Generative AI Risk Management and Regulatory Compliance in Energy & Commodities: Best Practices for Governance, Security, and Ethical AI Adoption

The energy and commodities sector is undergoing a profound transformation, with generative AI emerging as a catalyst for operational efficiency, risk management, and innovation. However, the sector’s unique regulatory landscape, operational complexity, and high-stakes environments demand a tailored approach to AI risk management—one that balances the promise of AI with robust governance, compliance, and ethical frameworks.

The Generative AI Opportunity—and Its Risks

Generative AI’s ability to synthesize vast datasets, automate complex tasks, and generate contextualized content is already delivering material impact across energy and commodities. From optimizing trading strategies and asset maintenance to codifying institutional knowledge and enhancing customer engagement, the technology is unlocking new value pools. Yet, these opportunities come with sector-specific risks:

Data privacy and proprietary information leakage
Regulatory compliance in safety-critical environments
Operational safety and reliability
Workforce disruption and the need for upskilling
Ethical concerns, including bias and misinformation

To realize the benefits of generative AI while mitigating these risks, organizations must adopt a comprehensive risk management strategy.

Governance: Building the Right Foundations

Effective governance is the cornerstone of safe and successful generative AI adoption. For energy and commodities companies, this means:

Codifying Institutional Knowledge: Generative AI can capture and institutionalize decades of operational expertise, reducing the risk of knowledge loss as experienced workers retire. By structuring and digitizing best practices, maintenance logs, and safety protocols, organizations accelerate onboarding and ensure continuity.
Establishing Data Governance and Security: Given the sensitivity of operational and trading data, robust data governance is essential. This includes anonymizing data, setting clear access controls, and ensuring that proprietary information does not leave the organization’s secure environment. Standalone, sandboxed AI tools with strict guardrails can enable innovation without risking data leakage.
Implementing Responsible AI Frameworks: With evolving global regulations—such as the EU AI Act and sector-specific mandates—energy and commodities firms must proactively define ethical guidelines, model documentation standards, and human-in-the-loop oversight. This ensures transparency, traceability, and accountability in AI-driven decisions, especially in safety-critical operations.
Cross-Functional Collaboration: Governance is not just an IT or compliance function. It requires collaboration across business units, risk management, legal, and technology teams to set policies, monitor usage, and respond to emerging risks.

Compliance: Navigating a Complex Regulatory Landscape

The energy and commodities sector is subject to some of the world’s most stringent regulations, from environmental reporting to market conduct and operational safety. Generative AI introduces new compliance challenges:

Data Privacy and Confidentiality: AI models must be trained and operated in ways that protect sensitive data, comply with privacy laws, and avoid inadvertent exposure of proprietary information.
Auditability and Explainability: Regulatory bodies increasingly require organizations to demonstrate how AI-driven decisions are made. Maintaining detailed model documentation, version control, and audit trails is essential for both internal governance and external compliance.
Sector-Specific Regulations: Whether it’s pipeline safety, emissions monitoring, or commodity trading, generative AI solutions must be tailored to meet the specific regulatory requirements of each domain. Automated compliance reporting, scenario simulation, and real-time monitoring can help organizations stay ahead of regulatory changes and reduce the burden of manual compliance tasks.
Proactive Risk Assessment: By generating synthetic scenarios and stress-testing operational and trading strategies, generative AI can help organizations anticipate regulatory risks and design more resilient controls.

Security and Data Privacy Protocols

Protecting sensitive operational and trading data is paramount. Best practices include:

Sandboxed Environments: Deploy generative AI tools in secure, isolated environments to prevent data leakage.
Anonymization and Pseudonymization: Use anonymized or synthetic data where possible, and implement data masking to protect personally identifiable or proprietary information.
Zero-Trust Architectures: Enforce strict access controls and continuous monitoring to ensure only authorized users can access sensitive data and AI outputs.

Auditability and Explainability Requirements

Transparency is critical for both regulatory compliance and building trust with stakeholders. Organizations should:

Maintain Model Documentation: Keep detailed records of model training data, parameters, and decision logic.
Enable Version Control and Audit Trails: Track changes to AI models and document the rationale behind key decisions.
Implement Human-in-the-Loop Oversight: Ensure that critical decisions—especially those impacting safety or compliance—are subject to human review and intervention.

Ethical AI Adoption: Frameworks and Culture

Ethical concerns, such as bias, misinformation, and unintended consequences, must be addressed proactively. Leading organizations:

Define Ethical Guidelines: Establish clear principles for responsible AI use, aligned with organizational values and regulatory expectations.
Monitor for Bias and Hallucinations: Regularly test AI outputs for accuracy, fairness, and potential harm, and retrain models as needed.
Foster a Culture of Experimentation and Learning: Encourage teams to pilot new AI solutions, learn from setbacks, and scale successful initiatives. Change management and continuous learning are essential to successful workforce transformation.

Actionable Steps for Robust Governance and Compliance

Start with a Shared Knowledge Base: Build transparency and trust by educating all stakeholders on the capabilities and limitations of generative AI. Use this foundation to identify high-value, low-risk use cases for early wins.
Establish Robust Governance and Guardrails: Define clear policies for data use, model oversight, and ethical AI deployment. Collaborate across business units to prevent shadow IT and duplication of effort.
Prioritize Data Security and Privacy: Implement sandboxed environments, anonymization protocols, and zero-trust architectures to protect sensitive information.
Align AI Initiatives with Regulatory Requirements: Stay ahead of evolving regulations by embedding compliance into the AI lifecycle—from model development to deployment and monitoring.
Invest in Workforce Upskilling: Launch targeted training programs to equip employees with the skills needed to collaborate with AI, manage risk, and drive innovation.
Foster a Culture of Experimentation: Encourage teams to pilot new AI solutions, learn from setbacks, and scale successful initiatives across the organization.

Unlocking Competitive Advantage with Publicis Sapient

Publicis Sapient brings deep expertise in digital business transformation and generative AI, helping energy and commodities organizations navigate the complexities of AI risk management. Our approach combines proven frameworks for AI governance, compliance, and ethical deployment with sector-specific guidance and workforce transformation strategies. By partnering with Publicis Sapient, energy and commodities leaders can confidently harness generative AI to drive operational efficiency, ensure compliance, and build a future-ready workforce—turning risk into a source of sustainable competitive advantage.

Ready to transform your organization with generative AI? Connect with our experts to start your journey.