Your marketing group just signed up for an AI tool that scrapes customer data. Your sales team is feeding proprietary deal terms into ChatGPT. Your developers are using AI coding assistants that may be training on your intellectual property. And your procurement team? They just approved three new SaaS contracts with “AI-powered features” buried in
AI doesn’t lie, it hallucinates. And attackers can exploit this weakness. Organizations need to understand what hallucination is, what it means for cybersecurity, and how to approach it within their Security Operations Center (SOC). This understanding becomes critical as more organizations deploy AI in their security operations. How AI Accuracy Training Creates False Confidence AI
Cloud evolved from packaging and elasticity toward stateless, autoscaling, and managed building blocks that reduced operational effort. AI is now shaping the next phase of that evolution, turning cloud into an AI runtime platform where hardware, scheduling, data movement, and governance are designed around latency and token-based economics—not just workload size and power. Inference, running
Artificial intelligence (AI) dominated 2025—from boardroom strategies to campaign brainstorms—and it’s reshaping how every business operates. Across industries, we’ve seen remarkable innovation, but also some recurring assumptions about what AI can (and can’t) do. Like every new wave of technology, AI adoption brings learning curves and course corrections. Our new eBook, Top Six B2B AI
The global enterprise software market is expected to reach $280 billion this year, on its way to more than $760 billion by 2034. The real question isn’t how much you’re spending. It’s whether you’re investing in code that ages or code that learns.1 Artificial intelligence (AI) isn’t just about improving enterprise software—it’s about transforming the
The next evolution of GenAI will be agentic AI: large language models (LLMs) that not only provide answers to user queries but also take specific actions that can include writing new data and creating new files. Businesses are abuzz at agentic AI’s potential to extend code generation activities to enable LLMs to connect new data
Artificial intelligence (AI) is accelerating down the enterprise highway. But according to new research, governance is idling in the slow lane, and the gap is already raising breach costs, fueling shadow AI adoption—when employees use generative AI tools outside official oversight—forcing regulators to wave the caution flag. Global average data breach costs fell in 2025
The artificial intelligence (AI) revolution began with general-purpose models—the Swiss Army knife of intelligence. Handy, flexible, and able to do a bit of everything, they’ve answered customer questions, written and debugged code, and even nailed a wedding toast or two. Impressive? Absolutely. But when precision and compliance are on the line, these jack-of-all-trades models aren’t
As businesses take to large language models (LLMs) to power productivity-saving AI chatbots and content generation, the biggest hurdle is overcoming costs. According to a recent industry survey, enterprise cloud costs rose 30% in 2024, and 72% of IT and financial leaders said cloud costs were becoming unmanageable, all primarily due to AI.1 Pennies per
Data fuels our global digital economy. As AI and analytics applications proliferate, the value of critical data continues to rise, along with the urgency to protect it. Governments around the world are responding. Most nations now enforce data sovereignty laws that regulate how digital information is collected, processed, and stored within their borders. The result
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