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The rapid adoption of large language models (LLMs) and other AI tools across enterprises is revolutionizing business operations and enhancing productivity. However, these tools also pose significant security and compliance challenges as they expand the organizational  attack surface. Historically, organizations have prioritized utility and speed to market over robust governance and control measures, and the evolution of AI governance is no exception. As enterprises integrate AI and ML capabilities into more and more tasks, it is critical that they establish a comprehensive AI governance framework that addresses these new risks to ensure internal and external compliance and security.

The Necessity of AI Governance

As AI/ML tools gain traction across functions including Operations, Sales, Legal, Human Resources, and others, the organization’s security leaders—which could include CISOs, CIOs, CTOs, CSOs, CDOs, Compliance Officers, and/or the General Counsel—must undertake to develop and implement an enterprise-wide AI governance framework. This process is pivotal in balancing the increasing demand for AI tools with the need to protect the expanding attack surface. This task involves assembling a cross-functional team with representatives from all major organizational functions to achieve consensus on governance principles and secure buy-in from stakeholders including the C-suite. Limiting the team to six to eight participants can streamline decision-making and implementation processes. The security leader most involved with the technology should lead; however, the role is not that of a subject matter expert, but that of a facilitator who demonstrates neutrality and avoids bias.

Current Landscape and Challenges

The surge in LLMs and other generative AI (GenAI) products provides enterprises myriad opportunities to enhance business processes, improve customer experiences, and boost professional productivity. These opportunities include custom-developed, in-house models reliant on proprietary data to satisfy industry- or company-specific use cases. The rapid adoption of these tools, as well as the large array of models and customization to the models, places a cognitive burden on users who might never have used such sophisticated digital tools until now. The security leaders must educate stakeholders—employees, contractors, customers, clients, and any others—about the risks,  responsibilities, results, and rewards of using these game-changing solutions. 

Framework for AI Governance

When establishing an AI governance framework, security leaders should focus on several key areas, including:

People

The security leaders must educate department, team, and practice leaders across functions about the importance of consensus-driven AI governance. 

Principles

Governance principles should align with organizational values and brand reputation, providing a clear business value foundation and a solid underpinning for AI-specific policies addressing issues such as acceptable use and consequences of misuse. Examples of principles include mandating human review of all LLM outputs and requiring identification of LLM use in internal communications. Early establishment of these principles allows for the amendment of existing company policies and the creation of new policies.

Regulation

AI governance must consider national and global regulations influenced by existing relevant privacy laws, such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Executives responsible for privacy and compliance must be integral to the governance framework, ensuring that AI-aligned or AI-dependent models, tools, and practices meet regulatory standards for consent, data anonymization, and data deletion.

Ethics

AI governance is labor-intensive, focusing on how people work rather than the tools they use. Ethical considerations must be integrated into AI development, with humans serving as arbiters of ethical judgment in technology design.

Controls

AI governance should incorporate three tiers of human input: Human-in-the-loop (HITL), human-on-the-loop (HOTL), and human-in-control. Doing so ensures human oversight and accountability across AI operations, mitigating risks associated with autonomous AI decision-making.

The Business Case for AI Governance

Implementing AI governance requires investment in technology and labor. However, the productivity gains and labor cost savings from using LLMs in core business workflows can be significant enough to justify these investments. Establishing an accountability model, in which identified employees are responsible for AI outputs, can minimize the need for additional support personnel. When such a framework is put in place and has executive, as well as employee, buy-in, the enterprise can efficiently, effectively, and safely manage extended risks and achieve measurable benefits aligned with company values and policies. 

Read more about developing a strong AI governance framework in our Strategic Blueprint for AI Adoption, available here

 

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