding of who is responsible for assessing and managing risks.

Effective AI governance also relies on appropriate documentation. Organisations should understand the data being used, the purpose of the model, its limitations and the decisions it supports. Maintaining an audit trail can help demonstrate how systems are used and how key decisions are made.

Embedding AI in existing compliance frameworks

Meaningful human oversight involves more than simply reviewing outputs. Professionals should be able to challenge results, consider context, identify unusual outcomes and determine when further investigation is needed. Human review remains particularly important where decisions have significant regulatory, legal or operational implications or are likely to attract regulatory scrutiny.

Governance arrangements should sit within the organisation's wider risk management framework and support risk assessment, issue escalation, ongoing monitoring and independent assurance activities. Where external providers are involved, organisations may need appropriate third-party oversight to understand dependencies and responsibilities.

Finally, AI systems should not be viewed as static. Oversight should continue across the AI lifecycle, as changes to models, data sources or use cases may require review, while retirement and change controls can help ensure systems remain aligned with organisational objectives and risk appetite. The specific AI governance framework will vary between organisations, but clear authority, transparency and effective oversight are central to responsible AI use.

The role of compliance professionals in AI-enabled organisations

Advising on regulations, policies and compliance standards

As organisations adopt AI, compliance professionals continue to play an important role in supporting responsible decision making and effective governance. Responsibilities may include advising on regulatory requirements, policies and compliance standards, participating in use-case assessments and identifying potential compliance implications before deployment.

Whether acting as a compliance officer, money laundering reporting officer (MLRO), risk leader or in another governance role, professionals may be expected to challenge assumptions, scrutinise evidence and assess how AI-enabled activities could affect customers, employees and other stakeholders. This includes considering whether proposed uses align with organisational obligations and risk appetite.

Supporting the wider compliance programme

Compliance professionals may also review controls, documentation and governance arrangements, helping to ensure that decisions are supported by appropriate records and accountability mechanisms. Also, that AI oversight is embedded within the wider compliance programme. Ongoing monitoring of outcomes can help identify emerging concerns, while established escalation processes support the management of issues when they arise.

Effective AI compliance typically requires collaboration across multiple disciplines. Compliance teams often work alongside legal, internal audit, risk, data governance, security and technology specialists to understand how systems operate and where responsibilities sit.

While AI can support analysis and information processing, key responsibilities remain human. Professional scepticism, critical thinking and informed judgement are still essential when evaluating evidence, interpreting context and determining appropriate action. The specific operating model may vary between organisations, but the need for effective challenge and collaboration remains consistent.

How AI connects with AML, financial crime and RegTech

AI is increasingly used to support AML and broader financial crime prevention activities. Common applications include AI-assisted pattern identification, transaction monitoring, sanctions screening, customer and entity analysis, fraud detection and the prioritisation of cases or alerts for review. These tools can help organisations process large volumes of information and identify areas that may warrant further attention.

AI may also support KYC activities by assisting with the analysis of customer information gathered from multiple sources. In fraud and financial crime contexts, it can help identify patterns, connections or anomalies that might otherwise be difficult to detect within large datasets.

Many of these applications sit within the wider category of RegTech, which refers to technology used to help organisations manage regulatory and compliance obligations. AI is one component of this broader landscape, alongside other tools designed to support governance, risk management and compliance processes.

However, the use of AI does not remove the need for robust controls. Explainability, testing, validation and human review remain important when assessing outputs and making decisions. AI can support analysis and prioritisation, but compliance professionals must still apply professional judgement and consider the relevant legal, regulatory and organisational context. The role of AI is typically to assist decision making, not to determine compliance outcomes or reporting obligations independently.

How AI regulations and regulatory expectations differ across jurisdictions

AI is not governed by a single global rulebook. Depending on the jurisdiction, organisations may be subject to specific AI regulation, such as the EU Artificial Intelligence Act (EU AI Act), existing legal frameworks or a combination of both. Relevant requirements can extend beyond AI-focused rules to include data protection, consumer protection, equality, financial services and technology-related obligations.

Regulatory standards and expectations can also vary by sector. Some frameworks, such as the AI Act, take a risk classification approach, with stricter requirements for uses considered higher risk. Activities that may be acceptable in one industry or location could be subject to additional requirements, oversight or restrictions elsewhere. For organisations operating across borders, this can create challenges when deploying AI systems across multiple jurisdictions.

Another important consideration is the organisation’s role within the AI supply chain. Different responsibilities may apply depending on whether an organisation develops, provides, procures or uses AI-enabled systems. Questions of governance, documentation and accountability may also differ between parties.

In addition, regulatory frameworks continue to evolve. New legislation, implementation timelines and official regulatory guidance may change over time, affecting how organisations assess and manage AI-related obligations.

For multinational organisations, a consistent governance approach can be helpful, but local legal and regulatory requirements still need to be considered. Compliance professionals should avoid assuming that rules are the same everywhere and should refer to current laws, guidance and specialist advice where appropriate. Because the regulatory landscape is developing rapidly, it remains important to verify the latest requirements before making decisions or implementing AI-related controls.

AI compliance frameworks and standards

Alongside legislation, many organisations draw on voluntary AI compliance frameworks and compliance standards to structure their approach. Examples include the National Institute of Standards and Technology (NIST) AI Risk Management Framework and ISO/IEC 42001, an international standard for AI management systems. These frameworks do not replace legal obligations, but they can provide a common reference point for governance, risk assessment and controls.

Skills compliance professionals need in an AI-enabled environment

As AI becomes more common across organisations, compliance professionals increasingly benefit from developing AI literacy and data literacy. This does not mean every professional needs to code or become a technical specialist. Instead, they should understand how AI systems are used, the types of outputs they produce and the risks and limitations that may affect decision making.

Core capabilities include risk assessment, critical thinking, awareness of regulatory trends and the ability to evaluate evidence rather than accepting outputs at face value. Professionals also need strong questioning and challenge skills to identify assumptions, test conclusions and recognise when additional review is required.

An understanding of ethics and governance is equally important. Compliance teams may be asked to consider fairness, accountability, transparency and whether AI-enabled decisions meet the organisation's ethical standards, as well as the broader consequences for customers, employees and other stakeholders.

Effective communication is another key skill. Compliance professionals often need to work across technical and non-technical teams, translating regulatory, risk and governance requirements into practical actions and informed discussion.

Teams should also recognise the importance of understanding role limitations. Not every issue can be resolved by compliance alone and specialist input may be needed from legal, risk, technology, security or data experts.

Identifying capability gaps through ongoing professional development can help individuals and teams build the knowledge required to provide effective oversight and challenge as AI use in compliance develops.

Questions to ask before using AI in a compliance activity

Before introducing AI into a compliance process, it is helpful to carry out a structured use-case assessment. Start by asking what problem the system is intended to solve and whether AI is genuinely necessary and proportionate for the task. In some situations, simpler approaches may be more appropriate.

A thorough risk assessment should consider the data involved, the purpose of the activity and the potential consequences of errors. Key questions include:

• What data will the system use?
• Are appropriate data governance arrangements in place?
• What could happen if an output is inaccurate, incomplete or misleading?

Clear accountability is also essential. Who owns the decision being supported? Who is responsible for the associated risks? Compliance teams should understand where decision-making authority sits and whether appropriate controls exist to support the process.

It is also important to consider how outputs will be checked. Will human review be required? What level of explanation or transparency is needed to justify decisions or demonstrate compliance obligations?

Other practical considerations include how performance will be measured and subjected to ongoing monitoring, how changes will be managed and whether third-party providers introduce additional risks or dependencies.

Finally, organisations should consider which jurisdictions, laws and regulatory requirements apply; how issues will be documented and escalated; and what knowledge users and reviewers need to operate the system responsibly.

The objective is not simply to assess the technology itself, but to understand how it fits within governance, risk management and decision-making processes.

Frequently asked questions

This guide is for educational purposes only and does not constitute legal advice.