AI Compliance

Now more than ever, organisations are being held accountable by increasing legal and regulatory obligations that dictate how organisations design, implement, and use artificial intelligence (AI) systems. Increasingly stringent standards, such as the EU AI Act, GDPR, and other industry-specific regulations, require organisations to be transparent and accountable and to document their processes at every stage of the AI life cycle.

Gartner’s Senior Director Analyst Roxane Edjlali reports that “63% of organisations either do not have or are unsure if they have the right data management practices for AI.” As more enforcement mechanisms take effect in 2026, these findings imply that organisations are at greater risk, and the challenge lies in building AI governance programmes that prove responsible, compliant practices.

AI compliance ensures an organisation’s AI systems adhere to applicable laws, regulations, industry standards, and internal policies throughout the system life cycle.

Data protection requirements, fair decision-making, and non-discrimination are examples of AI compliance. To comply, organisations keep audit logs and establish processes to follow compliance guidelines set by regulatory bodies like the GDPR, HIPAA, the EU AI Act, and the NIST AI Risk Management Framework. While regulatory compliance has legal implications, it also encompasses ethical concerns regarding transparency, accountability, and safety for users of AI systems.

For proper compliance, organisations must demonstrate that they know what data their AI systems use, how those systems make decisions, and where their use could lead to bias or harm. To conduct a compliance assessment, organisations must maintain an auditable record of their AI systems, monitor their evolution, and document any adjustments as rules change.

AI compliance covers all phases of the AI development life cycle (from conceptualisation to production deployment) and requires compliance obligations for each phase.

Model Development

Compliance during model development includes documenting design decisions, intended use cases, and risk assessments. Data collection and processing must be documented per legal requirements, as with the GDPR and other privacy-related regulations. Compliance documentation should also address the problem the AI system was developed to solve, its limitations, and the possible risks that may result.

Training

Datasets used for training require strong governance, as organisations must provide evidence that the data do not contain personally identifiable information, don’t promote or contain discriminatory elements, and don’t include unauthorised copyrighted materials. High-risk AI systems are required to use high-quality datasets that minimise bias. Fairness testing is often a compliance requirement, not just an ethics consideration.

Deployment

During deployment, organisations must implement rigid technical controls around logging, access control, and output filtering. Accountability is a key element, particularly with the NIST AI Risk Management Framework, which includes establishing clear lines of authority for AI system behaviour. Additionally, safety controls must be implemented to prevent the AI system from producing misinformed outputs, causing financial loss, or incurring reputational damage.

Monitoring

Monitoring is necessary to ensure that compliance doesn’t slip over time. AI systems can “drift” as new data distributions become available or when users find unintended uses for the system. Monitoring includes tracking model performance, detecting bias, and capturing incident data for investigation purposes. Organisations must create audit trails to demonstrate continuous oversight and trace decision-making back to the specific model version and the individuals or parties accountable for those decisions.

Non-compliance carries steep consequences. Organisations face regulatory fines, legal liability, reputational damage, and operational disruption when AI systems violate laws or ethical standards. The EU AI Act imposes penalties up to €35 million or 7% of global revenue for serious violations.

In addition to financial risk, failure to adhere to regulatory requirements diminishes trust and encourages increased regulatory scrutiny, which can impede the development of future AI projects. AI compliance also impacts specific roles within an organisation.

CISOs are responsible for demonstrating compliance in all forms of AI deployment throughout the organisation. Boards and regulators require CISOs to provide assurance of the organisation’s AI risk posture as well as established controls designed to help mitigate those risks.

SecOps and incident response teams require compliance frameworks that describe what constitutes an “AI incident” and what evidence is required to support a regulator’s investigation should an incident occur. Failure to comply with regulatory requirements results in missing telemetry and/or documentation necessary to respond to an incident.

CIOs/CTOs are constantly balancing the desire for innovation with the need for risk mitigation. Compliance provides the regulatory boundaries for AI adoption while allowing CIO/CTO to safely adopt new AI technologies without applying restrictive practices that inhibit business performance.

The role of compliance/risk professionals is to convert emerging regulatory requirements into actionable policy within the organisation. These professionals are responsible for auditing AI systems, maintaining audit records, and providing assurance that the organisation has complied with multiple, overlapping regulatory requirements.

Legal/privacy professionals address liability and data protection issues related to using AI systems. They’re responsible for ensuring that the organisation’s AI systems don’t infringe upon individuals’ rights. These professionals make sure that the organisation obtains individual consent in all jurisdictions where it conducts business.

As jurisdictions begin to implement AI regulations, an increasing number of organisations must navigate a rapidly changing and overlapping AI compliance frameworks.

EU AI Act

The EU AI Act became effective in August 2024 and is expected to go into full effect in August 2026. High-risk AI systems face strict obligations, including adequate risk assessment, high-quality training datasets to minimise discrimination, logging for traceability, detailed documentation, human oversight, and strong cybersecurity.

U.S. AI Policy and State Laws

Unlike many other developed countries, the U.S. doesn’t currently have comprehensive federal legislation regarding AI. As a result, there are numerous state-level regulations governing AI.

In December 2025, President Trump issued an Executive Order to provide the least burdensome national AI policy framework and to preempt conflicting state laws. As a result, organisations are left to navigate between emerging federal guidance and existing state regulations and compliance requirements, such as those found in California and Colorado.

International Harmonisation Efforts

Many countries around the world are also implementing their own AI regulation frameworks, including China’s PIPL, Japan’s APPI, and Brazil’s LGDP. As a result, global organisations must develop compliance programmes that are flexible and adaptable to evolving global frameworks and attempts at regulatory coordination from organisations such as the OECD and G7.

Sector-Specific Rules

In addition to complying with the applicable AI regulatory frameworks, infrastructure organisations must also follow the guidelines provided in the DHS Framework for AI in Critical Infrastructure, which provides voluntary guidance for energy, financial services, and healthcare sectors to ensure AI systems are safely and securely deployed within these industries.

Healthcare organisations must also ensure that all AI systems process and store protected health information (PHI) in compliance with the provisions of the HIPAA Privacy Rule and Security Rule. Additionally, financial institutions are subject to current regulations that require them to oversee AI-driven decision-making and algorithmic trading.

Privacy Law Intersections

Compliance with AI regulations can’t occur independently of data protection compliance obligations. For example, under the GDPR, the organisation must identify a legal basis for processing any personal data in an AI system and perform a Data Protection Impact Assessment (DIPA) on any high-risk application of AI.

If an organisation processes PHI in the context of healthcare, it must ensure that the AI systems processing this PHI comply with all provisions of the HIPAA privacy and security rules. In turn, organisations must take a holistic view of compliance, considering both data protection and AI regulations rather than treating them as standalone entities.

Organisations implement AI compliance through both voluntary and legal guidelines, including:

• The NIST AI Risk Management Framework is an operational structure for evaluating, assessing, and mitigating potential risks related to AI throughout its life cycle. It provides teams with a framework for mapping risks, assigning responsibility, establishing controls to meet regulatory compliance, etc.
• ISO/IEC has developed AI standards that provide a pathway for certification of the organisation’s quality management and security practices. The ISO/IEC AI Standards will help organisations meet their regulatory requirements while implementing operational best practices.
• Ethical AI guidelines are being implemented by corporations as a foundation for their organisational policies regarding fairness, transparency, and accountability. Organisations are developing review boards to assess the appropriateness of deploying an AI solution before releasing it into production.
• Integration of Governance, Risk, and Compliance (GRC) platforms ensures that AI governance is connected to the enterprise-wide risk registers, audit schedules, board reporting, etc., rather than existing independently.
• Documentation and auditing traceability form the basis of compliance. Organisations must document all aspects of model development decisions, training data sources, validation results, incident investigations, etc., to produce proof of oversight when requested by regulators.

AI compliance programmes face many hurdles within an organisation.

• Governance is fragmented if each department implements its own AI initiatives without a central governing body. Departments can have conflicting policies that create gaps in risk management and accountability.
• Shadow AI and unsanctioned tools allow employees to test external services beyond corporate control and create unmonitored data flows to third-party providers, creating holes in governance.
• Lack of skills and domain expertise by compliance and IT teams makes it difficult for them to identify potential technical risks and convert regulatory requirements into actionable compliance controls. Typically, compliance teams do not understand AI, while technical teams do not understand regulations.
• A compliance team’s ability to determine an organisation’s exposure to AI use is limited. An organisation typically doesn’t know where all its AI resides, what data it accesses, or how it operates.
• Organisations are forced to navigate through multiple jurisdictions with differing regulatory requirements and conflicting frameworks that impose different obligations. Due to the lack of standardisation, multinational organisations must comply with overlapping regulatory frameworks that may be inconsistent.
• Due to audit and documentation challenges, compliance teams are unable to provide proof of oversight when a regulator requests evidence. Many organisations lack the logging and record-keeping capabilities required to support compliance.

As we look toward the near future, several trends will dramatically alter how organisations develop their approaches for achieving AI compliance. One of these is automated compliance verification tools that will provide documentation of an organisation’s current compliance posture, perform gap analysis, monitor regulatory changes, and ensure the organisation is always ready for an audit by minimising manual effort. Real-time compliance monitoring allows an organisation to recognise “compliance drift” when an AI system evolves beyond its original intent and does so without the need for periodic assessments.

While there has been some movement toward global regulatory harmonisation with the adoption of international standards like ISO 42001, jurisdictions continue to create conflicting requirements, which can cause fragmentation in compliance strategy development. As regulators begin to move away from providing guidance and, instead, impose actual penalties, enforcement of AI regulations will become much more aggressive. And while this may start to affect companies in 2026, it will be clear by then that the hype around AI has given way to accountability for AI.

As regulators require greater transparency, organisations will find themselves under increasing pressure to provide more information about the methods used in their AI systems. To meet those expectations, organisations will need to invest in the explainability and auditability of AI systems and its decision-making processes and data processing.

Organisations can strengthen their AI compliance posture by adopting practical measures that align technical operations with legal and regulatory requirements.

Incorporate AI Projects within Existing Compliance Workflows

Before deploying AI systems, involve legal and compliance departments in reviewing AI-related use cases to determine regulatory obligations and establish controls.

Map Relevant Regulations to AI System Use Cases

Create a matrix to map your AI system to the relevant regulatory obligations (e.g., data processing, decision-making authority, risk classification). This map can help teams identify applicable regulatory obligations and prioritise the necessary compliance actions for AI systems with the highest risks and the most stringent regulatory requirements.

Document and Track Model and Data Development

Keep a complete record of the decisions made during development (i.e., the data used to train the model), the results of validations, the approval of deployment models, and any investigations into incidents involving an AI system. A complete documentation trail provides a record of organisational oversight for regulatory agencies when they request documentation.

Establish Cross-Functional Governance Body

Establish a compliance council composed of legal, compliance, security, engineering, and business to oversee the implementation of AI systems. This group should provide a collective evaluation of the potential risks associated with implementing an AI system.

Provide Training to Users Regarding Compliance Expectations

Train users on what data they can input into an AI system, how to identify potentially risky input prompts, and when to report compliance concerns. The training programme should be role-specific, with developers educated on model governance and end-users trained in safe interaction practices.

Continuously Monitor Changing Regulations and Policies Related to AI

Continuously monitor changes to AI-related regulations and policies across all jurisdictions in which they do business and determine whether and how these changes impact their current compliance programme. Clearly define organisational responsibilities for monitoring changing regulations and policies so that organisational compliance protocols can evolve.

Proofpoint supports organisations in meeting their AI compliance obligations by providing integrated solutions to protect sensitive data and classify it before it enters an organisation’s AI systems. Cross-channel monitoring enables organisations to gain visibility into all AI use cases across multiple channels (email, collaborative workspaces, and cloud applications). Audit and reporting capabilities provide the documentation and log records required for regulatory compliance audits.

Proofpoint’s platform integrates with an organisation’s existing GRC workflow, enabling organisations to manage AI compliance as part of their broader GRC programme. Additionally, user and behaviour analytics would allow organisations to enforce policies that balance enabling end-users with controlling what they can do. Ultimately, organisations will have the visibility and supporting documentation to demonstrate that they are practising responsible AI, while also allowing their staff to benefit from AI innovation and its associated benefits in a safe manner. Contact Proofpoint to learn more.