Key Takeaways The autonomous nature of agentic AI introduces new security and operational risks. Agents thrive on connectivity to enterprise systems, but making it happen securely requires strong identity, permission, and auditing controls. Agentic AI initiatives should prioritize ongoing monitoring and oversight rather than one-time governance policies. It’s easy to think of agentic AI as
Key Takeaways Traditional cybersecurity often fails to cover the gaps that exist when we deploy AI tools. While cybersecurity is never one size fits all, there are six actions CISOs can take to bolster AI security: take an AI asset inventory, layer prompt and input defense, classify data, use least-privilege and human review, use a
Key Takeaways AI security and governance are a must-have for any organization using AI tools. AI security includes protecting AI tools and outputs as well as safeguarding the data and systems AI interacts with. Six critical AI security areas leaders must address are shadow AI, prompt injection and adversarial manipulation, sensitive data leakage, agentic AI,
Key Takeaways Teams with supervisors who embrace AI use AI-based tools at much greater rates—regardless of how good the tools are, how effective the training was, or how great the incentives to utilize the new capabilities are. Incorporating employees into the design process around how AI will be integrated into daily operations has consistently been
Key Takeaways AI scalability is now constrained by energy availability, not only compute. AI costs are increasingly tied to electricity markets, grid stress, and regional demands. Vendors can win over customers by offering predictable, energy-efficient, and flexible AI. When a Virginia data center went offline for 90 minutes last summer amid a heatwave, it wasn’t
Key Takeaways AIOps—AI Intelligence of IT Operations—allows teams to stay a step ahead of problems by identifying them before they happen Teams can take advantage of AIOps to help them correlate and prioritize alerts, which streamlines operations and reduces noise Data is critical to AIOps implementations; inconsistent, disconnected, or out-of-date information can degrade system outputs
Key Takeaways AI solutions need to gain user trust to succeed; capabilities alone are not enough Manager adoption plays a key role in ensuring teams use AI tools regularly Including end users in how AI capabilities are designed and incorporated into workflows can help enhance outcomes Think about the last time a new system was
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
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