By Karan Bhagat, Field CTO at Myriad360

We will continue to see momentum in cybersecurity in 2025, especially with the growing adoption of large data lakes. As businesses pool and curate their data to leverage AI effectively, securing these data lakes will become even more critical. While AI will drive tremendous growth and innovation, security will play a pivotal role in protecting and governing these expansive datasets. Ensuring robust protection for these data repositories will be essential to prevent breaches and maintain trust in AI-driven insights.

There will be strong growth in the development of high-speed networking infrastructures for High-Performance Computing (HPC) and GPU clusters, particularly as AI 'factories' are developed in both private and public cloud environments. 

Scalable high-performance storage with High Performance Networking

To develop, train and infer on Large Language Models (LLMs), large datasets are essential. It's crucial to service vast amounts of data, and these data storage systems must be scalable and capable of delivering high performance. They also need to support a variety of access protocols and efficiently handle large GPU compute clusters with a verity of access methods.

We will see an uptick in new technology storage vendors who are developing advanced storage fabrics for both in private and public cloud to address:

• Low Latency: The model should get the data it needs as fast as possible to avoid bottlenecks. Any delay in data serving can slow down the entire training process, so low-latency data pipelines and caching systems are critical.
• Load Balancing: As requests from GPUs scale, data retrieval systems must balance the load effectively to avoid hotspots where certain nodes become overloaded.
• Asynchronous Data Streaming: While training, models often require a continuous stream of data to avoid waiting. 
  

With petabytes of data requiring processing in AI pipelines, the timely movement of these datasets is critical. With baseline speeds of 400G and 800G, InfiniBand and high-speed switch vendors are well-positioned to support the buildout of HPC and GPU factories, effectively handling both east-west and north-south traffic.

Security Automation

As cyber threats become more complex and frequent, organizations are facing an unprecedented volume and sophistication of attacks. Adversaries are increasingly using automated tools and AI to exploit vulnerabilities at machine speed, making it difficult for manual security operations to keep up. The sheer number of daily alerts, complex compliance demands, and the need for fast incident response require a more efficient approach.

Security automation is critical for organizations to detect, respond to, and mitigate threats effectively. It helps reduce the time it takes to detect (MTTD) and respond (MTTR) to incidents. By implementing automation, organizations can not only strengthen their security posture but also optimize resource use, ensure consistent security processes, and free up security teams to focus on strategic tasks instead of routine ones.

Data Security

Data has become one of an organization's most valuable assets, but it is also more vulnerable than ever to breaches, theft, and regulatory non-compliance, especially as data volumes continue to grow across cloud, on-premises, and hybrid environments.

Organizations now face significant challenges in protecting sensitive data throughout its entire lifecycle while ensuring it remains accessible for business operations, analytics, and innovation. With the average cost of data breaches reaching millions of dollars and the potential for lasting reputational damage, it is crucial for organizations to adopt comprehensive data security strategies that include discovery, classification, protection, and ongoing monitoring.

• Cybersecurity Momentum in 2025: The increasing reliance on data-driven technologies like AI will drive greater emphasis on cybersecurity to protect critical business assets.
• Data Lakes: As businesses build and expand their data lakes (vast storage repositories for raw, unstructured data), the complexity of managing and securing these assets will also grow.
• AI's Role: AI will help organizations extract value from these data lakes, but AI systems themselves are vulnerable to manipulation and exploitation, making cybersecurity an even more pressing concern.
• Governance: Data governance-ensuring that data is accessible, usable, and secure-will become a fundamental aspect of managing large-scale data lakes, with security policies that govern access, usage, and compliance becoming more complex.
  

Both security and infrastructure are crucial for building AI farms of the future. Establishing strong foundations with high-speed networking, high-performance, low-latency storage, and robust data security and governance is essential for success.

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ABOUT THE AUTHOR

With over 25 years of experience in data center technologies, cloud solutions, and backup & recovery strategies, Karan Bhagat serves as the Field CTO at Myriad360, a leading technology solutions integrator.  Karan has a proven track record of helping organizations transition to innovative technologies that drive cloud adoption, optimize data centers, and disaster recovery.  His expertise enables businesses to modernize their IT landscapes while maintaining scalability, security, and business continuity.  By collaborating with strategic technology vendors and enterprise clients, Karan delivers tailored solutions that transform Myriad360 customers' digital infrastructures.