Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI systems are quickly becoming critical elements of business technology. Imagine building an AI agent trained using your internal documents and guides to quickly improve customer experience, process loan applications, or provide tier 1 support to your customers. The potential is tremendous—but what happens if your models are trained using your own intellectual property like software code, customer data, or other sensitive information?

Most organizations today utilize cloud computing to streamline their workflows and deliver more cost-effective and scalable infrastructure. While some still host everything on-premises, and some are evolving to all-cloud, the middle ground of a hybrid cloud environment is becoming increasingly popular.

In 2025, hybrid cloud environments have emerged as a cornerstone for businesses striving to modernize their IT infrastructure. From leveraging AI-driven tools to enhancing performance, sustainability, and security, hybrid cloud is paving the way for a new era of efficiency and growth.

As organizations race to adopt the latest advancements in artificial intelligence, DeepSeek serves as a cautionary tale about the potential dangers of rushing into the hype cycle without adequate consideration of security and ethical implications. DeepSeek, a Chinese AI startup, has been identified as having several significant security risks and vulnerabilities that could pose threats to both the company and its users.

A cloud transformation is no time to discover shortcomings in your load balancing solution—or the vendor who provides it. But according to a recent series of industry surveys, that’s exactly the situation currently facing many enterprises. With hybrid environments now the norm, and cloud-first strategies on the agenda, both load balancer/ADC functionality and vendor relationships are leaving IT leaders unsatisfied. And that’s enough to make them consider other options.

In today’s digital age, as organizations modernize their applications and infrastructure by embracing hybrid cloud environments, API-centric architectures, and AI-driven technologies to enhance customer experiences and productivity, they also face an unprecedented increase in cyber threats aimed at disrupting their operations.

Building AI and LLM inference and integrating it in your environment are major initiatives, and for many organizations, the most significant undertaking since cloud migration. As such, it’s crucial to begin the journey with a full understanding of the decisions to be made, the challenges to overcome, and the pitfalls to be avoided along the way.

Application delivery has never been more critical—or more challenging. Customers, partners, and employees expect a great experience every time they access your apps, without having to worry about the security of their data. But meeting that expectation gets harder every day, as complex hybrid and multi-cloud environments, intensifying threats, rising scale, and constantly changing business requirements put new demands on your infrastructure.

For most enterprises, the question isn’t whether to use AI—it’s where and how. For customers, AI enables more personalized experiences and faster response times. Employees can achieve higher productivity with more intuitive ways of working. Businesses can open new revenue streams and build competitive differentiation while strengthening security. The potential benefits are many—but realizing them isn’t a trivial matter.

The EU’s Network and Information Systems Directive 2 (NIS2) for cybersecurity resilience entered full enforcement in October 2024, and compliance with its requirements presents major challenges for many companies, particularly those in the financial services sector. And while most IT leaders are confident of achieving NIS2 compliance, they also acknowledge that this cybersecurity directive has exacerbated existing challenges such as resource constraints and skills gaps.