NASSCOM president Rajesh Nambiar has compared the July CrowdStrike outage to a security team accidentally setting your house on fire. The incident triggered the infamous "blue screen of death" and caused millions of Windows systems to crash globally. This outage disrupted critical industries like airlines, hospitals, and banks. During a panel discussion at an event organised by the India Global Forum in the UAE, Nambiar highlighted the need for a balance between speed and quality in IT deployments to prevent such widespread disruptions in the coming days.
What Nambiar said about AI’s role in reducing these issues
Nambiar also emphasised the importance of artificial intelligence in making software development more efficient. He highlighted AI's potential to minimise errors in Continuous Integration and Continuous Deployment (CI/CD) cycles, where product vendors frequently release updates.
"However, achieving a balance between speed and quality remains essential. Security and stability are equally important, and systems must be resilient enough to recover quickly when things go wrong. This concept of cyber resilience—building enough redundancies into systems—is vital,” Nambiar noted.
Nambiar even highlighted the need for IT systems to be resilient in addition to being efficient. He argued against the trend of minimising redundancies to maximise performance, pointing out that the COVID-19 pandemic exposed the vulnerabilities of such an approach. The pandemic emphasised the importance of building robust systems that can withstand disruptions, even if it means sacrificing some efficiency.
“But redundancy, though often undervalued, is crucial for resilience. Don’t put all your eggs in one basket. Systems must incorporate redundancies to ensure stability and rapid recovery during disruptions. Resilience, stability, and efficiency must all be equally prioritised to create robust systems,” he added.
Nambiar also emphasised the dual importance of cybersecurity in the age of AI. He distinguished between two critical areas: securing AI systems themselves and leveraging AI to enhance cybersecurity.
AI systems, especially those using large language models, handle sensitive data and are vulnerable to breaches. As AI adoption grows, robust cybersecurity measures become even more crucial to protect against data leaks and other threats.
"When training LLMs, there’s significant potential for biases to creep in, and during inference, these systems may exhibit hallucinations," he explained.
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