The 2026-Ready AI Agent Governance Playbook: 5 Steps to Professional Control

In the coming years, AI governance will be the new frontier of competition. This five-step playbook from SUPERWISE provides a clear roadmap for organizations aiming to achieve professional control over their AI agents.

Identity Management: Conduct an agent discovery audit and assign unique identities to each AI agent. This is crucial for preventing “AI sprawl.”

Real-Time Monitoring: Proactively monitor and set up alerts for critical events and policy breaches to ensure AI security.

Policy-Based Guardrails: Implement automated guardrails to enforce policies, which helps scale governance effectively.

Secure Integrations: Address the security risks of AI agents accessing sensitive data by assessing API security and defining policies based on the principle of least privilege.

Continuous Compliance: Establish that AI compliance is an ongoing process by mapping regulatory requirements and using a continuous audit loop.

Following this playbook can lead to significant benefits, including a 75% reduction in security incidents and a 90% improvement in regulatory compliance scores.

How Platform-First Thinking Changes Everything

Despite impressive accuracy metrics, most predictive analytics projects in manufacturing and construction fail to deliver lasting impact. The key to success isn’t better models—it’s a platform-first approach that connects insights to decision-making systems, automates learning, and ensures scalability. Learn how industry leaders are transforming their predictive analytics into operational advantages by focusing on systems, not just models.

Why AI in Healthcare Fails—Here’s How to Fix It.

In industrial environments, delayed, incomplete, or inaccurate data can lead to costly inefficiencies, safety risks, and operational blind spots. Whether in construction, manufacturing, or logistics, frontline teams rely on real-time, high-quality data to make informed decisions that drive productivity and safety. Yet, traditional data collection methods—paper-based forms, siloed spreadsheets, and outdated digital tools—fail to meet the demands of modern industrial operations.

Unlocking Enterprise Resilience: How AI Drives Proactive Operations and Unrivaled Quality Across Industries 

In industrial environments, delayed, incomplete, or inaccurate data can lead to costly inefficiencies, safety risks, and operational blind spots. Whether in construction, manufacturing, or logistics, frontline teams rely on real-time, high-quality data to make informed decisions that drive productivity and safety. Yet, traditional data collection methods—paper-based forms, siloed spreadsheets, and outdated digital tools—fail to meet the demands of modern industrial operations.