Managed Services Providers (MSPs) increasingly rely on a variety of software applications embedded with generative AI, often powered by Large Language Models (LLMs). These technologies, like OpenAI and ChatGPT, are becoming integral to day-to-day operations, offering capabilities such as content creation, image generation, idea generation, and more. While the benefits of generative AI are vast, its rapid adoption often leaves MSPs unclear about what they’re using and how it might expose them to risks. This is where AI governance becomes crucial.
What is AI Governance?
AI governance refers to the policies and practices that ensure the responsible and ethical development and use of AI technologies, particularly LLMs. Its primary goal is to enable the safe use of AI while mitigating risks, protecting individual rights, and ensuring accountability. As AI systems become more embedded in business operations and personal lives, effective AI governance is essential.
Key components of AI governance include:
- Ethical Principles: Ensuring AI usage aligns with moral and ethical standards.
- Regulations and Compliance: Adhering to legal standards and industry regulations.
- Data Governance: Managing the quality, security, and privacy of data.
- Accountability: Establishing clear responsibility for AI decisions and actions.
- Transparency: Making AI decision-making processes understandable and auditable.
- Bias Mitigation: Reducing and managing biases in AI models.
- User Safety: Ensuring AI use does not harm individuals or organizations.
- Human Oversight: Involving human judgment in AI decision-making.
- Education and Awareness: Providing ongoing training on AI risks and best practices.
- Continuous Monitoring: Regularly evaluating AI systems for performance and compliance.
Key Elements of AI Governance for MSPs
- Data Sources and Model Accuracy
MSPs must understand the data sources used to create their AI LLMs, focusing on the quality, relevance, and potential biases of the data. Regular monitoring is crucial to maintaining model accuracy and relevance. - Data Security and Responsible Usage
Prioritizing the security and responsible handling of client data in relation to generative AI is essential. MSPs should adhere to data privacy regulations and obtain client consent for data usage. Clear policies on data handling and disposal are necessary to minimize risks. - Copyright and Intellectual Property Considerations
MSPs need to be vigilant about copyright and intellectual property risks when using AI for tasks like summarization, translation, or content creation. Compliance with regulations like HIPAA or GDPR is crucial to avoid exposing sensitive data. - Liability and Insurance Coverage
MSPs should assess their liability and insurance coverage in light of AI usage. Updating insurance policies to cover AI-related risks and having a plan for legal disputes related to AI-generated content are critical steps. - Cost Transparency
MSPs must maintain transparency regarding the costs associated with AI usage, including licensing fees and infrastructure expenses.
Taking Action: Responsible AI Usage
For MSPs to safely navigate the complexities of AI LLM implementation, a strong focus on AI governance is essential. This involves managing data responsibly, ensuring security, protecting intellectual property, addressing liability, and maintaining cost transparency.
The steps below outline what MSPs should do to ensure responsible AI usage:
- Operational Confidence
- Define measurable performance metrics for AI across your organization.
- Review existing processes to monitor fairness and explainability.
- Risk Management and Compliance
- Conduct a gap analysis against current and potential AI regulations.
- Align AI usage with business objectives and regulatory requirements.
- Process and Skill Development
- Establish traceability and auditability of AI processes.
- Operationalize updated processes and checkpoints throughout the AI lifecycle.
- Develop the necessary roles, skills, and learning agendas to implement responsible AI.
- Automation and Monitoring
- Create automatic documentation of model lineage and metadata.
- Ensure AI models are fair, explainable, high-quality, and regularly reviewed.
- Strengthen regulatory compliance for data science teams without adding overhead.
- Establish repeatable workflows with built-in stakeholder approvals to reduce risk and scale AI usage.
Trustworthy AI with IBM watsonx
The CrushBank AI Knowledge Management system uses IBM’s watsonx, a platform that stands out for its transparency and control over the LLM environment. watsonx enables responsible AI usage by providing:
- watsonx.ai: A component for building, training, validating, and deploying foundation models, generative AI, and machine learning.
- watsonx.governance: Tools for directing, managing, and monitoring AI activities with a focus on responsibility, transparency, and explainability.
By leveraging watsonx, CrushBank ensures that MSPs can deploy AI solutions that are safe, ethical, and aligned with best practices in AI governance.