What is AI Goverance?

How to Manage AI Agents

We have all seen the news regarding Artificial Intelligence (AI) agents. AI Agents delete a company’s database in nine seconds. AI Agents in “sandbox” testing jumps out to real world systems. At an enterprise level, AI adoption has become nearly universal with 88% of organizations already use AI, yet only 8% globally have established a comprehensive AI Governance framework. This fact highlights the gap between usage and structure oversight with AI Governance policies. In this blog post we are going to discuss the basic AI Governance and what is needed for an organization.

AI Governance refers to the processes, standards and guardrails that help ensure that AI systems are safe and ethical. AI Governance provides a structured framework to guide how artificial intelligence (AI) is developed, deployed, and managed. These frameworks additionally help organizations maintain regulatory compliance and secure sensitive data with respect to AI-powered technologies. There are three main components to AI Governance. These components are the following:

  1. Control of AI Agents
  2. Organizational Process and Controls
  3. Legal and Regulatory Rules

We will briefly go into these three areas of focus for AI Governance and expand on each area in later blog posts and how CipherGov.AI platforms will be able to help your organization with AI Governance.

Control of AI Agents

The first one must do is define control of the AI agents. What does control AI Agents mean? In short, AI Agents Controls must define the following:

  • Business and Use Case for the AI Agent
  • What can the AI agent do and not do?
  • What areas can the AI agent access?
  • Risk classification of the agent

Once these controls are defined, it helps set the guidelines for Control and Attestation of the AI agent. This information helps create a digital audit trail, or birth certificate for the agent. It will also help define when the AI Agent should kill within the system.

Organizational Process and Controls

AI Governance defines the rules and policies that define how AI systems are built and used responsibility. The policies should consider fairness, transparency, accountability and compliance in these processes and controls. This will help reduce risk and protect people and their information. These policies translate into high-level actionable rules. These rules define how data is collected, stored, and used by the systems. The models developed by them should follow these policies in building, testing, and validation. It will define the deployment and potential rollback of these models including 3rd party AI vendors.

Legal and Regulatory Rules

An organization MUST maintain a strong audit trail through logs of activity, decision records, and compliance reports including external audit documentation. Organizations can use several legal and regulatory frameworks and guidelines to develop their governance practices. Some of the most widely used frameworks include the NIST AI Risk Management Framework, the OECD Principles on Artificial Intelligence and the European Commission’s Ethics Guidelines for Trustworthy AI (Regulation 50). These frameworks provide guidance for a range of factors, including transparency, accountability, fairness, privacy, security and safety.

In later blog post, we will highlight these frameworks in detail and how CipherGov.AI can help you implement these regulatory frameworks.

In closing, we have just given you a brief overview of the AI Governance, stay tuned as we dive deeper into AI Governance and AI Regulations. Hit the subscribe button to see further blog post on AI Governance and CipherGov.AI