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Artificial intelligence has rapidly moved beyond research labs and into everyday business operations, and leadership in every organization is being called upon to build smarter, safer systems. From automated workflows to data-driven decision-making, AI systems now play a vital role in how organizations innovate and compete. Yet with this shift comes an evolving set of cybersecurity challenges. And as technology grows more complex, so does the responsibility to manage it securely and ethically. Organizations are discovering that traditional cybersecurity frameworks, while still essential, fail to fully address the risks introduced by AI and cloud systems. Algorithms can make it difficult to understand the decision-making process, training data can expose sensitive information, and third-party vendors can introduce unseen vulnerabilities. Navigating these risks requires both technical insight and leadership skills capable of…

AI adoption is accelerating across government agencies and businesses in virtually every industry. Leaders, understandably, are looking for ways to boost efficiency, strengthen security, and improve decision-making. But while AI holds great promise, most organizations are misusing the technology. In fact, as much as 95% of AI efforts today are wasted investments, and in many cases, they actually increase risk. Instead of rushing to deploy AI throughout your company, the smarter move is to take smaller, more deliberate steps. Start with one use case. Build guardrails around it. Prove it works. Then expand. This is the foundation of responsible AI governance. What AI Governance Really Means AI governance is about more than simply managing technology. An interdisciplinary approach that blends computer science, ethics, law, and cybersecurity is required. AI decisions…