AI Governance for IT Operations and Agentic AI: Policies, Risks, and Controls

Establish reasons to use AI in your organization

Provide clear reasons and goals to implement AI within your organization. These reasons help you remain within governance boundaries and provide a baseline to measure compliance, performance, and risk. 

Set clear limits on autonomous actions

Define what AI agents are allowed to do and where human approval is required. For example, automated remediation can occur for routine issues, but high-impact system changes or complex issues require manual review. 

Assign ownership for accountability

Designate a person or team responsible for each AI-driven action. This way, when errors, bias, or policy violations occur, accountability is clear and corrective measures can be applied quickly. 

Embed oversight to catch issues

Use real-time monitoring and alerts to detect policy deviations or anomalous behavior. For example, flag unusual automated changes to critical systems so IT teams can intervene before problems escalate. 

Automate compliance checks

Integrate automated checks to confirm that AI actions follow internal standards and regulations. This can help with audit gaps and ensures every remediation or workflow change stays within defined compliance boundaries. 

Make AI transparent and understandable

Document all AI decisions so IT teams can understand why actions were taken. For example, show the inputs and logic behind a remediation recommendation to validate correctness and maintain trust.

Assess risk as AI evolves

Look for risks as new AI models, agents, and automation capabilities are introduced. Regular assessments can inform new updates, boundaries, and oversight to prevent errors or compliance gaps. 

• Implement and monitor AI workflows.
• Define guardrails for AI agents.
• Review actions in real time.
• Escalate anomalies before they affect systems.

• Protect IT systems from risks introduced by AI. 
• Enforce access controls. 
• Assess vulnerabilities in autonomous workflows. 
• Ensure AI actions don’t compromise sensitive data.

• Evaluate potential impacts of AI actions.
• Quantify exposure.
• Design mitigation strategies. 
• Keep AI deployments within operational and compliance limits.

• Ensure AI operations meet regulatory requirements and internal policies. 
• Maintain audit trails.
• Review governance processes.
• Offer guidance on ethical and lawful AI use.

• Define objectives and success metrics.
• Prioritize AI initiatives. 
• Ensure AI governance aligns with goals.
• Hold teams accountable for outcomes.