Safe Secure LLMS in Critical Infrastructure

This use case examines how an energy company leveraged LLMs to enhance its operations while mitigating potential safety and security risks. The focus is on areas such as internal process efficiency,  customer service, predictive maintenance, regulatory compliance, and strategic decision-making. A key component of the risk management strategy involves using Trusted AI's AI TIPS which manages trust throughout the AI lifecycle by aligning with the TrustedAI Center of Excellence methodology.

Key focuses of the AI COE include:

• Risk Assessment and Management: Developing and applying advanced risk evaluation models to anticipate and mitigate potential AI-related vulnerabilities in LLMs such as confabulation, toxicity.
• Ethical AI Deployment: Establishing guidelines and standards to ensure AI solutions uphold ethical principles and societal norms.
• Strategic AI Integration: Assisting organization in aligning their AI strategies with broader business objectives while minimizing risks.
• Innovation and Compliance: Navigating the regulatory landscape to foster innovation within safe and approved parameters.
• Safety & Security: Creating threat models and security plan by use case.
• Additionally, the AI COE collaborates closely with sectors prone to AI disruption, offering tailored risk assessment tools and strategic insights that safeguard interests and promote informed AI utilization.

Dependence on Stakeholder involvement and Data Governance:

• Issue: The effectiveness of AI TIPS and LLMs is heavily reliant on the quality and quantity of data available. Poor data quality or insufficient data can lead to inaccurate predictions and model biases. Additionally if the business doesn't prioritize Data Governance there can be exposure of sensitive data that can quickly unravel.

Integrating Large Language Models in critical infrastructure should not be done without adequate Guardrails in place - using tools like AI TIPS from Trusted AI, underscores the importance of robust data governance, continuous model training, and human oversight to maintain system effectiveness and compliance. These implementations reveal the need for scalable, flexible AI solutions that accommodate growth and adapt to evolving regulatory landscapes. Effective blending of automation with human expertise and ensuring system transparency are crucial for building trust and managing stakeholder expectations. The ongoing commitment to resource allocation for maintenance and updates is vital for sustaining the long-term viability and security of AI initiatives in traditional industries.

Trusted AI TIPS is used in our methodology of creating an AI Center of Excellence for organizations.  It is very holistic and integrates AI Governance and Risk Management by context. It allows for integration of Governance platforms by industry or criticality so existing skills and technology is leveraged to create a clear AI adoption process and approach that relies on people, process and technology changes.