AI Governance

AI governance is key to maintaining transparency, fostering trust, mitigating risk, and allowing your organization to grow in a responsible and sustainable direction.

Ongoing monitoring is crucial to assess and refine AI systems while maintaining compliance. EisnerAmper’s experienced professionals help clients strengthen AI governance by evaluating internal processes against industry standards and best practices to identify and mitigate risks. Then, we develop customized frameworks that guide clients through internal monitoring and testing to keep strategies up to date as the commercially available large language models or functionality inevitably change over time.

Different Types of AI—Different Governance Needs

AI governance should be risk-focused and supported by empowered stakeholders who understand their roles and responsibilities. Leaders tasked with establishing a comprehensive risk strategy must address current and future AI-related scenarios that could impact the organization. As different forms of AI emerge, knowing how to navigate and comply with various regulations and standards is essential.

Gen AI

Generative AI

Generative AI (GenAI), a popular form of artificial intelligence, leverages advanced technologies and existing data sets to produce requested outcomes. With the ability to understand patterns and past human interactions, GenAI can generate materials or data such as photographs, video, text, music, and even programmer codes. Although this transformational tool can be extremely useful in any industry, organizations must understand how to remain compliant in the digital age. GenAI users must emphasize transparency, risk management, and continuous monitoring to best adhere to governing compliance.

Large Language Model

A Large Language Model (LLM) is a subcategory of Generative AI that understands human language patterns and texts to generate copy. LLMs analyze written instructions to generate prompts in a more approachable way, avoiding the need for a programming language to prompt an output. Rooted in complex data structures, LLMs can generate natural language, producing content translations, summarizations, and conversations across different languages and mediums. When implementing this system, organizations will find the most success and compliance by optimizing their models, stimulating their engineering with precise instructions to enhance system accuracy and relatability, and consulting with non-technical stakeholders to adapt and integrate into regular practice.

large language model

machine learning

Machine Learning

Machine Learning AI (ML) is another subset of AI that enhances computer predictions and decisions by utilizing and continuously improving statistical models and algorithms. This form of AI relies on various learning algorithms and training through inputs to make predictions based on individual needs. Organizations that integrate ML can gain more precise forecasting insights and decision-making tools rooted in historical data. Machine learning AI relies on precise statistical models and evolutionary algorithms, so if organizations follow a standardized procedure, implement automation for continuous updates, and use a centralized management model tool, they will best leverage this AI system.

Key Components of an AI Governance Framework

Risk Management

Organizations can work to address issues like bias, fairness, transparency, and accountability.

Compliance & Regulation

Organizations may avoid legal pitfalls by using AI systems responsibly and ethically following applicable regulations.

Trust and Transparency

By following established frameworks for AI usage, organizations can provide important transparency to stakeholders, which can improve user confidence and acceptance.

Top AI Governance Frameworks

NIST AI RMF is a voluntary framework that provides guidelines for managing risks with AI systems in the U.S. As a flexible guide focusing on governance, organizations can tailor this approach to best suit their risk management practices.
ISO 42001 is the international standard management system approach for all industries. As a framework for establishing, maintaining, and managing AI risk, it enforces specific AI policy and compliance requirements to promote ethical and responsible AI use.
EU AI Act, one of the first legally enforceable regulations of its kind in the world, aims to regulate AI use and practices within the EU, regardless of where the originating organization is based. It focuses on protecting personal information and provides specific requirements and AI use cases for general use, internal governance, and risk-based assessments.

These governance frameworks focus on building and establishing large-scale AI models but can also be leveraged for smaller operations. The controls can be utilized to address risks associated with other AI use cases, such as generative AI.

Best Practices for Effective AI Governance

It can be challenging to stay abreast of ever-changing new technologies, the risks they introduce, and the latest regulations and frameworks established to safeguard organizations and individuals. The best practices for effective AI governance include:

Technical Oversight and Monitoring

Implementing AI frameworks can streamline day-to-day operations while providing visibility into complex processes and technologies.

Embedding Data Privacy and Security

Organizations can leverage AI frameworks to provide the foundation necessary to make AI systems compliant, safe, and effective.

Transparent AI Use

Frameworks emphasize the ethical use of AI, guiding organizations to consider the broader impact of their AI systems on society.

How Can We Help?

With dedicated and experienced professionals, our team at EisnerAmper is ready to guide you into the future of technology, instilling personalized solutions for your organizations to help maintain compliance and grow in the digital age with ease. Contact us below to get started.