There is little ambiguity left in how Indian enterprises view the future of cybersecurity operations. Artificial intelligence is no longer a differentiator; it is fast becoming foundational to the modern Security Operations Center.
A recent global study conducted by Kaspersky, which included respondents from India, underscores just how decisive this shift has become. Every organisation surveyed in India indicated plans to integrate AI into its SOC. The signal is clear: security leaders see AI as central to improving detection speed, investigation depth, and operational efficiency.
Yet beneath this near-universal consensus lies a more complex reality. The enthusiasm for AI is real, but so are the constraints that prevent it from translating into measurable outcomes.
Indian enterprises are approaching AI in the SOC with a distinctly pragmatic lens. The focus is not on experimental deployments or long-term moonshots, but on immediate operational gains. Security teams expect AI to strengthen threat detection through automated analysis of large volumes of data, identify anomalies that would otherwise go unnoticed, and correlate fragmented alerts into coherent attack patterns. There is also a strong emphasis on continuously improving detection accuracy through machine learning models that adapt over time.
This emphasis reflects the day-to-day pressures inside most SOCs. Alert volumes are rising, attack surfaces are expanding, and skilled analysts remain in short supply. In that context, AI is being positioned less as a transformative leap and more as a necessary response to operational overload. The goal is straightforward: reduce noise, accelerate response, and allow human analysts to focus on higher-value decisions.
However, this is precisely where the paradox begins to surface.
For all the clarity around use cases, execution remains uneven. The most significant constraint is not a lack of tools, but a lack of readiness. Nearly half of the organisations surveyed point to insufficient high-quality training data as a primary barrier. AI systems are only as effective as the data they are trained on, and in many enterprises, data remains fragmented, inconsistent, or poorly contextualized.
The talent gap further complicates the picture. Building, tuning, and operationalising AI models within a SOC requires a hybrid skill set that blends cybersecurity expertise with data science capabilities—something that remains in short supply. At the same time, the cost of deploying and maintaining AI-driven systems continues to be a concern, particularly when return on investment is not immediately visible.
There is also a structural issue at play. Many organisations are attempting to layer AI onto existing security architectures that were never designed for it. Integration challenges, tool fragmentation, and limited interoperability mean that AI initiatives often operate in silos, diluting their potential impact. Compounding this is the emergence of new threat vectors linked to AI itself, forcing security teams to defend against risks introduced by the very technology they are trying to adopt.
What emerges is a widening gap between intent and execution—a gap that defines the AI–SOC paradox. Enterprises recognize that AI is essential, yet struggle to embed it meaningfully into their security operations.
Interestingly, the Indian market appears more grounded than some of its global counterparts. While large enterprises globally are pursuing expansive AI-led transformations across multiple SOC functions, Indian organisations are prioritizing targeted deployments that deliver immediate value. This measured approach may ultimately prove advantageous. It reduces the risk of overinvestment in immature capabilities and keeps the focus on tangible outcomes.
But pragmatism alone will not be enough.
For AI to move beyond promise and deliver sustained impact, organisations will need to address foundational gaps. This means investing in data quality and governance, rethinking SOC workflows to incorporate AI-driven decisioning, and building teams that can operate at the intersection of security and machine learning. Just as importantly, it requires a shift in mindset—from viewing AI as an add-on capability to treating it as an integral part of the security architecture.
The trajectory is unmistakable. AI will define the next phase of cybersecurity operations.
The real question is not whether organisations will adopt it, but how effectively they can operationalize it.
Right now, that remains the hardest part.