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To measure the effectiveness of data governance and risk management , organizations must define SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) controls. When designed correctly, data, IT, and system-related controls can be automated, bringing significant efficiency to the organization.
In today’s fast-moving regulatory landscape, manual controls and reactive processes simply can’t keep up. Risk managers are expected to anticipate challenges, adapt in real time, and prove compliance under increasing scrutiny. The future of operational risk is clear: proactive, automated, and AI-enabled.
At Auditrol, we have incorporated AI-powered tools like GitHub Copilot into our software development process. Over several product release cycles, we've observed valuable patterns in their usage. Below, I share lessons learned, along with specific examples, to help others.
Imagine spinning up your cloud infrastructure as easily as writing a few lines of code in your favorite programming language. Sounds pretty great, right? That's exactly what Pulumi , a modern Infrastructure as Code (IaC) tool, brings to the table. Unlike traditional tools that rely on specialized languages or bulky templates, Pulumi lets you define your cloud resources using languages you already know - think Python, TypeScript, or Go.
To measure the effectiveness of data governance and risk management , organizations must define SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) controls. When designed correctly, data, IT, and system-related controls can be automated, bringing significant efficiency to the organization.
For decades, the core principles of banking have remained largely unchanged, with institutions taking calculated risks to generate profits. However, the industry has experienced significant transformations in recent years. New regulations and amendments, the rise of neo-banking, technological advancements, heightened security concerns, and privacy issues have all reshaped the banking landscape.