Safely leveraging generative AI: A practical guide for compliance leaders

While these capabilities deliver significant efficiency gains, they also introduce a new and complex set of compliance risks.

These risks include authoritative but incorrect outputs (hallucinations); data privacy and confidentiality exposures arising from “Shadow AI”; emerging security threats such as prompt injection and unintended data leakage; bias and discrimination risks in sensitive decision-making contexts; challenges with auditability and traceability as models evolve; intellectual property and copyright concerns; and rapidly maturing regulatory expectations across jurisdictions, particularly in the EU.

For compliance leaders, the objective is not to slow innovation, but to enable responsible and well-governed GenAI adoption. This column presents a practical, risk-based governance playbook to help compliance teams support GenAI use while maintaining transparency, accountability, and regulatory readiness.

A risk-based operating model

Effective GenAI governance begins with understanding how the technology is actually used across the organization. Rather than relying on static policies alone, compliance teams should establish a comprehensive inventory of GenAI use cases and apply oversight that is proportionate to the level of risk each use case entails.

The Use Case Registry

Before any GenAI application is deployed, it should be registered with the compliance team. This registration should document the business purpose, the data types involved, the specific model and version used, and the degree of GenAI reliance. Establishing this baseline enables compliance functions to identify higher-risk applications early and focus resources where oversight is most critical.

Risk tiering for scalable oversight

Once the use case library is established, organizations should apply tiered risk classification to scale governance appropriately:

Tier 1 (Low): Internal ideation or non-sensitive brainstorming. Example: Using GenAI to draft initial ideas for an internal training presentation or brainstorm audit themes.

Tier 2 (Moderate): Internal research or process support where GenAI is used to improve efficiency, with human review before use. Example: Using GenAI to summarize internal policies or assist with audit planning, with a human reviewing the output before use.

Tier 3 (High/Restricted): Customer-facing outputs, financial reporting, or highly automated decision-support processes in regulated contexts that require documented human approval before execution. Example: Using GenAI to assist in drafting customer communications with documented management review and approval prior to release.

This tiered risk classification allows compliance to focus on material risk rather than attempting to govern low risk AI experimentation with the same level of rigor, enabling innovation while maintaining appropriate governance.

Addressing “Shadow AI” and unauthorized use

Even with a formal use case registry and tiered risk model in place, Shadow AI remains one of the most challenging compliance risks to control. In most organizations, unauthorized AI usage is rarely driven by malicious intent. Instead, it typically emerges when approved tools or processes fail to meet business needs, prompting employees to seek faster, more convenient, or more capable alternatives.

Addressing Shadow AI therefore requires more than prohibition alone. Organizations should adopt a practical, risk-based approach:

• Implement approved, enterprise-grade platforms: Organizations should offer secure, enterprise-approved GenAI platforms that meet data protection, security, and compliance requirements. Blocking public tools without providing practical, approved alternatives often drives Shadow AI further underground.
• Implement technical guardrails: Once approved platforms are in place, organizations should implement technical guardrails to enforce appropriate use. These may include web filtering, network firewalls, and Cloud Access Security Broker rules to restrict access to unauthorized public AI tools and prevent the upload of sensitive data.
• Clarify acceptable use: Policies should focus on what data can be used and for what purposes, rather than attempting to catalog every prohibited tool. Explicit guidance around customer, personal, and other sensitive data is essential to maintaining compliance with data protection and privacy regulations.
• Educate continuously: Mandatory, role-based training should be provided to all employees to ensure employees understand GenAI risks and acceptable data handling practices. Ongoing education reinforces expectations and helps prevent misuse.

Handling policy violations

While guardrails and training are essential to reducing Shadow AI, no control framework is complete without clear and consistently enforced consequences for policy violations. When employees violate AI usage policies, responses should be proportionate to the level of risk involved.

Low-risk violations can often be addressed through targeted education, coaching, and clearer guidance.

Repeated or high-risk violations, such as using unapproved GenAI tools with customer or other sensitive data, should trigger formal investigation, escalation, and disciplinary action in accordance with existing data protection and information security policies.

Compliance functions should also work closely with Human Resources and Legal to ensure AI-related violations are handled within the organization’s broader control and disciplinary framework, rather than treated as a special or isolated category. This approach reinforces accountability, aligns AI governance with established practices, and avoids creating the perception that AI policies are informal or experimental.

Balancing leadership pressure and compliance

Sustainable GenAI governance depends on alignment with business leadership priorities and delivery timelines. Many organizations face strong pressure from senior leadership to deploy AI quickly in order to keep pace with competitors. In these environments, compliance is sometimes perceived as a bottleneck rather than an enabler. The solution, however, is not to resist this pressure, but to engage early and shape adoption in a way that supports both speed and control.

Compliance leaders can enable faster and safer AI adoption by:

• Creating clear guidance for low-risk use cases, allowing teams to move quickly where potential impact is minimal.
• Pre-approving low-risk AI use cases, so teams do not need to start from scratch for each initiative.
• Embedding AI-specific compliance checks into existing workflows, rather than introducing them late in the product release process.
• Maintaining documentation of prior AI initiatives and compliance approvals, so new projects clearly understand expectations and approved patterns.

When compliance is involved from the start, governance enables teams to move faster and more confidently, transforming compliance from a perceived obstacle into a foundation for responsible innovation.

Creating guardrails without clear regulation

Despite growing attention from regulators and policymakers, the U.S. still lacks a single, comprehensive regulatory framework governing artificial intelligence. Organizations must therefore navigate a combination of executive guidance, sector-specific rules, emerging state laws, and voluntary frameworks such as the NIST AI Risk Management Framework.

In this environment, waiting for prescriptive regulation is not practical. Instead, organizations should establish internal guardrails grounded in existing compliance frameworks such as data privacy, consumer protection, fair lending, employment law, and information security. Strong documentation, oversight, and traceability become essential in the absence of clear regulation.

For higher-risk or customer-facing use cases, organizations should require logging of all AI outputs, explicit human review, and clear accountability for decisions influenced by AI-generated results. In addition, guidance from the EU AI Act can serve as a useful reference point, even for U.S.-based organizations. Its risk-based classification, transparency requirements, and expectations for human oversight provide a practical benchmark for designing internal controls. Finally, AI governance should be treated as a living program, with periodic reviews and updates as models, use cases, and regulatory expectations continue to evolve.

Embedding compliance into AI governance

Lastly, compliance should be embedded early and continuously throughout the lifecycle of AI-enabled initiatives rather than engaged as a final approval step. This includes participating in use case design, data selection, vendor evaluation, and deployment decisions to ensure risks are identified and addressed upfront. Each AI use case should have clearly assigned business and risk ownership, with accountability for data inputs, outputs, and ongoing performance.

Compliance teams should define clear checkpoints for risk assessment, documentation, and human oversight, and require continuous monitoring through logging, periodic reviews, and escalation mechanisms. By integrating compliance into existing workflows, organizations can recognize, mitigate, and monitor AI-related risks in real time while enabling responsible and efficient adoption.

GenAI has moved beyond experimentation and now requires formal compliance oversight. Regulators, auditors, customers, and boards increasingly expect organizations to demonstrate control, transparency, and ethical discipline consistent with other critical processes. By adopting a structured approach grounded in risk-based classification, comprehensive logging, and clear accountability, compliance leaders can support AI adoption while remaining prepared for regulatory scrutiny.